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In the article, Data breach trends in the United States, Hotfreter and Harrington discuss data breach trends and organize them in three categories: external, internal, and non-traceable.  In the article, they took a random sample of 300 data breach cases and classified them in the categories.  Their sample came from the Privacy Rights Clearinghouse records which consisted of over 2,280 data breaches and over 512 million associated tainted records (242).  The study identified trends overtime.  Of the 300 random breaches the following industries were represented: finance, telecommunications, education, government, manufacturing, hospitality, professional service, healthcare, and non-profit. There are loop-holes in the law that do not require all data breaches to be reported to victims.

As discussed, in the text, there are external and internal data breaches.  In addition, there are unintended and intentional data breaches.  Initially, data breaching was seen as a problem for the information technology industry.  However, now with the advances in technology all industries can be affected by data breaches.  I found this article interesting because, a number of the data breaches discussed made me think about my own vulnerabilities.  Specifically, the Zappos data breach in 2012, I am a customer and I was not aware!

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5 Huge Cybersecurity Breaches at Companies You Know-Adrianna Razo

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5 Huge Cybersecurity Breaches at Companies You Know-Adrianna Razo

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The article starts out by explaining the threat of cybersecurity breaches and how they have been growing more recently. Then they go into detail to look at 5 of the biggest security breaches. First is Walmart where hackers broke into the computer system of the companies development team to steal information from cash registers. Second, was home depot who fell victim to cybercriminals who stole over 60 million of their customers' credit card numbers. Third, a massive security breach at target in which hackers accessed personal information of about 110 million customers. Fourth, Apple's ICloud accounts were broken into, specifically celebrities whose personal information and even photos were leaked. Lastly, the retailer Neiman Marcus suffered a security attack last February where hackers were undetected from some time and were stealing customer information from the cash registers.

This article helps me to better understand the importance of security in any business because cyber attacks like this can cause companies to lose customers and it could all be prevented with better use of security.

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What Do Hackers Do with Corporate Security Breaches?

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What Do Hackers Do with Corporate Security Breaches?

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Garrett Anthony
https://www.google.com/search?q=Corporate+Security+Breaches&rlz=1C1CHWA_enUS624US624&oq=Corporate+Security+Breaches&aqs=chrome..69i57j0l5.2080j0j9&sourceid=chrome&ie=UTF-8
The Article that I found was short but informative. It went over details on how and what hackers do with the information they collect.  It also went into detail on where the hackers country of origin. This was very interesting to me and found it informative. I also thought the length was appropriate because I was able to read it in an amount of time that I enjoyed spending on the article.

Yes this article helps me understand more about the topic and the people who perform hackings.  The information also was impactful because it showed how companies can prevent hacking. Also it was shown how companies find out over all it was an informative article.

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Erica Noye

From explaining how Target can predict when a woman is going to get pregnant from her recent purchases to Netflix hosting competitions with it's customers, I found this entire article to be entertaining and exciting. It discussed topics such as entertainment, sports, education, disease outbreak, politics, and even video games. The article explained how using predictive analytics continues to grow and that people are finding ways to use data in order to solve problems.

This article relates to what we have been learning by taking topics that people enjoy spending their time doing or learning about in order to demonstrate how business analytics are used and how beneficial they can be. It showed me how to apply this subject matter to actual life situations. It just goes to show that business will continue to use data in order to make predictions to obtain the best outcome in a situation.

http://www.aunalytics.com/our-favorite-analytics-stories/

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Using Marketing Analytics to drive superior growth- Claire Riddle 

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Using Marketing Analytics to drive superior growth- Claire Riddle 

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The article discusses different ways to use data analytics to increase a company’s chances of making better decisions for their long term success.  It also briefly describes the importance of using the right data the right way, or the smarter way.  For example, one main point of the article was formed around the idea of making technologies work together to receive the greatest return on investment.  Another key point was to put data analytics at the main focus and that many people from various areas of study need to come together to develop the needed information.

This is extremely relevant to what we are learning in class because these scientists and data analysts work with big data and create hypothesis for future decisions of businesses. It shows how important it is to not only have the correct data, but use it in a way that will best benefit your company.  For example, a company spent $1 million on online advertising and received 1300 new customers. When they put $1 million into TV/radio, they had 4300. In all, this data helps show companies where they need to use their spending to improve areas, rather than spend it where they already have high ratings but for short term success.

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Companies have so many analytical options at their disposal that they often become paralyzed, defaulting to just one tool. But an integrated marketing-analytics approach is the key to driving growth.

There's no question that the development of better analytical tools and approaches in recent years has given business leaders significant new decision-making firepower. Yet while advanced analytics provide the ability to increase growth and marketing return on investment (MROI), organizations seem almost paralyzed by the choices on offer. As a result, business leaders tend to rely on just one planning and performance-management approach. They quickly find that even the most advanced single methodology has limits.

The diverse activities and audiences that marketing dollars typically support and the variety of investment time horizons call for a more sophisticated approach. In our experience, the best way for business leaders to improve marketing effectiveness is to integrate MROI options in a way that takes advantage of the best assets of each. The benefits can be enormous: our review of more than 400 diverse client engagements from the past eight years, across industries and regions, found that an integrated analytics approach can free up some 15 to 20 percent of marketing spending. Worldwide, that equates to as much as $200 billion that can be reinvested by companies or drop straight to the bottom line.

Here's one example. A property-and-casualty insurance company in the United States increased marketing productivity by more than 15 percent each year from 2009 to 2012. The company was able to keep marketing spending flat over this period, even as related spending across the industry grew by 62 percent. As the chief marketing officer put it, "Marketing analytics have allowed us to make every decision we made before, better."

Anchoring marketing analytics to strategy

A company's overarching strategy should ground its choice of analytical options. Without a strategy anchor, we find companies often allocate marketing dollars based largely on the previous year's budget or on what business line or product fared well in recent quarters. Those approaches can devolve into "beauty contests" that reward the coolest proposal or the department that shouts the loudest rather than the area that most needs to grow or defend its current position.

A more useful approach measures proposals based on their strategic return, economic value, and payback window. Evaluating options using such scores provides a consistent lens for comparison, and these measurements can be combined with preconditions such as baseline spending, thresholds for certain media, and prior commitments.

The other prerequisite in shaping an effective MROI portfolio is understanding your target consumers' buying behavior. That behavior has changed so radically in the past five years that old ways of thinking about the consumer -such as the marketing "funnel"-generally don't apply. Where the funnel approach prioritized generating as much brand awareness as possible, the consumer decision journey recognizes that the buying process is more dynamic and that consumer behavior is subject to many different moments of influence. (A sidebar, "Five questions for maximizing MROI," highlights additional considerations.)

Five questions for maximizing MROI

One home-appliance company, for example, typically spent a large portion of its marketing budget on print, television, and display advertising to get into the consideration set of its target consumers. Yet analysis of the consumer decision journey showed that most people looking for home appliances browsed retailers' websites-and fewer than 9 percent visited the manufacturer's own site. When the company shifted spending away from general advertising to distributor website content, it gained 21 percent in e-commerce sales.

Making better decisions

While new sources of data have improved the science of marketing analytics, "art" retains an important role; business judgment is needed to challenge or validate approaches, but creativity is necessary to develop new ways of using data or to identify new opportunities for unlocking data. These "soft" skills are particularly useful because data availability and quality can run the gamut. For instance, while online data allow "audience reached" to be measured in great detail, other consumer data are often highly aggregated and difficult to access. But such challenges shouldn't impede the use of data for better decision making, provided teams follow three simple steps.

1. Identify the best analytical approaches

To establish the right marketing mix, organizations need to evaluate the pros and cons of each of the many available tools and methods to determine which best support their strategy. When it comes to nondirect marketing, the prevailing choices include the following:

Advanced analytics approaches such as marketing-mix modeling (MMM). MMM uses big data to determine the effectiveness of spending by channel. This approach statistically links marketing investments to other drivers of sales and often includes external variables such as seasonality and competitor and promotional activities to uncover both longitudinal effects (changes in individuals and segments over time) and interaction effects (differences among offline, online, and-in the most advanced models-social-media activities). MMM can be used for both long-range strategic purposes and near-term tactical planning, but it does have limitations: it requires high-quality data on sales and marketing spending going back over a period of years; it cannot measure activities that change little over time (for example, out-of-house or outdoor media); and it cannot measure the long-term effects of investing in any one touchpoint, such as a new mobile app or social-media feed. MMM also requires users with sufficiently deep econometric knowledge to understand the models and a scenario-planning tool to model budget implications of spending decisions.

Heuristics such as reach, cost, quality (RCQ). RCQ disaggregates each touchpoint into its component parts-the number of target consumers reached, cost per unique touch, the quality of the engagement-using both data and structured judgment. It is often used when MMM is not feasible, such as when there is limited data; when the rate of spending is relatively constant throughout the year, as is the case with sponsorships; and with persistent, always-on media where the marginal investment effects are harder to isolate. RCQ brings all touchpoints back to the same unit of measurement so they can be more easily compared. It is relatively straightforward to execute, often with little more than an Excel model. In practice, though, calibrating the value of each touchpoint can be challenging given the differences among channels. RCQ also lacks the ability to account for network or interaction effects and is heavily dependent on the assumptions that feed it.

Emerging approaches such as attribution modeling. As advertising dollars move online, attribution becomes increasingly important for online media buying and marketing execution. Attribution modeling refers to the set of rules or algorithms that govern how credit for converting traffic to sales is assigned to online touchpoints, such as an e-mail campaign, online ad, social-networking feed, or website. Those credits help marketers evaluate the relative success of different online investment activities in driving sales. The most widely used scoring methods take a basic rules-based approach, such as "last touch/click," which assigns 100 percent of the credit to the last touchpoint before conversion. But newer methods that use statistical modeling, regression techniques, and sophisticated algorithms that tie into real-time bidding systems are gaining traction for their analytical rigor. While these approaches are a step up from methods tied to rules, they still typically depend on cookie data as an input, which limits the richness of the data set and consequently makes it difficult to accurately attribute the importance of each of the online touchpoints.

2. Integrate capabilities to generate insights

Although some companies rely on just one analytical technique, the greatest returns come when MROI tools are used in concert. An integrated approach, which includes pulling in direct-response data and insights, reduces the biases inherent in any one MROI method and provides business leaders with the flexibility to shift the budget toward activities that produce the most bang for their buck.

So how do these techniques work together? A company may find, for instance, that TV, digital, print, and radio make up about 80 percent of its marketing spending. Since those activities generate audience-measurement data that can be tracked longitudinally, it makes sense to use MMM. But digital spending can be refined further using attribution modeling to pinpoint the activities within broad categories-such as search or display-that are likely to generate the most conversion. The company could then use heuristics analysis such as RCQ to monitor the remaining 20 percent of its spending, which may go toward sponsorships and out-of-home advertising to capture the company's non-TV-watching target audience.

Developing common response curves across analytical techniques helps marketers put the values of different approaches on common footing. The organization can then use a decision-support tool to integrate the results, allowing business leaders to track and share marketing performance on a near-real-time basis and course correct as needed.

An international power company, for example, used RCQ analysis to adjust its out-of-home and sponsorship mix, efforts that increased reach within its target audience and raised the efficiency of marketing communications by 10 to 15 percent. The company then turned to MMM to get a more granular MROI assessment of its spending on digital versus traditional media. It found that while each €1 million invested online generated 1,300 new consumers, the same investment in TV, print, and radio helped the company retain 4,300 consumers (40 percent of whom were likely to stay loyal to the brand over the long term). Those insights helped the company understand where to best focus its spending and messaging for both attracting new customers and keeping existing ones.

In fine-tuning the mix, it can be tempting to allocate money to short-term initiatives that generate high ROI. That bias is fed by the fact that so much data comes from consumers engaging in short-term behavior, such as signing up for brand-related news and promotions on a smartphone or buying a product on sale. That short-term effect typically comprises 10 to 20 percent of total sales, while the brand, a longer-term asset, accounts for the rest. Businesses need to ensure their mix models are capable of examining marketing effectiveness over both time horizons.

One consumer food brand almost fell into this short-term trap. It launched a campaign using Facebook advertising, contests, photo-sharing incentives, and shared-shopping-list apps. At a fraction of the cost, the approach delivered sales results similar to those generated by more traditional marketing, which included heavy TV and significant print advertising. Not surprisingly, the brand considered shifting spending from TV and print advertising to social-media channels. Yet when long-term effects were included in its calculations, the impact of its digital efforts was cut by half. If the company had proceeded with significantly cutting its TV spending, as traditional MMM suggested, it would have reduced the net present value of the brand's profit.

3. Put the analytical approach at the heart of the organization

It's not uncommon for teams to outsource analysis or throw it over the wall to an internal analytics group. When the findings come back, however, those same teams may be reluctant to implement them because they don't fully understand or trust the numbers.

To solve that problem, marketers must work closely with data scientists, marketing researchers, and digital analysts to question assumptions, formulate hypotheses, and fine-tune the math. Companies also need to cultivate "translators," individuals who both understand the analytics and speak the language of business. One financial-services company, for instance, set up councils within its marketing function to bring the creative and analytical halves of the department together. The councils helped analysts understand the business goals and helped creatives understand how analysis could inform marketing programs. We've seen such collaboration cut the duration of MROI efforts in half.

Speed and agility are also important. Insights from the consumer decision journey and the marketing-mix allocation should inform the tactical media mix. Actual results should be compared with target figures as they come in, with the mix and budget adjusted accordingly. Attribution modeling can be especially helpful with in-process campaign changes, since digital spending can be modified on very short notice. Our research shows that the best-performing organizations can reallocate as much as 80 percent of their digital-marketing budget during a campaign.

The pressure on business leaders to demonstrate return on investment from a diverse portfolio of marketing programs is only increasing. The data to make smarter decisions are available, as are the analytical tools. We believe that taking an integrated analytics approach is the key to uncovering meaningful insights and driving above-market growth for brands.

For more from McKinsey on the topic of marketing and sales, visit the McKinsey on Marketing & Sales website.

About the author(s)

Rishi Bhandari is a senior expert in McKinsey's Chicago office, Marc Singer is a director in the San Francisco office, and Hiek van der Scheer is a consultant in the Amsterdam office.

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Using marketing analytics to drive superior growth

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Companies have so many analytical options at their disposal that they often become paralyzed, defaulting to just one tool. But an integrated marketing-analytics approach is the key to driving growth. There's no question that the development of better analytical tools and approaches in recent years has given business leaders significant new decision-making firepower.

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Business Analytics:  India's analytic and intelligence revenue

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Business Analytics:  India's analytic and intelligence revenue

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Julie Depowski

This article addresses how business in India has begun to prioritize the effective analysis and utility of business data. The revenue associated with these business analytic fields has increased greatly over the past year and is forecasted to continue to increase in this current year. There are constant changes to the way that business data is analysed with a particular increase in emphasis on how a business can monitor and access organizational data.

With regards to our course material covered to data, this article helps to reinforce the significance of data management. It explains the importance of business analytics and business intelligence. This article supports the course material in explaining how business analytics is an increasing part of business development and the future direction of business in order to predict trends and stay competitive in the rapidly changing technology market. This article shows that business analytics is not just a Western priority but is rather a global business priority.

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Bangalore: Indian business intelligence ( BI) software revenue is forecast to reach $245 million (in constant currency) in 2017, a 24.4 percent increase over 2016 revenue of $206 million, according to Gartner. This forecast includes revenue for BI platforms (comprising of traditional and modern BI platforms), data science platforms, analytic applications and CPM Suites.

BI /analytics has deepen in terms of key elements of data management, such as data integration, data quality, data governance, master data management (MDM), and the upcoming tools, architectures, processes, skills and roles that together allow organizations to leverage data as an asset. Digital business dynamics are changing the complete field of data and analytics.

Indian chief information officers ( CIOs), chief data officers (CDOs), and other data and analytics leaders must evolve traditional approaches to focus on business outcomes, go with the market trends, benefit from algorithmic business, adopt new technologies and most importantly build trust.

"The data and analytics market is undergoing a significant change. Adoption of machine learning techniques for data management and analytics, the settling of hype around big data through more mature data storage, and processing and analysis solutions are a few of the changes. We are also seeing the rapid shift to the cloud and hybrid data management through focused offerings, and the emergence of modern BI platforms, smart data discovery and self-service data preparation solutions, which are all fueling the next round of investments," said Ehtisham Zaidi, Principal Research Analyst - Gartner.

"We are also seeing a huge focus on Internet of Things ( IoT) data integration, data management and analytics by Indian companies, particularly in the consumer packaged goods (CPG) and manufacturing sector, who are determined the lead the market through competitive differentiation in these key areas,"said Zaidi.

The BI platforms segment (comprising of modern and traditional BI platforms) continues to lead the BI software market in India (see Table 1). All segments of the BI software market (other than the traditional BI segment) are expected to experience double-digit growth in the next two years in India.

"We see signs of the emerging importance of data management and analytics in India as senior executives are increasingly exploring the different styles of analytics to resolve their business imperatives," said Zaidi.

"There is an increased emphasis on the ability to access, monitor, process and analyze the ever growing organizational data due to the advent of IoT, big data and cloud for business management, and strides towards a digital business. Big data use cases are maturing and have executive visibility. This is leading to more investments in data management and analytics," added Zaidi.

"Purchasing decisions continue to move from IT leaders to line-of-business executives and users who want more agility and more flexible personalized options - making the land-and-expand model the new norm. This is in stark contrast to the large, enterprise-scale deals that fueled double-digit growth at a time when IT had larger budgets and wielded much more influence in buying decisions," concluded Zaidi.

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India BI and Analytics software revenue to reach $245 million in 2017: Gartner - ET CIO

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Bangalore: Indian business intelligence ( BI) software revenue is forecast to reach $245 million (in constant currency) in 2017, a 24.4 percent increase over 2016 revenue of $206 million, according to Gartner. This forecast includes revenue for BI platforms (comprising of traditional and modern BI platforms), data science platforms, analytic applications and CPM Suites.

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BlueData Introduces EPIC 3.0 for Large-Scale Deployments of Big Data Analytics

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BlueData Introduces EPIC 3.0 for Large-Scale Deployments of Big Data Analytics

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Aubrey Leppek
This article was created on June 14, 2017, and informs readers of a new program being released. The article regards the new app as "increased scalability, enhanced networking, significant security and performance optimizations, and new functionality for distributed data science operations." It's also said that with this release also comes a quick and easy way to deploy large production environments for Big Data Analytics and data science running in Docker containers. This app can also be used on-premises,  in the public cloud, or in a hybrid architecture. Previous to the release, the company that created the app expanded and had an increase in docker containers which in return made the release possible.
With that being said,  this article relates to this class due to the facts that it's on business analytics. It helped me understand the real world application of the subject as well as how a company can use business analytics to help improve other companies.

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b y Angela Guess

According to a new press release , " , provider of the leading Big-Data-as-a-Service (BDaaS) software platform, today announced BlueData EPIC™ version 3.0. This major new release provides increased scalability, enhanced networking, significant security and performance optimizations, and new functionality for distributed data science operations. With BlueData EPIC 3.0, enterprises can quickly and easily deploy large-scale production environments for Big Data analytics and data science running in Docker containers - either on-premises, in the public cloud, or in a hybrid architecture."

The release goes on. "Enterprise deployments of the BlueData EPIC software platform have expanded significantly over the past year, driven by increased adoption of Docker containers and cloud computing for Big Data. Many of these enterprises want their Big Data workloads running in a hybrid model that spans both on-premises and public cloud infrastructure, whether for initial dev/test and sandbox environments or large-scale production deployments. These organizations need a solution that can scale to thousands of containers, with agility and flexibility for distributed data science operations in a multi-tenant architecture. And for Big Data workloads deployed on-premises, they want minimal to no changes to their existing network and security infrastructure."

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BlueData Introduces EPIC 3.0 for Large-Scale Deployments of Big Data Analytics - DATAVERSITY

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by Angela Guess According to a new press release, "BlueData®, provider of the leading Big-Data-as-a-Service (BDaaS) software platform, today announced BlueData EPIC™ version 3.0. This major new release provides increased scalability, enhanced networking, significant security and performance optimizations, and new functionality for distributed data science operations.

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How does business analytics provide a competitive edge? 

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How does business analytics provide a competitive edge? 

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Chassie Moore
https://channels.theinnovationenterprise.com/articles/how-does-business-analytics-provide-a-competitive-edge

The basis of this article is exactly what analytics and the difference between business intelligence and business analytics. This article also talks about how important business analytics is and how many business people do not really know or understand it. The other thing I found interesting was that the article stated that business analytics will be critical over the next 10 years.

This article relates to our class and what we are learning right now obviously first and foremost because it explains more about business analytics. It also teaches us a little more about how business intelligence and business anayltica differ which is important for us to know going forward.

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The article was posted on the website pentaho.com, a Hitachi group company. It gave us a quite clear definition of business analytics. “Business analytics is one aspect of business intelligence, which is the sum of all of your research tools and information infrastructure.” It also mentioned that business analytics and business intelligence are not the same. “Business analytics focuses on statistical analysis of the information provided by business intelligence. In this world of big data gathered from many different sources, giving decisions could be tricky. Business analytics is a very useful way for enterprises to make the right decisions. This is called “data-driven decision making.” The article included three main levels of business analytics’ operation: descriptive a—viewing data to determine what has happened so far; predictive—data is run through algorithms or models in order to extrapolate trends that might indicate future challenges or opportunities; and prescriptive—once a problem is identified, processes determine hypothetical outcomes for various courses of action.

The article is definitely related to the chapter covered this week. It was taking about the same topic business intelligence. Even though the article included the information covered in the chapter, it is still helpful to reinforce the knowledge that I have learned.

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DATA-DRIVEN DECISION MAKING FOR THE ENTERPRISE

As proponents of big data increasingly tout its benefits to businesses, discussions can get tangled up in the language of analysts and engineers. Simply stated, business analytics is the collection of statistical functions and mathematical processes that make sense out of raw data.

Business analytics is one aspect of business intelligence, which is the sum of all your research tools and information infrastructure. Due to this close relationship, the terms business intelligence and business analytics are sometimes used interchangeably. Strictly speaking, business analytics focuses on statistical analysis of the information provided by business intelligence.

Standalone data is often ambiguous. It takes business analytics to determine what the information suggests and drive business impact. Decision makers don't want 10,000-line spreadsheets delivered to their inboxes - they need reports that clearly indicate trends, challenges and opportunities. That is exactly what business analytics provides.

The heavy lifting is done by business analytics programs that organize and display data in familiar ways, such as visualizations, predictive analytics models and interactive dashboards.

These applications help companies understand historic performance, identify obstacles to growth and find opportunities for product development. If knowledge is power, business analytics is the generator. The end goal is to deliver useful information as quickly as the incoming data will allow.

THE ONLY WAY TO BENEFIT FROM BIG DATA

Many companies gather information from transactions, payroll systems, email campaigns and more. Without business analytics, the data are just values, variables and fields sitting on expensive servers. Business analytics software changes raw data into a real asset.

Data mining and online analytical processing (OLAP) enable end users to see correlations among data sets, as well as dive more deeply into the information. In addition to surfacing relevant new insights, these programs allow analysts to spend more time reviewing results and formulating solutions instead of crunching numbers.

Business analytics operates at three main levels:

Descriptive: Reviewing data to determine what has happened so far. Often, these are traditional reports that tell a story about current performance

Predictive: Data is run through algorithms or models in order to extrapolate trends that might indicate future challenges or opportunities

Prescriptive: Once a problem is identified, processes determine hypothetical outcomes for various courses of action

Business analytics existed prior to big data - even before computers. Advances in technology have made it more useful and responsive than ever before. For this reason, companies that wish to excel cannot afford to ignore the benefits offered by business analytics.

The Pentaho Business Analytics platform is designed to handle the ever-increasing demands on your big data infrastructure. Intuitive dashboards, easy ad hoc reporting and visual analytics allow users of all experience levels to perform insightful analyses of your data.

FINANCIAL SERVICES

Pentaho helps financial services firms identify growth, attract and retain customers, optimize operations, ensure compliance, detect fraud, and mitigate risk. Specific use cases include:

Targeting high value clients with tailored promotions and recommendations

Stability improvement of ATM networks with an easy to use reporting and analytics layer that can handle massive amounts of network and log data

Managing mortgage portfolio valuations by identifying which loans are at risk of prepayment or default

Read more detailed information about Pentaho for Financial Services use cases.

GOVERNMENT

Pentaho helps government organizations take advantage of big data, streamline operations, and ensure transparency. Specific use cases include:

Utilizing self-service, real-time analytics to reduce waste, fraud, and abuse

Support and augmentation of "human decision-making" with automated algorithms and machine learning

Provisioning new cloud-based agency services

Read more detailed information about Pentaho for Government use cases.
Learn about additional industry specific Pentaho solutions here.

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As proponents of big data increasingly tout its benefits to businesses, discussions can get tangled up in the language of analysts and engineers. Simply stated, business analytics is the collection of statistical functions and mathematical processes that make sense out of raw data.

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Five Ways Business Analytics Will Shape Business, Sports, and Politics

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Five Ways Business Analytics Will Shape Business, Sports, and Politics

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Morgan Blaine

The article talks about how business analytics is becoming more and more prominent in all careers. By using business analytics companies are going to be able to keep up with more of the mainstream ways consumers are adjusting too. Adding the analytical side within the businesses, it is able to allow IT to provide data much faster.
The article is able to relate to everything that we've learned so far in the class by talking about how business analytics is becoming more of a popular thing. It talks about the growing need for business analytics, and the different ways it can help the business world--something that was covered in this weeks chapter.

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Over the past few years, we've seen a steady rise in the importance of data analytics, in organizations as varied as consumer goods companies, professional sports franchises, political consultancies, medical research institutions, and financial firms. At the steering wheel of many of the analytics these organizations perform is not necessarily the ever-elusive data scientist but often a data analyst who understands his or her business and department in addition to the data it uses to drive decisions. In fact, the US Bureau of Labor Statistics reports that the job market for various data analyst disciplines is growing at 27% annually-far exceeding the national job growth average, which is a mere 11%. Many factors are lining up to make 2016 the "Year of the Data Analyst."

Here are five predictions of how analytics will shape the business, sports and political landscape in 2016:
Prediction #1: The hottest corporate job of 2016? Data analyst.
Demand for data analysts has never been higher-and 2016 will continue to see demand rise as more organizations look to deal with the explosion of data and the opportunity presented by having better analytics.

For start-ups to multinational corporations looking to hire qualified data analysts, the great news is that there are plenty of them. According to the US Bureau of Labor Statistics, between 4 and 6 million people are employed as data analysts. What's more, the pipeline for the future is bright: Universities are pumping out more graduates skilled in data analytics than ever before. Specialized degree programs in analytics are cropping up all over and courses in analytics are now not only standard fare for computer science students, but also business and economics students as well. The growth in this already abundant supply of data analysts is not particularly surprising, as the role offers an intellectually fulfilling and lucrative (salaries for business analysts start around $75K and climb rapidly from there) career path.

The combination of growing demand and supply in the market for data analysts will make it by far the hottest corporate job in 2016.
Prediction #2: IT will embrace the self-service analytics movement.
The increasing demand for data analytics has placed unprecedented pressures on IT to provide the infrastructure and tools that enable users across the business to get the answers they need. And the users will not only include individuals with a data analyst title, but also those in the organization who work with data every day - in marketing to operations to finance to HR - users who you can think of as empowered data workers.

In 2016, IT will embrace the concept of self-service analytics and relish the important role they can play in enabling business users with access to critical data. By providing business users with leading-edge analytics tools that allow them to create sophisticated analytics without requiring programming assistance from data scientists-and without waiting for data from IT-companies will find themselves on new competitive footing and ready to uncover previously hidden trends that catapult them into leadership positions.
Prediction #3: Predictive analytics goes mainstream.
Not only has the emergence of big data increased interest in predictive analytics, it has also significantly broadened the market for this kind of advanced analysis, since every function in an organization can see the value in investing in predictive analytics to improve business performance. Until now, though, actually running predictive analytics required hiring expensive data scientists with Ph.D. degrees, but with the demand for predictive far outpacing the supply of data scientists, we think that's all going to change in 2016. When you combine the number of new college graduates who want to use the newest, cutting-edge tools without being computer programmers with the real-life business experience of existing data analysts, this is the year that predictive analytics will become accessible to everyone.

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Five Ways Data Analytics Will Shape Business, Sports And Politics In 2016

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Photo: PRNewsfoto Over the past few years, we've seen a steady rise in the importance of data analytics, in organizations as varied as consumer goods companies, professional sports franchises, political consultancies, medical research institutions, and financial firms. At the steering wheel of many of the analytics these organizations perform is not [...]

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New Business Analytics Certificate Expands Big Data Education Programs at OU

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New Business Analytics Certificate Expands Big Data Education Programs at OU

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Adrianna Razo

This article begins by describing some of the benefits of business analytics, and states that the market is projected to double by 2020. The article reads, "To address this demand Oakland University School of Business Administration now offers a full complement of business analytics programs to meet a variety of educational and career needs." They go on to explain the importance of Business analytics, and claim that, “Employers are increasingly seeking professionals across disciplines who can harness data to solve business problems.”
I think that this goes hand in hand with what I have learned about business intelligence in this unit. Since the business and technology industries are evolving at such a rapid pace, it is important for businesses to be able to harness this data, make predictions, and ultimately make the best decisions to remain competitive in this global market.

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Every day more and more companies are leveraging the benefits of business analytics to improve customer service, enhance operational performance, identify new business markets and drive revenue growth. The industry is flourishing, and Forbes reports the market is expected to nearly double by 2020.

To address that demand, the Oakland University School of Business Administration now offers a full complement of business analytics programs to meet a variety of educational and career needs.

The newest addition is Oakland's Graduate Certificate in Business Analytics. This five-course program is ideal for working professionals from any discipline to gain knowledge in this growing field. The program is available beginning in the fall of 2017 and students can apply online now at oakland.edu/applynow.

"Business analytics is moving beyond the scope of IT professionals," said Vijayan Sugumaran, professor of Management Information Systems and chair of the Decision and Information Sciences Department at Oakland University. "Employers are increasingly seeking professionals across disciplines who can harness data to solve business problems."

Employers agree that finding people who have the requisite blend of quantitative computing and business domain knowledge and skills for business analytics is a challenge. From automotive and financial services to marketing, human resources and education, career opportunities abound for those who understand how to leverage the power of data to improve business processes.

"Oakland's Graduate Certificate in Business Analytics is perfect for someone seeking specific knowledge in analytics to enhance their performance or career prospects without committing to a full master's degree program," Sugumaran said.

The need for business analytics education includes, but also goes beyond, the IT profession. Recognizing this, the Oakland School of Business Administration offers a suite of programs to meet the varied interests of those seeking to expand their skills and knowledge in this area.

OU's complete suite of business analytics programs includes:

Graduate Certificate in Business Analytics

MBA with a business analytics concentration

Master of Science in IT Management with a business analytics concentration

Business analytics minor for Oakland undergraduate students in any major

Business analytics specialization for Oakland MIS students.

For more information about the Business Analytics Graduate Certificate, visit oakland.edu/business/graduate-executive-programs/business-analytics-graduate-certificate.
To learn more about OU's Business Analytics programs, visit oakland.edu/business/undergraduate-majors-minors/business-analytics.

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New business analytics certificate expands big data education programs at OU

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Every day more and more companies are leveraging the benefits of business analytics to improve customer service, enhance operational performance, identify new business markets and drive revenue growth. The industry is flourishing, and Forbes reports the market is expected to nearly double by 2020.

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Business Analytics and Data Science: Once Again? Sarah Moore-Ar-Rasheed

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Business Analytics and Data Science: Once Again? Sarah Moore-Ar-Rasheed

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In the article by Bichler et al., a number of definitions of business analytics and data science are explored.  According to the article there is no established academic definition for business analytics.  The term business analytics has been used in business schools for years.  The concept of business analytics is no longer limited to that setting, business analytics can enhance all fields of management.

The increase in available of large amounts of data has led to a trend in analytics.  As discussed in the Mapping the Future: The Power of Algorithms video, everything from web searches to cellular phone activity is information that can be analyzed.  The use of evidence based management and analytics based information systems has added value to a number of organizations.

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Business Analytics and Data Science, Once Again

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Tara Habeck

China’s days of being known for cheap labor and manufacturing may soon be numbered as it emerges as a tech titan. China is the world’s largest e-commerce market, generating huge amounts of data, and is the third largest market for IT spending. Industry leaders Alibaba and Tencent (WeChat) are analyzing data to optimize advertising and marketing.

This article built upon my understanding of the course by showing how quickly the opportunity and demand for data mining and predictive analytics is growing, both in the USA and abroad. Domestically and abroad, we are in a tech-savvy age and a high-tech economy.

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Historically, China maintained its position as an economic powerhouse as it boasted cheap labor and manufacturing. The country's next economic wave will capitalize on technology as its consumers flock to it at unmatched speeds and government and big businesses continue to invest in it.

While there is no question that China's economic slowdown is both real and significant to global economies, the country's IT spending has held strong.

According to research firm , China is the third largest market for enterprise IT spending, after the US and Japan, and spending on technology products and services is forecasted to reach $155.8 billion (RMB 972.4 billion) in 2016. Of these technological investments, the business intelligence (BI) market shows promise, not only as a way to improve operations, but also as a means to derive value from the growing amounts of consumer data.

China's emergence as a tech titan

China's tech industry has experienced a coming of age over the past decade. Increased research and development spending, government investments and a tech-savvy population make it fertile grounds for innovation. Industry leaders, like Baidu, Alibaba and Tencent, have made huge strides toward becoming technology powerhouses in industries like telecommunications, mobile devices, online services and more.

In the past year, Alibaba announced a $1 billion investment into its cloud computing unit Aliyun, Baidu hired Google's former AI chief Andrew Ng to lead research efforts that will help internet companies maximize data, and Intel announced a joint investment of $31.7 million with the Beijing Municipal Government and the Institute of Automation of Chinese Academy of Sciences to collaborate on Internet of Things (IoT) related technology research and innovation.

Chinese consumers are data-generating machines

Not only are China's huge enterprises leading the way in tech, but its consumers are way ahead of the tech adoption curve. More than 70 percent of China's mobile internet users use m-commerce, as compared to about 31 percent of US users, according to research from the China Internet Network Information Center , indicating that Chinese users and businesses have been fast to adopt new technologies.

According to McKinsey's 2016 China consumer report , China is the world's largest e-commerce market - generating revenue of $615 billion in 2015, around the same as Europe and the United States combined. Its consumers are increasingly purchasing online and via mobile devices. As a result, payment applications and e-commerce sites are accruing huge amounts of data.

This data presents a new type of capital. If used properly, companies can use data analytics to better understand the behaviors of valuable customer segments and engage them with tailored marketing efforts.

Big data's role in global gains

We've seen global business become more profitable by embracing big data technologies, and Chinese businesses are well positioned to do the same .

According to a report by China Industry Research, the global big data industry will generate $23.8 billion of revenue this year, growing 31.7 percent from a year ago. Estimated annual growth in China in the coming years will be more than 60 percent, with total revenue reaching $7.13 billion (46.29 billion yuan) in 2019.

We've already seen that Baidu, Alibaba and Tencent are making big business gains by finding new ways to gain insights from the growing amount of data they collect from their customers. Baidu is analyzing data based on user searches, Alibaba is putting transactional data to work and Tencent has a stronghold on social network data. Industry expert Bernard Marr put it nicely when he wrote:

At the start this was primarily driven by marketing needs, but as technology has evolved it has begun to be introduced across all industries and equally enthusiastically adopted by the government. In parallel with developments in the US, China's "big three" internet giants all started out specializing in their own niches - search, retail and social - but have since evolved into competitors as each one developed alternatives to their rivals' specialities. Now they all compete to sell targeted advertising based on big consumer data, as well as business services such as cloud hosting and analytics.

While each company has access to different data sources, all three companies have used their data to help optimize advertising and marketing. Similar to Google, Baidu's ability to tailor advertisements based on search behavior and trends has improved as its big data capabilities expand. With the ability to monitor what users are searching for, they can optimize ad costs, while also appealing to more marketers with targeted ad opportunities.

Similarly, Tencent, which runs China's largest mobile chat network, WeChat, has extensive data on the most influential users within social groups. Armed with this knowledge, Tencent can work with advertisers to target ad spend toward the most significant individuals.

While advertising and e-commerce have been the most common uses for big data, Chinese companies are expanding their use cases. In the past year, Alibaba's spinoff Ant Financial has used online shopping and transaction data to determine if small businesses qualify for loans. As internet companies move into the payments and banking space, it opens up even more opportunities for big data analytics to flourish.

All of these things point to one thing - the Chinese economy is poised to take on tech. China's tech-savvy consumers and openness toward embracing technological innovations is a harbinger of growth. With all of these factors at play, Chinese businesses are well situated to take advantage of big data analytics and enter its next phase of economic growth.

Read next: We looked at 137,052 tweets and learned hashtags were worthless

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Why China is poised for an economic resurgence with data analytics

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Historically, China maintained its position as an economic powerhouse as it boasted cheap labor and manufacturing. The country's next economic wave will capitalize on technology as its consumers flock to it at unmatched speeds and government and big businesses continue to invest in it.

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Five Ways Data Analytics Will Shape Business, Sports And Politics In 2016 

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Five Ways Data Analytics Will Shape Business, Sports And Politics In 2016 

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Vendela Hale

This article makes five predictions for the future of data analytics for the year 2016. Not only does it give these predictions but it also gives strong support to those predictions. One of the most interesting is the prediction that Data Analysis will be the “hottest” job of 2016. This is due the rapid shift in demand along with the abundant about of college graduates specializing in data analytics.  But probably the one prediction i find the most interesting is that Analytics will win the Gold Medal—and the US Presidency. This is the prediction that the winner of the election will likely have the best data analysis on their campaign. To me this was a bold but very interesting prediction. Especially now that we have seen the results of the election.

This article helps put a clear picture as to all of the implications that data analytics can have on our society. It si one thing to learn about the concept but it is completely different to be about to hear about the rising demand and the amount of college graduates looking to go into the field. Furthermore how it is used in the workplace and out. This article helps paint a full picture to the future of data analytics.

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Over the past few years, we've seen a steady rise in the importance of data analytics, in organizations as varied as consumer goods companies, professional sports franchises, political consultancies, medical research institutions, and financial firms. At the steering wheel of many of the analytics these organizations perform is not necessarily the ever-elusive data scientist but often a data analyst who understands his or her business and department in addition to the data it uses to drive decisions. In fact, the US Bureau of Labor Statistics reports that the job market for various data analyst disciplines is growing at 27% annually-far exceeding the national job growth average, which is a mere 11%. Many factors are lining up to make 2016 the "Year of the Data Analyst."

Here are five predictions of how analytics will shape the business, sports and political landscape in 2016:
Prediction #1: The hottest corporate job of 2016? Data analyst.
Demand for data analysts has never been higher-and 2016 will continue to see demand rise as more organizations look to deal with the explosion of data and the opportunity presented by having better analytics.

For start-ups to multinational corporations looking to hire qualified data analysts, the great news is that there are plenty of them. According to the US Bureau of Labor Statistics, between 4 and 6 million people are employed as data analysts. What's more, the pipeline for the future is bright: Universities are pumping out more graduates skilled in data analytics than ever before. Specialized degree programs in analytics are cropping up all over and courses in analytics are now not only standard fare for computer science students, but also business and economics students as well. The growth in this already abundant supply of data analysts is not particularly surprising, as the role offers an intellectually fulfilling and lucrative (salaries for business analysts start around $75K and climb rapidly from there) career path.

The combination of growing demand and supply in the market for data analysts will make it by far the hottest corporate job in 2016.
Prediction #2: IT will embrace the self-service analytics movement.
The increasing demand for data analytics has placed unprecedented pressures on IT to provide the infrastructure and tools that enable users across the business to get the answers they need. And the users will not only include individuals with a data analyst title, but also those in the organization who work with data every day - in marketing to operations to finance to HR - users who you can think of as empowered data workers.

In 2016, IT will embrace the concept of self-service analytics and relish the important role they can play in enabling business users with access to critical data. By providing business users with leading-edge analytics tools that allow them to create sophisticated analytics without requiring programming assistance from data scientists-and without waiting for data from IT-companies will find themselves on new competitive footing and ready to uncover previously hidden trends that catapult them into leadership positions.
Prediction #3: Predictive analytics goes mainstream.
Not only has the emergence of big data increased interest in predictive analytics, it has also significantly broadened the market for this kind of advanced analysis, since every function in an organization can see the value in investing in predictive analytics to improve business performance. Until now, though, actually running predictive analytics required hiring expensive data scientists with Ph.D. degrees, but with the demand for predictive far outpacing the supply of data scientists, we think that's all going to change in 2016. When you combine the number of new college graduates who want to use the newest, cutting-edge tools without being computer programmers with the real-life business experience of existing data analysts, this is the year that predictive analytics will become accessible to everyone.

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Five Ways Data Analytics Will Shape Business, Sports And Politics In 2016

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Photo: PRNewsfoto Over the past few years, we've seen a steady rise in the importance of data analytics, in organizations as varied as consumer goods companies, professional sports franchises, political consultancies, medical research institutions, and financial firms. At the steering wheel of many of the analytics these organizations perform is not [...]

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How Does Business Analytics Provide a Competitive Edge?

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How Does Business Analytics Provide a Competitive Edge?

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Jorge Lorenzo

“Too many businesspeople do not really know what predictive modeling, forecasting, design of experiments or mathematical optimization mean or do. However, over the next ten years the use of these powerful techniques will become standard.” Author Gary Cokins writes in his article, How Does Business Analytics Provide a Competitive Edge?,  about the importance of data analytics and what it really means in terms of the relationship between customers and organizations. He further goes on to describe in detail of how big-data and data-mining have created an entire new level of competitiveness in terms of being able to analyze this data and the risks of a business being analytic-illiterate. Mostly due to the information of being able to predict customer behavior and even forecast on upcoming trends or help make integral decisions. “The use of business analytics is a skill that is gaining mainstream value due to the increasingly thinner margin for decision error.”, he writes.

I think this article strongly supports this last chapter covered in the course because it describes the types of data and how important it is for businesses to be able to interpret it in a meaningful way. And pushing even further back, the article also strongly reinforced the theme of survival-of-the-fittest in terms of businesses adapting to a changing environment and adopting the new technologies available to them.

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How Does Business Analytics Provide a Competitive Edge? | Articles | IBM

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How Does Business Analytics Provide a Competitive Edge? Gary Cokins' takes a look at how business analytics is narrowing the margins of error

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How Machine Learning Is Helping Us Predict Heart Disease and Diabetes

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How Machine Learning Is Helping Us Predict Heart Disease and Diabetes

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Jennifer Crane

This article by the Harvard Business Review shows how Boston University’s Center for Information and Systems Engineering is using the same analytic tools used for business to predict hospitalizations for patients with diabetes and heart disease. They found by using the data from the entirety of the patient’s medical chart they can predict these hospitalizations within an 82% accuracy rate a year in advance. They are also using algorithms to predict readmissions within 30 days after surgery. This data can be used to administer preventative care and let doctors know when additional post-op monitoring is necessary.

They compare their predictive methods to the Framington 10-year risk score which uses only specific diagnostic test scores and found their method was much more accurate. They found their accuracy levels were higher because they were using a patient’s entire health record. They plan to increase this accuracy by tapping into even larger data sets, possibly using credit card purchases to determine diet, smartphone activity, fit trackers, implantable devices, and others.

Like what we have learned in Chapter 5, this article shows how tapping into big data, and the development of better computerized analytic capabilities is improving and changing the future of how things are done.

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Executive Summary

While debate drags on about legislation, regulations, and other measures to improve the U.S. health care system, a new wave of analytics and technology could help dramatically cut costly and unnecessary hospitalizations while improving outcomes for patients. For example, by preventing hospitalizations in cases of just two widespread chronic illnesses - heart disease and diabetes - the United States could save billions of dollars a year. Toward this end, the author and his colleagues at Boston University have been striving to bring the power of machine-learning algorithms to this critical problem. In an ongoing effort with Boston-area hospitals, they found that they could predict hospitalizations due to these two chronic diseases about a year in advance with an accuracy rate of as much as 82%. This will give care providers the chance to intervene much earlier and head off hospitalizations. This accomplishment is only one example of how analytics could transform health care.

While debate drags on about legislation, regulations, and other measures to improve the U.S. health care system, a new wave of analytics and technology could help dramatically cut costly and unnecessary hospitalizations while improving outcomes for patients. For example, by preventing hospitalizations in cases of just two widespread chronic illnesses - heart disease and diabetes - the United States could save billions of dollars a year.

Toward this end, my colleagues and I at Boston University's Center for Information and Systems Engineering have been striving to bring the power of machine-learning algorithms to this critical problem. In an ongoing effort with Boston-area hospitals, including the Boston Medical Center and the Brigham and Women's Hospital, we found that we could predict hospitalizations due to these two chronic diseases about a year in advance with an accuracy rate of as much as 82%. This will give care providers the chance to intervene much earlier and head off hospitalizations. Our team is also working with the Department of Surgery at the Boston Medical Center and can predict readmissions within 30 days of general surgery; the hope is to guide postoperative care in order to prevent them.

The hospitals provide patients' anonymized electronic health records (EHRs) that contain all of the information the hospital has about each patient, including demographics, diagnoses, admissions, procedures, vital signs taken at doctor visits, medications prescribed, and lab results. We then unleash our algorithms to predict who might have to be hospitalized. This gives the hospital opportunities to intervene, treat the disease more aggressively in an outpatient setting, and avoid a costly hospitalization while improving the patient's condition.

The accuracy rates of these predictions surpass what is possible with well-accepted risk scoring systems such as the one that emerged from the famous Framingham Heart Study, the ongoing long-term cardiovascular cohort study that is now in its third generation of participants. Using that system, a doctor assesses the patient's age, cholesterol, weight, blood pressure, and several other factors to arrive at the individual's chances of developing cardiovascular disease over the next 10 years. Using the Framingham Study 10-year cardiovascular risk score, one can predict hospitalizations with an accuracy of about 56%, which is substantially lower than the 82% rate we achieved.

In fact, we found that feeding the factors used in the Framingham 10-year risk score into more sophisticated machine-learning methods still leads to results inferior to ours (an accuracy rate of about 69%). This suggests that using the entirety of a patient's EHR (which can contain as much as 200 factors) instead of just a few key factors leads to superior prediction results. What's more, an algorithmic approach can easily be scaled so it can be applied to a very large number of patients - something that is impossible with human monitors only.

The potential benefits from applying machine-learning analytics in health care are enormous. Based on a study of a year's worth of hospital admissions, the U.S. Agency for Healthcare Research and Quality (AHRQ) estimated that 4.4 million of those admissions in the United States, totaling $30.8 billion in costs, could have been prevented. Of that $30.8 billion, $9 billion was for patients with heart diseases and $5.8 billion for patients with complications from diabetes. That's half of all unnecessary hospitalizations.

Just 5% of Medicaid's 70 million beneficiaries account for 54% of Medicaid annual expenditures of more than $500 billion, and 1% account for 25% of the total. Of this 1%, 83% have at least three chronic conditions. Approaches like ours could reduce their use of hospital services and save Medicaid a large amount of money.

Ongoing U.S. reforms in health care that link payments with outcomes are forcing hospitals to assume more financial risks. In response, hospitals are increasingly making analytics and new technologies an integral part of hospital operations. Business analytics widely used in the transportation industry by airlines and shipping companies are beginning to be employed to schedule operating rooms and staffing. Other algorithms are being developed to assist physicians in making diagnoses. My team has developed methods to automatically titrate medications in intensive care units in response to the patient's condition.

These advances are only the tip of the iceberg. We are on the cusp of major changes in health monitoring and care. Google and other companies with lots of experience in collecting and learning from data appear ready to step into this domain. A myriad of technologies, from implantable medical devices (such as defibrillators and pacemakers) to fit trackers, smart watches, and smartphones already capture our health data and lifestyle choices. Our credit-card and electronic-payment systems know our purchase history and the type of food we consume. The result is the emergence of a rich personal health record we carry in our pockets.

If we can now predict future hospitalizations with more than 80% accuracy using medical records alone, imagine what is possible if we can tap into this trove of personal data. Recommender systems could be used to nudge us to adopt healthier eating habits and behaviors. The holy grail of heading off the emergence of conditions by keeping people well could be realized.

Yes, analytics and data-driven personalized medicine and health monitoring present risks. Do we want our employers and health insurers to know the status of our health and the risks we face? Privacy, security, and reliability of new systems and methods are also critical concerns. But rather than retreating from this new era, we should be working on how to strengthen our methods, institutions, laws, and regulatory framework to avoid those unintended consequences. Algorithms - the foundation of encryption methods, privacy-preserving data processing, and intrusion- and fraud detection systems - could help.

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How Machine Learning Is Helping Us Predict Heart Disease and Diabetes

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Executive Summary While debate drags on about legislation, regulations, and other measures to improve the U.S. health care system, a new wave of analytics and technology could help dramatically cut costly and unnecessary hospitalizations while improving outcomes for patients.

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Microsoft Widens business Intelligence Approach- Is everyone a Business Analyst Now?

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Microsoft Widens business Intelligence Approach- Is everyone a Business Analyst Now?

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Arthur Denys

This article describes how recently with the increase in technology and the way that our society is headed, everyone will soon have access to business analytics. Business intelligence is headed in that direction and Microsoft is letting everyone know that this will be available for people to use on their apps as well as other apps that we use on a daily basis.
One of the biggest things for Microsoft will be to adapt its applications and change its marketing techniques to accommodate for how people will be using them. This risks changing drastically from how and who their applications are currently being used.

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13 June, 2017

In the future, everyone will be a business analyst. Well, not exactly, workers will actually carry on being salespeople, dentists, pharmaceutical research specialists, children's entertainers, accountants and cakemakers. To be clearer then... in the future, everyone will have access to a degree of business analytics embedded inside the everyday software applications that they use on day-to-day basis. This is the way so-called Business Intelligence (BI) is going and every major IT vendor has a stake in this market.

When we talk about BI in this sense, we are of course referring to business 'dashboards' (not unlike the speedometer dials in an automobile) that display core enterprise performance metrics and Key Performance Indicators (KPIs). A good BI dashboard will be fully customizable so that users can a) arrange the screen in the way they want and, more fundamentally, b) choose which data streams from which departments or line of business functions are presented in summary graphical form.
Read full story

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Microsoft Widens Business Intelligence Approach - Is Everyone A Business Analyst Now? - BI News - Business Intelligence

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In the future, everyone will be a business analyst. Well, not exactly, workers will actually carry on being salespeople, dentists, pharmaceutical research specialists, children's entertainers, accountants and cakemakers. To be clearer then... in the future, everyone will have access to a degree of business analytics embedded inside the everyday software applications that they use on ...

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Significance of Business Analysis-Ryan Pierce

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Significance of Business Analysis-Ryan Pierce

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The article "Significance of Business Analysis", explains the role that business analysis plays in business, how it's significant, and in what ways it is important to have effective business analysis.
The information we've gone over has also gone over how business analysis can affect a company. We've seen the impacts it makes in various business fields and how important it is.

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TMW Reveal Series Business Intelligence and Analytics- Deseree Klann

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TMW Reveal Series Business Intelligence and Analytics- Deseree Klann

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https://www.ajot.com/news/tmw-reveal-series-business-intelligence-and-analytics
This article is about a transportation company coming out with a new system to make their work a lot easier.
By using business intelligence and analytics they now have a platform to keep all of their data and compare and expand it.
This article builds on my knowledge of how important having a database is. It can change the way everything is done and improve a business in so many different ways.

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What Hospitals Can Learn from Airlines About Buying Equipment

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What Hospitals Can Learn from Airlines About Buying Equipment

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Rakia Ruffin

This article talks about hospitals and their lack of connection between medical technology. By now, the technology in a hospital should be mind blowing. For ventilators for example, You should not have to tell the machine what air pressure to be set at. Innstead, the machines should be compatible enough with each other to read a patient air mass. Medical staff should not have to depend on two seperate machines or doing things manually.
In our lessons, we have been talking about how much technology has changed and how it is taking over everything. Technology now days is life line. I feel that we do need to keep up with all the changes thats been going on. I work at a hospital so I see first hand everything the medical staff has to do as far as charting on the computer and doing it manually, instead of the machines keeping temporary record of the patients.

https://hbr.org/2017/06/hospitals-are-dramatically-overpaying-for-their-technology

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Five Ways Data Analytics Will Shape Business, Sports and Politics In 2016- Shelby Wachenr

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Five Ways Data Analytics Will Shape Business, Sports and Politics In 2016- Shelby Wachenr

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This article describes five predictions of how the topic of data analytics will shape business, sports, and political landscape in the year 2016. Forbes predicted that in 2016, the hottest corporate job would be in data analytics because there is such a high demand in that field. Another prediction is that IT will embrace the self-service analytics movement because of the demand for data analytics. Predictive analytics will also go mainstream because of how much it improves business performance. They say this will be the year for data to be combined with other information for a better depth of an analysis. Data analytics is then taken to sports where the information is used for a personal player performance.
Terms that we have learned about in class have also come up in this article which was helpful when reading and being able to understand the topic more. These topics will also come up in the future so it's good to read up on them now!

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Over the past few years, we've seen a steady rise in the importance of data analytics, in organizations as varied as consumer goods companies, professional sports franchises, political consultancies, medical research institutions, and financial firms. At the steering wheel of many of the analytics these organizations perform is not necessarily the ever-elusive data scientist but often a data analyst who understands his or her business and department in addition to the data it uses to drive decisions. In fact, the US Bureau of Labor Statistics reports that the job market for various data analyst disciplines is growing at 27% annually-far exceeding the national job growth average, which is a mere 11%. Many factors are lining up to make 2016 the "Year of the Data Analyst."

Here are five predictions of how analytics will shape the business, sports and political landscape in 2016:
Prediction #1: The hottest corporate job of 2016? Data analyst.
Demand for data analysts has never been higher-and 2016 will continue to see demand rise as more organizations look to deal with the explosion of data and the opportunity presented by having better analytics.

For start-ups to multinational corporations looking to hire qualified data analysts, the great news is that there are plenty of them. According to the US Bureau of Labor Statistics, between 4 and 6 million people are employed as data analysts. What's more, the pipeline for the future is bright: Universities are pumping out more graduates skilled in data analytics than ever before. Specialized degree programs in analytics are cropping up all over and courses in analytics are now not only standard fare for computer science students, but also business and economics students as well. The growth in this already abundant supply of data analysts is not particularly surprising, as the role offers an intellectually fulfilling and lucrative (salaries for business analysts start around $75K and climb rapidly from there) career path.

The combination of growing demand and supply in the market for data analysts will make it by far the hottest corporate job in 2016.
Prediction #2: IT will embrace the self-service analytics movement.
The increasing demand for data analytics has placed unprecedented pressures on IT to provide the infrastructure and tools that enable users across the business to get the answers they need. And the users will not only include individuals with a data analyst title, but also those in the organization who work with data every day - in marketing to operations to finance to HR - users who you can think of as empowered data workers.

In 2016, IT will embrace the concept of self-service analytics and relish the important role they can play in enabling business users with access to critical data. By providing business users with leading-edge analytics tools that allow them to create sophisticated analytics without requiring programming assistance from data scientists-and without waiting for data from IT-companies will find themselves on new competitive footing and ready to uncover previously hidden trends that catapult them into leadership positions.
Prediction #3: Predictive analytics goes mainstream.
Not only has the emergence of big data increased interest in predictive analytics, it has also significantly broadened the market for this kind of advanced analysis, since every function in an organization can see the value in investing in predictive analytics to improve business performance. Until now, though, actually running predictive analytics required hiring expensive data scientists with Ph.D. degrees, but with the demand for predictive far outpacing the supply of data scientists, we think that's all going to change in 2016. When you combine the number of new college graduates who want to use the newest, cutting-edge tools without being computer programmers with the real-life business experience of existing data analysts, this is the year that predictive analytics will become accessible to everyone.

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Five Ways Data Analytics Will Shape Business, Sports And Politics In 2016

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Photo: PRNewsfoto Over the past few years, we've seen a steady rise in the importance of data analytics, in organizations as varied as consumer goods companies, professional sports franchises, political consultancies, medical research institutions, and financial firms. At the steering wheel of many of the analytics these organizations perform is not [...]

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Business Intelligence and Analytics in the Cloud

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Business Intelligence and Analytics in the Cloud

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John Shuler
https://www.forbes.com/sites/louiscolumbus/2017/02/26/business-intelligence-and-analytics-in-the-cloud-2017/#68a76770a289

This article by Louis Columbus highlights the ways in which business analytics use is changing. More and more BI work is done using cloud computing, and use of the cloud is up 14% from 2013 to 2016. The structure of cloud used for business analytics varies, dependent primarily on the size of the business in question; and most businesses report that cost savings is the primary factor in adopting cloud computing.
This article serves to reinforce how rapidly technology is advancing and being adopted. Many new technologies are emerging, and businesses must keep up with what will be useful if adopted into their approach. Maintaining a competitive advantage is important, and increasingly dependent on identifying new and useful technologies to use.

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Business Intelligence And Analytics In The Cloud, 2017

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Business Intelligence And Analytics In The Cloud, 2017

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Garrett Anthony
https://www.forbes.com/sites/louiscolumbus/2017/02/26/business-intelligence-and-analytics-in-the-cloud-2017/#3fca916aa289

This article in tails how we use analytic trends to further our intelligence. One of the major points was to use Analytics to benefit the business. It also went over basic information and how to use data to help you.
That is where the article  helped me it gave me a better understanding  how Analytics is used in  every day life. I also found the examples they gave me a nice visual to use now and in the future.

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Top 11 Business Intelligence and Analytic Trends for 2017 - Tyler Schingeck

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Top 11 Business Intelligence and Analytic Trends for 2017 - Tyler Schingeck

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This article covers exactly what the title says, the top 11 trends for business intelligence and analytics for 2017. The article predicts that predictive analytics will be the number one trend for businesses in 2017, followed by, artificial intelligence, business intelligence center of excellence, collaborative business intelligence, cloud analytics, embedded business intelligence, security, data governance, digitization, visual data discovery, data storytelling and data journalism.

We have touched base on predictive analytics in class and the article defines it as an extension of data mining, by using data from previous years it allows businesses to discover trends from the past that they can apply to their business to help predict future trends. Another thing I found very interesting in the article is that it predicts by 2018 more than half of large organizations will use artificial intelligence to help make sense of it's algorithms used in predictive analytics.

http://www.datapine.com/blog/business-intelligence-trends-2017/#

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Which analytics tools are business ready now?- Maryanne Carter

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Which analytics tools are business ready now?- Maryanne Carter

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http://www.information-age.com/analytics-tools-business-ready-123466355/

This article touches base on the different types of business intelligence tools that are used in big business. Machine Learning, Artificial intelligence, and internet of things are the three they discussed. Every company needs certain types of business intelligence tools to get all of their data organized to the point that they can digest it and use it. Users being able to understand the data they are presented with is usually the biggest issue a company can face.

The article explains more about dashboards which we have touched base on in the course so far. It states that the dashboard allows users to get an understanding of the big picture.

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What is Business Intelligence, Machine Learning, and Business Analytics

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What is Business Intelligence, Machine Learning, and Business Analytics

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Eric McGuigan

I started reading an article with the intent of sharing it here, thinking it would somehow apply to my role as a Business Analyst at Ford. As I read the fifth paragraph about re-imagining social media and the push towards machine learning skills, I starting to think back on my experiences with in these spaces: Predictive analytics for e-commerce and online marketing....and how inefficient it is at predicting my next purchase or where my advertising preferences sit.

Next stop, Google: machine learning. The second top story returned is titled "How to tell if AI or machine learning is real."

Right out of the gate, the author defines the two terms.

AI utilizes 'cognitive technologies to enable ad hoc or situational reasoning, planning, learning, communication, perception, and the ability [to] manipulate objects to an intended purpose." When combined, proponents promise entities (hardware or software) that biological animal species possess.

Machine learning is a "subset" of AI, and refers specifically to software that is coded to detect patterns and observe outcomes, then apply that analysis to modify its "behaviour" or guide people to better results. Per the author, it does not "require the kind of perception and cognition that we associate with intelligence; it simply requires really good, really fast pattern matching and the ability to apply those patterns to its behavior and recommendations. While humans and other animals may learn the same way (observe what works and do that more often, while avoiding what you observe doesn't work), machines only do what is told or programmed to do.

This explains why I'm advertisements are geared towards what I've already purchased, but not really beyond. For instance, I buy a bunch of building materials to build a wet bar. The software knows what I purchased (cabinets, lumber counter top, sink), but keeps telling me about sales and deals on similar products. Why aren't they trying to promote items that I may need to go with those materials (dish soap, booze, cabinet hardware like knobs and handles)?

The truth is this: tech is a lot like people. Some are smart, which requires intelligence and cognitive ability. Others are savvy, "which only requires information and ability to take advantage of it." Now I disagree with the his pessimism because you have to be smart to be opportunistic, but I do see how the limitations of technology are based in that of its programmer.

https://inc42.com/resources/machine-learning-business-intelligence/

http://www.infoworld.com/article/3199295/artificial-intelligence/primer-how-to-tell-if-ai-or-machine-learning-is-real.html

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Business analytics in the age of Big Data-LaShonda Dailey

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Business analytics in the age of Big Data-LaShonda Dailey

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The increase in interest surrounding big data will lead to a greater respect and exercise of business analytics.  Business analytics can be beneficial no matter the size of the data.  According to the author Vahn, business analytics success stories have been observed in healthcare, government, and e-commerce.  Many MBA programs teach small data analytics approaches.  Going from small data analytics to big data analytics can be challenging.  Many of the big business such as Amazon, Google, and Facebook, employ data scientists (p.9).  A report in 2011 estimated, “by 2018, there will be a shortage of 140,000 to 190,000 workers with ‘deep analytical’ experience” in the workforce (p.9).

In the article, the three types of traditional business analytics are discussed: descriptive, predictive, and prescriptive.  The course text also expands on these three types of business analytics.  It is important to access which types of data you are looking for.  Each type of analysis yields different information, big data versus traditional analytics.  Some data sets are too big for traditional tools.  Overall, business analytics rather small or large can generate value.

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business analytics (BA)--Redha Abu Rshaid

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business analytics (BA)--Redha Abu Rshaid

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The article defines and explains the practice of Business analytics, along with discussing its application and uses. Business analytics is a method used to explore the data of an organization, which is applied by a data-driven decision making approach. The quality of the data provides a successful business analytics. Furthermore, the article provides two important examples of BA: 1) Basic business intelligence, which provides historical data to study the performance of a company. 2) Deep statistical analysis that apply statistical algorithms to make certain predictions about the performance of a product. In the article, the applications of business analytics were described, such as; Data visualization tools, big data platforms, and self-services analytics platforms.

The article helped me extend my knowledge of the concept of BA, and think about it in a different way that involve a real world application. The article helped me understand the importance of data, and data access to the development of company’s performance, and production quality.

http://searchbusinessanalytics.techtarget.com/definition/business-analytics-BA

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Three things business owners needs to know about business intelligence - Josh Dean

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Three things business owners needs to know about business intelligence - Josh Dean

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This article starts off by stating just how important business intelligence is for companies and how information for smaller to medium sized companies are becoming more and more accessible. it quote "“Information is the oil of the 21st century, and analytics is the combustion engine." which is so incredibly true. the first subject the article talks about is self-service BI for small business. Which talks about how information is accessible for anyone these days who know where to look. Part two talks about data for visualization. which goes over how all this new data that is now so easily obtained is not just easy to find but also well put in a way thats easy to visualize and make better decisions from. The last part talks about the security of this information and how because information goes through so many hands it will be easier to leek to those a company may not want to see.
All in all, this article talks about the importance of business intelligence and how essential it is to modern day businesses. which is fine and all but it also comes with some risks. learning how to be good at both is what will make or break the company you work for and will only become more important as time goes on.
https://www.entrepreneur.com/article/289500

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What is Business Intelligence and why do we need it? - Michelle Bartus

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What is Business Intelligence and why do we need it? - Michelle Bartus

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This article is very interesting, it explains why small businesses are reluctant to business intelligence unlike the larger corporations. Data mining is out there, and almost all companies should utilize it, to make their companies successful.  Being more knowledgeable in business intelligence, will make your company have more advantages over other companies, and you can make more informed decisions.  Times are changing, data is growing and so are the companies around us. Without business intelligence no company can make it, we need to make inform decisions and know the facts.

Business intelligence is not just limited to the data, but it also gives you dashboards.  These dashboard, show key performance indicators in a visual presentation for more clear understanding.  Different views of data,  such as in charts or graphs might grasp the viewer in a different manner.

Business intelligence is the charts, graphs, and dashboards they we use in the workplace now.  We just have to understand how the information got there, and where the data is from.

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The term "business intelligence" came into use in the 1960s to describe a system for sharing information among departments. Today, business intelligence (BI) tools enable self-service data management and digital data analysis. BI software solutions efficiently collect data to help owners and managers make informed business decisions. These critical insights can help your company adapt to industry and market fluctuations.

By 2025, the business intelligence market is expected to grow to $40.5 billion. So whether you have a small business or an enterprise, your company can benefit from BI solutions to stay ahead of the curve, decrease operating costs, and sustain and grow customer relationships.

We'll explore business intelligence, why a business owner needs it, and how BI relates to competitive intelligence and artificial intelligence.

What is business intelligence?

Business Intelligence is not a single piece of software or even a software suite for crunching big data. Instead, it is an umbrella term that includes best business practices, software, infrastructure and other tools to optimize decision-making and enhance performance.

In essence, BI is about information analysis. A database pulls together information from the different parts of your business, and then applications convert that raw data into reports, charts and other analytical tools to provide insights.

Business owners can use BI in many ways:

1. Examine any aspect of your business.

2. See how well your sales efforts are going.

3. Assess the efficiency of your manufacturing processes.

4. Measure employee performance and company goals.

5. Uncover and track industry trends using data from multiple departments.

6. Inform business leaders who make critical companywide decisions.

7. Track historical data.

8. Make custom reports based on specific queries.

9. Design graphs, charts, etc., based on collected data.

10. Stay competitive in your industry.

When tracking industry trends via social media, identify and track trending topics on sites like Facebook, Instagram, TikTok, Reddit and Twitter.

How does business intelligence work?

These are some of BI's analytical applications:

Collaborative BI

Mobile BI

Open-source BI

SaaS BI

Operational BI

Real-time BI

Technology is not just about gathering intelligence but about making immediate sense of data through visualization applications.

BI also offers dashboards and performance scorecards. Key performance indicators and business metrics are much easier to understand when displayed as visualized data.

How can small businesses use business intelligence?

Attempts to analyze data without BI are clumsy. For instance, teams often tabulate information on Microsoft Excel spreadsheets. However, collecting the data is time-consuming, and it's tedious to put it together in an easy-to-grasp way.

Even worse, the results are not always accurate or insightful. While Microsoft Excel has many excellent uses, it's like using a hammer when you need a jackhammer. BI tools are bigger, better, faster and more efficient for significant tasks, like taking a company to the next level.

Properly analyzing data can mean the difference between a modest profit and outrageous success. Proper analytics give you some important abilities:

Gain insight into trends. Analytics offers insights into industry trends and helps you spot effective marketing opportunities you would have otherwise missed.

Better serve customers. Customer analytics helps you learn what customers want and need from your company. This information can help you redesign your business to gain more customers.

Advantages of incorporating business intelligence

BI tools can help take your business to the next level. By increasing productivity in your workforce and improving the customer experience, BI tools provide the data you need to make proactive business decisions.

Traced accountability

BI tools can assign tasks and reporting to teams and individuals to create a traced accountability system within the business. Holding your team accountable can increase loyalty by providing each person with a clear understanding of their role and reducing confusion among departments.

Increased productivity

BI tools boost productivity in several ways:

Companies of any size can create custom reports.

Automated data can increase cash flow by decreasing the costs of labor and resources.

By automating busy work, employees can be more productive and efficient.

Frequent reporting can eliminate the need for an IT team and consultants.

Streamlined processes

BI gives companies access to a wide variety of data that can help streamline business processes, reduce pinch points and set measurable standards. You can use data reporting in real time, leading to better, faster business decisions. For example, team members can analyze detailed client information and forecasting before a sales meeting. As a result, it's easier to connect with the client and conduct business more professionally.

Competitive advantage

Using BI tools to analyze customer data can determine how to engage customers, anticipate buying hot spots and downturns, and monitor industry and market changes. Anticipating your customers' needs is a substantial competitive advantage and can keep you one step ahead for maximum profit potential.

Improved customer experience

BI tools also help your customers. By connecting phone records, text data from live chats and prior purchases, you can offer excellent customer service using data history to solve problems.

Verizon invested in more than 1,500 employee dashboards across multiple departments. The dashboards housed data from customer support live chat sessions. By collectively analyzing the data, Verizon eliminated 43% of its support calls.

Disadvantages of incorporating business intelligence

Although there are many benefits to BI tools, implementing them isn't always easy, especially for small businesses. There are a few things to consider before diving in.

Cost: BI tools can be costly and require a significant upfront investment. ROI can make up for the cost, but usually not within the first year. If budget is a concern, consider using self-service BI tools. These tools can help you advance your business without needing IT support for onboarding or minor adjustments.

Complexity: Depending on your business tool knowledge level, you might have to hire a team for onboarding and initial training. Plus, if you use different BI products and try to combine them, the data could be confusing and even overlap. Look for an all-in-one BI solution that can house all your necessary tools. Or find a third-party data connector to make sense of the varied information you gather from different BI tools.

Security: If you invest in BI tools that house your customer and employee data, any error in your system could put that information in the wrong hands. To decrease the risk of a data breach, choose a BI company with security best practices in place, and thoughtfully decide what data you will allow your BI tools to access.

Employee resistance: Implementing new tools across all departments can come with its share of obstacles. While some departments might catch on quickly and find BI tools to benefit their time and knowledge, other departments may find it cumbersome and give up on it entirely. Look for intuitive software that solves employee complaints and has clear actions and goals. The more employees adopt the tools, the easier it will be to maintain your data and profit from the analysis.

Conflict between sales and marketing teams isn't uncommon, due to clashing strategies and role confusion. You can alleviate this stress by outlining a cohesive strategy that lets each team play a crucial role in the company's success.

Business intelligence vs. competitive intelligence vs. business analytics

BI is often considered synonymous with competitive intelligence, and it's also confused with business analytics. However, there are some important distinctions.

Business intelligence: BI increases your competitive advantage. It uses various technologies to review internal data and analyze business processes. BI collects data through asking questions, reporting and online analytical processing.

Competitive intelligence: With a marketplace focus, competitive intelligence uses data differently. Information is gathered, analyzed and disseminated to understand how to get an edge over competitors.

Business analytics: Business analytics is related to competitive intelligence and can be considered a subset of it. Business analytics is all about predictive modeling, and its tools are statistics and quantitative measurements.

Business intelligence vs. artificial intelligence

BI and artificial intelligence (AI) overlap in their technological applications. BI and AI tools create valuable data from their analytics, allowing businesses to translate that data into informed business decisions.

However, their goals are significantly different. BI tools streamline data collection while automating the frequency and improving how a business utilizes the data. By using BI tools in various departments, your team can collect and aggregate that data to make informed business decisions.

On the other hand, AI tools are built to model human intelligence in the form of rational decision-making. For example, in certain instances, an AI chatbot can take the place of customer service reps. While it doesn't necessarily improve the B2C relationship, it can fill the gap in real time for simple questions or concerns.
Tom Drake contributed to the writing and research in this article.

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The term "business intelligence" came into use in the 1960s to describe a system for sharing information among departments. Today, business intelligence (BI) tools enable self-service data management and digital data analysis. BI software solutions efficiently collect data to help owners and managers make informed business decisions.

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Data Analytics Top Ten Trends for 2017  

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Data Analytics Top Ten Trends for 2017  

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Rhonda Schaffer
In this article the author explains that after the poll data got it wrong leading up to the Brexit and the US election that some said data was dead, but that is not the case. In fact, the percentage of companies using BI is predicted to rise, and there will be new technologies coming in 2017. Tools to analyze unstructured   data are going to be getting better because the importance of the data, it holds the answers to why things are happening. The ability to embed analytics into software is the fastest growing area in BI. With Artificial Intelligence gaining popularity and being able to analyze data faster than a human the role of data scientist will change. Data mining will continue to grow in 2017 by targeting shopping behaviors along with the ability to predict the personality of shoppers, with only 30 minutes of online shopping data.  Prescriptive analytics tools are used with predictive analysis, it analyzes the data and puts it into scenarios which, provides decision makers with hypothetical’s of the impact for each scenario.

This article shows how things are continuously evolving and new technologies keep building on ones that we are learning about. Data mining for marketing purposes is going one step further and predicting your personality.  Psychologists have been trying to do this for years and data and algorithms are able to do this with 30 minutes web browsing.

https://channels.theinnovationenterprise.com/articles/data-analytics-top-trends-in-2017

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Data Analytics Top Trends for 2017Rhonda SchafferIn this article the author explains that after the poll data got it wrong leading up to the Brexit and the US election that some said data was dead, but that is not the case. In fact, the percentage of companies using BI is predicted to rise, and there will be new technologies coming in 2017. Tools to analyze unstructured   data are going to be getting better because the importance of the data, it holds the answers to why things are happening. The ability to embed analytics into software is the fastest growing area in BI. With Artificial Intelligence gaining popularity and being able to analyze data faster than a human the role of data scientist will change. Data mining will continue to grow in 2017 by targeting shopping behaviors along with the ability to predict the personality of shoppers, with only 30 minutes of online shopping data.  Prescriptive analytics tools are used with predictive analysis, it analyzes the data and puts it into scenarios which, provides decision makers with hypothetical’s of the impact for each scenario.  This article shows how things are continuously evolving and new technologies keep building on ones that we are learning about. Data mining for marketing purposes is going one step further and predicting your personality.  Psychologists have been trying to do this for years and data and algorithms are able to do this with 30 minutes web browsing. 

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Data Analytics Top Trends for 2017Rhonda SchafferIn this article the author explains that after the poll data got it wrong leading up to the Brexit and the US election that some said data was dead, but that is not the case. In fact, the percentage of companies using BI is predicted to rise, and there will be new technologies coming in 2017. Tools to analyze unstructured   data are going to be getting better because the importance of the data, it holds the answers to why things are happening. The ability to embed analytics into software is the fastest growing area in BI. With Artificial Intelligence gaining popularity and being able to analyze data faster than a human the role of data scientist will change. Data mining will continue to grow in 2017 by targeting shopping behaviors along with the ability to predict the personality of shoppers, with only 30 minutes of online shopping data.  Prescriptive analytics tools are used with predictive analysis, it analyzes the data and puts it into scenarios which, provides decision makers with hypothetical’s of the impact for each scenario.  This article shows how things are continuously evolving and new technologies keep building on ones that we are learning about. Data mining for marketing purposes is going one step further and predicting your personality.  Psychologists have been trying to do this for years and data and algorithms are able to do this with 30 minutes web browsing. 

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Lauren Helwig
The article reviews four (plus one) core usability principles for optimal information visualization. These principles include: 1] Appropriate information hierarchy: display the high level info on the dashboard, and details at subsequent drill downs (a.k.a. bite-snack-meal). 2] Collect and aggregate user feedback from multiple sources (forums, surveys, log, survey forms, etc.). 3] Integrate analytics into product usage. 4] Enable interactive (i.e., drill down). Finally (plus one): make your BI experience intuitive.

Item three, seemingly similar to item two (analytics is just another source of feedback), is actually the thrust of the article: embed feedback into your product so that usability and satisfaction is a core product output. That’s radical. The article focuses on the usability of BI readouts, which is also sub-focus of the course reading. It advocates for embedded product analytics enabling data gathering and analysis about usability and usage patterns (alongside margins, sales, etc.), with the goal of optimizing product performance. “Embedded” product analytics and “intuitive” BIs is a shift from the data gathering and readout focus of the text. “Intuitive” is not a stand-alone design attribute (like color) or downloadable cognitive template. Intuitive is the result of intelligent subtractive and inductive logic; and … intuition.

Author: Eldad Farkash

Source: http://www.uxbooth.com/articles/how-to-make-an-intuitive-data-display/

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Business Analytics: Defining the field and identifying a research agenda

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Business Analytics: Defining the field and identifying a research agenda

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Alexandra Brown
This article discusses the background and nature of business analytics. Business analytics reflects the increasing significance of the data. Commercial organizations can use the business analytics to understand how to use their large amounts of data to create more value in their business. Governments and communities use business analytics to create value of a broader nature by exploiting their data resources. “The current view of analytics is encapsulated by Davenport and Harris’ (2007) succinct and widely adopted definition: “By analytics we mean the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions.” (p. 7, emphasis in the original).” Analytics provide insight into the meaning behind the given data. Business analytics can be viewed as the intersection of a variety of disciplines.

This article builds on my knowledge and understanding of material covered in our course because it goes over how the data we may accumulate in programs such as Microsoft Access is actually used in different types of organizations. It also goes over things such as ethics and governance issues in business analytics, big data and business analytics, organizational issues in business analytics adoption, data quality and business analytics, as well as business analytics and decision support, which can help us in the future.

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European Journal of Operational Research

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Business Analytics: Defining the field and identifying a research agenda Deadlines Submission due date: January 31, 2018. But we start...

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Fishing Equipment Market to Exceed $20.3 Billion by 2015, According to New Report by Global Industry Analysts, Inc.

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Fishing Equipment Market to Exceed $20.3 Billion by 2015, According to New Report by Global Industry Analysts, Inc.

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Keegan Bailey
Despite being written in 2009, this article is still relevant, since it provides trends and statistics characteristic of the fishing tackle market, itself. I have been an avid angler for as long as I can remember, so this article appealed to me in that sense. The article states that the market for fishing tackle was expected to experience what is described as "healthy growth", which PRWEB chocks-up to developing markets in Asia and other parts of world. This makes sense, as individuals in countries with developing economies are much like America post-WWII. People, for the first time, gain disposable income, which they can then invest in enjoyable pastimes, such as recreational fishing. The article also reports that the fishing equipment people purchase is swayed heavily by "brand loyalty". This is something that I have found to be true in my own fishing-related buying habits, as I don't tend to step out of my comfort-zone to a great extent. The article then goes on to say that each major company has developed strategies to promote their products to specific audiences. This utilization of web-based marketing is something that is deeply related to information systems, as companies must form databases in order to understand the desires of their target population. In my personal experience, I have seen online advertisements for tackle companies that specifically target me due to my Google searches. Judging by the amount of fishing tackle that I have purchased over the last year, their tactics are undoubtedly working.

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Fishing Equipment: A Global Strategic Business Report

Global fishing equipment market is projected to exceed US$20.3 billion by 2015. Europe represents the largest fishing equipment market. Asia-Pacific represents the fastest growing fishing equipment market over the period 2006-2015.

Fishing rods, reels & poles market represents the largest product segment, while Fishing lures, flies & baits market represents the fastest growing segment over the period 2006-2015.

With fishing being a favorite activity, fishing equipment market is registering healthy growth. Increasing disposable income and rapidly growing emerging markets, especially within Asia-Pacific, have largely stimulated spending culture in the global retail markets, contributing to the constant surge in fishing equipment and other leisure products markets. Further, increasing expenditure by millions of anglers across the globe and special taxes collected on fishing tackle and motorboat fuel are significantly contributing in supporting fish recreational programs and fisheries conservation.

Development of new advanced fishing equipment, brand loyalty and consolidation activity among the players represent the key trends governing the global fishing equipment market. To address the surging demand for fishing equipment, manufacturers are developing new gears and long lines with several hooks attached to the systems for catching fish. Fishing equipment market is largely characterized by brand loyalty. With majority of anglers who fish use specific brands, value of the premium quality fishing tackle brands is surging phenomenally. Further, the "catch and release" trend among the anglers has largely plummeted the use of live bait, which in turn has increased the demand for premium quality lures.

Fishing equipment is a highly fragmented market, with several players vying to attain the larger market pie. Players are adopting several strategies to reinforce their strategic positions. Strategic acquisitions represent one of the key strategies adopted by the players to enhance their positions. The consolidation activity enables players to increase their product portfolios, pursue co-branding or cross-selling in foreign businesses and enter new geographic regions. Further, acquisitions also aid companies to expand their distribution channels to market their products.

These and other market data and trends are presented in "Fishing Equipment: A Global Strategic Business Report" published by Global Industry Analysts, Inc. This GIA report discusses the prevailing trends, issues, demand forecasts, and activities that affect the industry. The fishing equipment market is analyzed by the following geographic regions - US, Canada, Japan, Europe, Asia-Pacific, and Latin America; and product segments -Rods, Reels & Poles; Lures, Flies & Baits; Fishing Lines; Hooks and Other Fishing Equipment. Analytics for the period 2006-2015 provide a comprehensive understanding of the market.

The global marketplace is characterized by participants such as AFTCO Mfg. Co, Bass Pro Shops, Berkley, Cabela's, Castaic Soft Bait, Daiwa Corporation, Fenwick, Gamakatsu, Gibbs/Nortac Fishing, Hagen's, Hedron, Jarden Corporation, Pure Fishing, O. Mustad & Son, Pflueger Fishing Tackle, Rome Specialty Company, Rapala VMC Corporation, Sensas SA, Shimano, and Tibor Reel Corporation, among several others.

For more details about this research report, please visit http://www.strategyr.com/Fishing_Equipment_Market_Report.asp

About Global Industry Analysts, Inc.
Global Industry Analysts, Inc., (GIA) is a reputed publisher of off-the-shelf market research. Founded in 1987, the company is globally recognized as one of the world's largest market research publishers. The company employs more than 700 people worldwide and publishes more than 880 full-scale research reports each year. Additionally, the company also offers a range of more than 60,000 smaller research products including company reports, market trend reports and industry reports encompassing all major industries worldwide.

Global Industry Analysts, Inc.
Telephone 408-528-9966
Fax 408-528-9977
Email press (at) StrategyR (dot) com
Web Site http://www.StrategyR.com

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Fishing Equipment Market to Exceed $20.3 Billion by 2015, According to New Report by Global Industry Analysts, Inc.

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Surging demand from the emerging markets of Asia-Pacific and Latin America is propelling growth in the global fishing equipment market. With increasing number of people adopting fishing as a leisure activity, the global market is expected to register steady growth.

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Five Ways Data Analytics Will Shape Business, Sports And Politics In 2016

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Five Ways Data Analytics Will Shape Business, Sports And Politics In 2016

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Shanna Morse
Although this article was written in 2016, I still find it relevant and enjoyed comparing the author's predictions to what has occurred over the course of the past year. Especially how he tied in the way data analysis will impact the outcome of the presidential election. It also helped me to visualize the many ways in which data analysis can contribute in ways I hadn’t realized in the past. This article, written by George Mathew begins by him explaining how recently there has been an increase in the significance of data analytics throughout all organizations. From consumer goods companies and professional sports franchises to political consultancies, medical research institutions, and financial firms. He also acknowledges the data analysts that turn organizations data into useful information that is used in decision making. He then lists and summarizes his predictions for the five main ways business analytics will “shape the business, sports and political landscape” in the years to come.

Prediction #1: The hottest corporate job of 2016? Data analyst :“The combination of growing demand and supply in the market for data analysts will make it by far the hottest corporate job in 2016.”

Prediction #2: IT will embrace the self-service analytics movement:“By providing business users with leading-edge analytics tools that allow them to create sophisticated analytics without requiring programming assistance from data scientists—and without waiting for data from IT.

Prediction #3: Predictive analytics goes mainstream: “When you combine the number of new college graduates who want to use the newest, cutting-edge tools without being computer programmers with the real-life business experience of existing data analysts, this is the year that predictive analytics will become accessible to everyone.”

Prediction #4: The “Internet of Things” will get more organizations interested in geospatial analytics:“We’ll see more beacons placed in stores to detect when people enter and where they spend their time to enable better merchandising and personalization.”

Prediction #5: Analytics will win the Gold Medal-and the US Presidency
“2016 will be the year that sports analytics really hits the mainstream in its use for performance optimization by individual athletes – and nowhere will this be more evident than in the 2016 Summer Olympics.” Regarding the presidential election, he states that the winner of the election may very likely turn out to be the one who has the best analysts on his or her team.”

This builds on my knowledge of business analytics by acknowledging real world examples of the many ways in which using data can be beneficial and advantageous for varying groups of people/organizations.

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Over the past few years, we've seen a steady rise in the importance of data analytics, in organizations as varied as consumer goods companies, professional sports franchises, political consultancies, medical research institutions, and financial firms. At the steering wheel of many of the analytics these organizations perform is not necessarily the ever-elusive data scientist but often a data analyst who understands his or her business and department in addition to the data it uses to drive decisions. In fact, the US Bureau of Labor Statistics reports that the job market for various data analyst disciplines is growing at 27% annually-far exceeding the national job growth average, which is a mere 11%. Many factors are lining up to make 2016 the "Year of the Data Analyst."

Here are five predictions of how analytics will shape the business, sports and political landscape in 2016:
Prediction #1: The hottest corporate job of 2016? Data analyst.
Demand for data analysts has never been higher-and 2016 will continue to see demand rise as more organizations look to deal with the explosion of data and the opportunity presented by having better analytics.

For start-ups to multinational corporations looking to hire qualified data analysts, the great news is that there are plenty of them. According to the US Bureau of Labor Statistics, between 4 and 6 million people are employed as data analysts. What's more, the pipeline for the future is bright: Universities are pumping out more graduates skilled in data analytics than ever before. Specialized degree programs in analytics are cropping up all over and courses in analytics are now not only standard fare for computer science students, but also business and economics students as well. The growth in this already abundant supply of data analysts is not particularly surprising, as the role offers an intellectually fulfilling and lucrative (salaries for business analysts start around $75K and climb rapidly from there) career path.

The combination of growing demand and supply in the market for data analysts will make it by far the hottest corporate job in 2016.
Prediction #2: IT will embrace the self-service analytics movement.
The increasing demand for data analytics has placed unprecedented pressures on IT to provide the infrastructure and tools that enable users across the business to get the answers they need. And the users will not only include individuals with a data analyst title, but also those in the organization who work with data every day - in marketing to operations to finance to HR - users who you can think of as empowered data workers.

In 2016, IT will embrace the concept of self-service analytics and relish the important role they can play in enabling business users with access to critical data. By providing business users with leading-edge analytics tools that allow them to create sophisticated analytics without requiring programming assistance from data scientists-and without waiting for data from IT-companies will find themselves on new competitive footing and ready to uncover previously hidden trends that catapult them into leadership positions.
Prediction #3: Predictive analytics goes mainstream.
Not only has the emergence of big data increased interest in predictive analytics, it has also significantly broadened the market for this kind of advanced analysis, since every function in an organization can see the value in investing in predictive analytics to improve business performance. Until now, though, actually running predictive analytics required hiring expensive data scientists with Ph.D. degrees, but with the demand for predictive far outpacing the supply of data scientists, we think that's all going to change in 2016. When you combine the number of new college graduates who want to use the newest, cutting-edge tools without being computer programmers with the real-life business experience of existing data analysts, this is the year that predictive analytics will become accessible to everyone.

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Five Ways Data Analytics Will Shape Business, Sports And Politics In 2016

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Photo: PRNewsfoto Over the past few years, we've seen a steady rise in the importance of data analytics, in organizations as varied as consumer goods companies, professional sports franchises, political consultancies, medical research institutions, and financial firms. At the steering wheel of many of the analytics these organizations perform is not [...]

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Which analytics tools are business ready now?

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Which analytics tools are business ready now?

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Michelle Harrigan
http://www.information-age.com/analytics-tools-business-ready-123466355/
This article opens by stating that "Business analytics is a core component of the digital transformation, and collecting large amounts of data is a big trend right now" It goes on to discuss the importance of Business Analytics in helping inform decision makers to improve business outcomes. Currently, many businesses have all of their data segmented into separate systems and analytical tools, which means their data can't be combined. The article goes on to discuss how Dashboards can solve this problem by consolidating segmented data and offering a visual display of the data. Dashboards have the ability to take large amounts of data and convert them to reports that are easy to understand. These reports can be viewed on a computer, smartphone or tablet. This business analytics tool can provide businesses with a competitive advantage.

I chose this article because of the amount of key terms that I recognized from chapter 5. It was as if the article was written specifically for what we are currently studying. Seeing terms like BI, Dashboards, KPI's and big data made the article feel familiar and it was a good way to relate what we are studying in chapter 5 to real world examples of how businesses can use business analytics tools.

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Emily Watson
"Enhancing product value for total customer satisfaction is every company’s end goal. Target markets of different industries are dynamic with their needs often changing according to society’s standards, issues, and fads. Knowing this, it is necessary to pull ahead of the competition through unique innovation of ideas and products which would readily attract customers."
The quote above is how Benjamin Goewey starts his article about the 5 benefits of Business Analytics. He goes on to say that the first benefit is that Analytics helps you measure how much of your mission statement is accomplished. He goes on to say that a good business has it's own mission statement. Their mission statement is a set of values to their consumers as a marketing plan or a basis of checking in on their own development. He says that many businesses retain or promote employees using their values in their mission statement as guidelines. The next benefit is that analytics encourages smart decision-making. Companies can maximize the use of analytics when they share discussion to as many employees as needed. A group is usually able to analyze data better and reach objective and informed decisions compared to a single person. The third benefit is that Analytics provides clearer insights through data visualization. They care about how you present your data to your analytics team. Visualization or graphs, charts can be used to make sure that decision-making is more interesting. The fourth benefit is that Analytics keep you updated. Changes in the industry can occur at a very rapid pace. It is not unusual to see larger companies being devoured by promising start ups.
Lastly, Analytics offer efficiency. Analytics encourages a company culture or efficiency and teamwork where employees are able to express their insights and share in the decision-making process. It also provides companies with better choices like where to take the business as well as determining the steps needed to achieve new goals.

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Datamensional - BI Consulting, Data Management

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Enhancing product value for total customer satisfaction is every company's end goal. Target markets of different industries are dynamic with their needs often changing according to society's standards, issues, and fads. Knowing this, it is necessary to pull ahead of the competition through unique innovation of ideas and products which would readily attract customers.

Before business analytics became the version we know today, businessmen had to make do with error-ridden analytic models which also potentially damaged their plans. Since there was no constructive way to initialize organized data extraction, earlier versions of analytics did not work well for its first generation of users. Traditional analytics teams had to dig hard and deep whenever they tried to gather information about their consumers.

The data gathered is vital in statistical analysis, which in turn is essential for decision making. Decision making is the critical process which makes or breaks a company's goals. A slight mistake or an overlooked factor can delay a decision and may even put the business plan to a halt.

But how does analytics really work? What benefits can your company gain from it?
Analytics helps you measure how much of your mission statement is accomplished
A good business has its own missions statement, which is a set of values presented to their consumers either as a marketing plan or as basis of checking in on their own development. Many businesses retain or promote employees using the values in their mission statements as guidelines. Although this is helpful in determining who helps your company succeed, it isn't strategic enough to leave it at that. Values must also be quantified and expressed in a tangible way such as generating more profit for the company.

Quantified values can help the business improve their analytical process because it defines a common goal that should be followed by everyone involved in the business. When these values are quantified, they will be evaluated by the employees in order to gain a clearer view of what is expected from them. The more informed they are, the more productive they will become.
Analytics Encourages Smart Decision-Making
Accessibility to important data gives companies the power to make accurate decisions that could leverage businesses. Not only does it provide useful data, it also allows companies to make decisions faster and more efficiently than before.

Companies can maximize the use of analytics when they share the discussion to as many employees as needed. Ever heard of the saying "two heads are better than one?" A group is usually able to analyze data better and reach objective and informed decisions compared to just one person.
Analytics Provides Clearer Insights Through Data Visualization
Recent versions of analytics care about how you present your data to your analytics team. Comprehensive charts and graphs can be used to make sure that decision-making is more interesting. Through visual representations of extracted data, relevant and useful insights can be extracted in a much clearer way.

With analytics' data visualization, information that you need about your market is there on your table, presented in a visually appealing and organized manner.
Analytics Keep You Updated
Modern consumers change their mind easily as fads come and go, and they are easily swayed by "better" offers. Analytics can give you insight about how your target market thinks and acts. You will be prompted to be dynamic at all times to serve the needs of your ever-changing consumers.

Changes in the industry can occur at a very rapid pace. It is not unusual to see larger companies being devoured by promising start-ups. Protect your business from unpredictability with analytics so that you may be able to innovate and pre-empt your products according to your consumer's needs and preferences.
Analytics Offer Efficiency
Efficiency for businesses has been improving since the advent of business analytics. With the ability to gather a large amount of data at a fast rate and present it in a visually appealing way, companies can now formulate decisions to help achieve specified goals. Analytics encourages a company culture of efficiency and teamwork where employees are able to express their insights and share in the decision-making process.

Analytics also provides companies with better choices on such matters like where to take the business as well as determining the steps needed to achieve new goals.

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5 Benefits of Using Business Analytics - Datamensional - BI Consulting, Data Management

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Enhancing product value for total customer satisfaction is every company's end goal. Target markets of different industries are dynamic with their needs often changing according to society's standards, issues, and fads. Knowing this, it is necessary to pull ahead of the competition through unique innovation of ideas and products which would readily attract customers.

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3 Things Business Owners Need to Know About BI

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3 Things Business Owners Need to Know About BI

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Wendy Keezer
"Information is the oil of the 21st century, and analytics is the combustion engine" Peter Sondergaard of Gartner Research.  This quote from the article I feel sums up today's world perfectly, which is what drew me to this article in particular.  The article goes on to explain the advantages of how BI changes the decision making for small companies, if they do it well.  They focused on three major trends for businesses; Self-service BI for small business; Data for visualization, and Security for data.
These three trends are related to what we have been learning, though we haven't gotten to security yet.  Self-servie BI for small business, big data is now accessible for smaller companies. Governments are utilizing it now as well. I work for the State of Michigan, Governor Snyder has implemented a dashboard for civil service employees that has been very useful.  The second mentioned, data for visualization immediately made me think of USAFacts.org, this is a perfect example of numbers to information, to understanding.

https://www.entrepreneur.com/article/289500

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How Does Business Analytics Provide a Competitive Edge? 

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How Does Business Analytics Provide a Competitive Edge? 

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Erica Noye
https://channels.theinnovationenterprise.com/articles/how-does-business-analytics-provide-a-competitive-edge
I never realized that there was such a difference between business intelligence (BI) and business analytics until I chose to read the article “How Does Business Analytics Provide a Competitive Edge?” A good summary of the difference in business intelligence is stored information and business analytics produces new information. The article goes on to explain that in order for an organization to stay competitive, their management team must be analytically prepared. This article, along with a few others that I read, argued that analytics are the next big thing for organizations to have in order to successfully compete with the others.

As this article stated in the very beginning, business analytics used to be “nice-to-have,” but now they are considered to be a necessity. Like we have already learned, the more you know the easier and more accurate your work will be. The more data a person knows, the easier it is to predict the future outcomes of a business. It helps to put you one step ahead of the game.

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'Microsoft Widens Business Intelligence Approach -- Is Everyone A Business Analyst Now?'

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'Microsoft Widens Business Intelligence Approach -- Is Everyone A Business Analyst Now?'

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Dante Robinson

https://www.forbes.com/sites/adrianbridgwater/2017/06/13/microsoft-widens-business-intelligence-approach-is-everyone-a-business-analyst-now/#208ab64c32ec

The smartphone, tablet, and portable PC has changed literally every industry imaginable. Anyone can look up a recipe on Pinterest and become a faux master chef, Mechanics can youtube a complex part installation to ensure a better experience for their customers, and landscaping companies can use apps to track mileage to increase profitability. With that said, business intelligence is no exception. Thanks to incredibly easy access to software applications, anyone in any field of the business has access to business intelligence. Anyone from the janitor to the CEO can pull up data about profitability and future trends, considering they have authorization. Microsoft has sped up this evolution of BI with their Power BI software. It can be used on tablets, smartphones, and computers.
This article took what we learned in Chapter 5 and applied it to real life scenarios. Any discussion of software or programs being used in business has changed in the past few years. Before, only high level employees in suits had access to this sort of technology. They'd lock themselves in a room with dedicated analysts and make decisions for the business. Now, anyone can access the same information and get in on discussions that affect the entire company. This helps in my opinion. Hopefully, this leads to more open minded discussions about business intelligence and possibly creates new ideas that the entire industry takes on.

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The 4 Key Benefits of Business Analytics

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The 4 Key Benefits of Business Analytics

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Kathy Scherdt
https://www.techopedia.com/2/30429/it-business/business-intelligence-bi/the-4-key-benefits-of-business-analytics

Analytics equals Agility. What is mean is meant by this statement? This article that I found talks about how by using analytics a company can make better business decisions. With more information found at the fingertips of the company, the company can make decisions quicker and smarter. Analytics is a process of finding patterns in many parts in a business, both internal and external. Companies have been using error-ridden ways to collect data and then sift through the data to get what they think is good data. With analytics, the sorting part is done by analytics and you get the data you want at a moment’s notice. Analytics can do 4 key things for a company: first, it can help quantify company values, to ingrain smart decision-making into the company culture, it provides greater, faster insight through data visualization, and helps the company avoid being left behind.

What I took away from this article is that there is software out there to help not just upper management, but help the worker bee make better decisions. I think that in order to improve processes to make the company money or save them money. With analytics, a company can stay one foot ahead of others. Companies that are smart would be using this all the time.

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3 Things Are Holding Back Your Analytics

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3 Things Are Holding Back Your Analytics

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Andrew Lovely
In this article written by Harvard Business Review (HBR), they talk about things that companies are doing wrong and what is holding them back (outside of technology).  The Three main things that companies are doing wrong are…

Structure: Business Analytic departments are typically one of two extremes. Either they are too independent of business and produce very complex models with very few insights with what may happen.  Or, They are too buried into the business aspects and they tend to be biased and present biased information.

Culture: Sometimes, there are companies who are too focused on what flawed models say. They will sometimes ignore common sense and blindly follow data which can lead to problems for the company.

Methodology:  Similar to structure, methodology/models fall into two extremes as well. With one, they are overly complex models with very limited adaptability for changing inputs. While the other is so overly simplified that it fails to solve the problem it was designed for.

Leaning more about Business Intelligence and Analytics with this article really helps me connect what we have learned with real world experiences. It’s good to have detailed data and graphs, however there should always be a line with this and you should always try to gage how detailed you need to be. Just because the graphs say one thing doesn’t mean it is the only thing to follow. You should always use common sense and general business knowledge along with data to make decisions.

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Executive Summary

Many firms continue to struggle with business analytics. This has nothing to do with technology. We've found three main obstacles to realizing analytics' full value: the organization's structure, culture and approach to problem solving. Structurally, analytics departments can range between two opposite but equally challenging extremes. On the one hand are data science groups that are too independent of the business. These tend to produce impressive and complex models that prove few actionable insights. On the other hand, analysts who are too deeply embedded in business functions tend to be biased toward the status quo or leadership's thinking. Culturally, organizations that are too data-driven (yes, they exist) will blindly follow the implications of flawed models even if they defy common sense or run counter to business goals. Alternatively, organizations that rely too heavily on gut instinct resist adjusting their assumptions even when the data clearly indicates that those assumptions are wrong. The dichotomy continues when it comes to methodology. At one extreme, we see analytics groups that create overly complex models with long lead times and limited adaptability to changing inputs. On the other side of the coin, some teams create models that are too simplistic and fail to capture the nuances of the problems they're trying to solve.

During the past decade, business analytics platforms have evolved from supporting IT and finance functions to enabling business users across the enterprise. But many firms find themselves struggling to take advantage of its promise. We've found three main obstacles to realizing analytics' full value, and all of them are related to people, not technology: the organization's structure, culture, and approach to problem solving.

Structure

Structurally, analytics departments can range between two opposite but equally challenging extremes. On the one hand are data science groups that are too independent of the business. These tend to produce impressive and complex models that prove few actionable insights.

Consider the experience of one retail financial services firm. There, the analytics function was comprised of employees who used specialized software packages exclusively and specified complicated functional forms whenever possible. At the same time, the group eschewed traditional business norms such as checking in with clients, presenting results graphically, explaining analytic results in the context of the business, and connecting complex findings to conventional wisdom. The result was an isolated department that business partners viewed as unresponsive, unreliable, and not to be trusted with critical initiatives.

On the other hand, analysts who are too deeply embedded in business functions tend to be biased toward the status quo or leadership's thinking. At a leading rental car agency, for instance, we watched fleet team analysts present intelligence purportedly showing that the fleet should skew toward newer cars. Lower maintenance costs more than compensated for the higher depreciation costs, they said. This aligned with the fleet vice president's preference for a younger fleet.

But it turned out that the analysts had selected a biased sample of older cars with higher-than-average maintenance costs among cars of the same age. An analysis of an unbiased sample (or the entire population) would have yielded a different result. (Of course there might have been other motivations to keep a younger fleet-customer satisfaction and brand perception, to name two-but cost reduction was not one of them.)

Culture

Culturally, organizations that are too data-driven (yes, they exist) will blindly follow the implications of flawed models even if they defy common sense or run counter to business goals. That's what happened at a financial services firm where management was mulling a change to its commission structure. They wanted to switch the basis of its salesforce compensation from raw results to performance relative to the potential of each salesperson's market.

In response, analysts developed an admirable data envelopment model. The model simultaneously compared sales of different types of products with local demographic and financial statistics to come up with a single efficiency measure for each salesperson relative to their peers. Indeed, this seemed to have made compensation more equitable. But it reduced the compensation of salespeople who were less efficient but ultimately more valuable-causing them to defect to competitors.

Alternatively, organizations that rely too heavily on gut instinct resist adjusting their assumptions even when the data clearly indicates that those assumptions are wrong. The aforementioned rental car agency, for example, was extremely reluctant to change course even after discovering that the data didn't support their cost reduction claims.

Methodology

The dichotomy continues when it comes to methodology. At one extreme, we see analytics groups that create overly complex models with long lead times and limited adaptability to changing inputs.

An example of this was the data science team, made up of repurposed actuaries, at a national auto insurance company. This team created a highly impressive model to predict whether a car would be a total or partial loss after the first accident report. The ensemble model consisted of a random forest, a principal component analysis, and a Bayes classifier.

Unfortunately, the company lacked the infrastructure to directly implement the trained model in a production setting. The model was also too complex for the IT team to reproduce. As it turned out, a simple logistic regression was almost as effective.

Then again, some teams create models that are too simplistic and fail to capture the nuances of the problems they're trying to solve. A large fashion retailer ran into this issue. The company noticed that certain promotions were highly correlated with increased foot traffic. They failed to realize that they tended to run those promotions on Fridays and Saturdays, when foot traffic was substantially higher than other days of the week to begin with.

Elements of an Effective Analytics Organization

In light of these obstacle, we believe an effective business analytics organization balances functional knowledge, business instinct, and data analysis, with an operating philosophy to add complexity only when the additional insights justify it. This kind of organization includes:

An analytics nerve center. Ideally, a small team of independent, highly skilled data scientists-typically with advanced degrees in statistics, mathematics, computer science and the like-serves as the analytics nerve center of an organization. Also, reporting into this nerve center are analytics generalists embedded in each major business function.

In this setup, the embedded generalists gain the deep functional know-how they need to initiate and develop actionable analyses. They rely on the nerve center for additional support, model validation, and training. Knowing the languages of analytics and business, the embedded generalists also serve as liaisons between the independent data scientists and the business partners in their functions.

Representation at the top. Heading up the nerve center? A chief analytics officer who brings the voice of analytics straight to the C-suite, where instinct tends to rule. When analytics and instinct come together, a strategy becomes more powerful. When the two disagree, research can reveal whether it's the model or the business assumption that's flawed.

A champion-challenger approach. To manage the complexity-insight tradeoff in this ideal structure, analysts initially focus on creating a type of minimum viable product, or MVP. Here, an MVP is a model that solves the problem to a minimum acceptable level as simply as possible. This MVP becomes the "champion" and touches off challenges to add complexity and unseat it. A challenger can replace the champion, but only if all stakeholders agree that its benefits are worth the increased complexity. An interesting byproduct of this approach is that it nearly always produces something usable, even if the process is interrupted.

Modern business analytics has made it possible to extract new types of insights from vast volumes of data. The result is that analytics has become crucial to any large organization's ability to make decisions. Getting this capability right means creating an analytics organization with the structure, culture, and problem-solving methodology to reveal the actionable insights that business leaders need to compete.

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3 Things Are Holding Back Your Analytics, and Technology Isn't One of Them

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Executive Summary Many firms continue to struggle with business analytics. This has nothing to do with technology. We've found three main obstacles to realizing analytics' full value: the organization's structure, culture and approach to problem solving. Structurally, analytics departments can range between two opposite but equally challenging extremes.

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Dental Practice Turnaround Begins with A Business Analysis

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Dental Practice Turnaround Begins with A Business Analysis

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-Brook Andrews

I have been in the dental field for thirteen years so I wanted to find an article that brought together my current field and this course.  I found this interesting article about business analytics in dentistry.  It discussed the key points that an office should focus on when determining the decline in the practice.  Things like the percentage of no shows (which should be below 1%) or hygiene production and how it should be 25% of total production.  Keeping the analysis to only a few points is best.  There is also a link for a company that specializes in dental analytics called “Levin Group In-Office Practice Analysis”

Just finding an article that is about my current field that applied to this class was very eye opening.  By doing a business analysis within the practice it will create easy access to information and enable the ease of sharing the information as well.  That way everyone in the office has a better understanding of what direction the business/office wants to go and the reasoning behind it.  As well as reducing waste in the system like were learned in this section.  Knowing where the waste is in the office will inevitably reduce waste in many areas of the business.

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Dental practice turnarounds begin with a business analysis

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Outside analysis can work wonders for a dental practice's success

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Casandra Whitmore
This article highlights how information is an asset but requires understanding to be used as a competitive advantage. The article state “The value of analytics lies in its ability to deliver better outcomes” The article points out 4 big mistakes and how to avoid them.

Mistake No. 1: If you can’t define the value, how do you know what it’s worth?

The Solution: Make sure you define the value.

You can do this by including right measures of return, profiling both tangible and intangible return and considering a rage of benefits.

Mistake No. 2: If people can’t understand the value, why should they care?

The Solution: Make sure you communicate the value.
The article points out the four most common perspectives are analytical, process, personal and strategic.

Mistake No. 3: If you don’t know where you’re going, how are you going to get there?

The Solution: Make sure you deliver the value.
Be sure to have a clear roadmap of how to use the information to create a competitive advantage.

Mistake No. 4: If you can’t prove what you’ve delivered, why should people trust you again?
The Solution: Make sure you measure the value.

Show how the data is captured, minimize the overhead of imposed on the team, be comparable across projects. The article puts it nicely “The biggest value of a measurement framework lies in its ability to benchmark highly varied initiatives”

This article was a great read, very informative. I agree with the statement “The people who know how to manage this data deluge are our future”

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Headlines

Most organizations have a relatively immature understanding of what "business analytics" is, let alone how it creates value.

By Evan Stubbs

The intersection of statistically based insight and the realization that information can be an asset has had and will continue to have serious reverberations in the business world. Being smarter has always meant being successful; as far back as the 19th century, analytics was already generating competitive advantage.

Success requires more than just knowledge of statistics or ways of dealing with "big data." Execution is essential, but without a plan and commitment, little happens. Success also requires an understanding of how analytics translates to competitive advantage.

Possibly the best-guarded secret in business analytics is that, in practice, its success comes down not only to organizational culture but also to the ability of managers to successfully sell the value of analytics. As researchers such as Thomas Davenport and Jeanne Harris have rightly pointed out, overall success can often be linked to a variety of factors including organizational structure, management commitment and successful strategic planning. However, it's often "where the rubber hits the road" that the greatest impact can occur.

Analytics is a multi-disciplinary activity: the value from insight comes not from the activity but from the execution. Often, this crosses a variety of departments within an organization - few analytics groups have responsibility for both the insight creation and the execution of that insight. Because of this, selling the value of analytics isn't just a goal for managers; it's a necessary criterion for success.

For many managers, this can be challenging. Despite broad interest in business analytics as a discipline, most organizations have a relatively immature understanding of what "business analytics" is, let alone how it creates value. When projects fail, it's all too easy to point to the organization as the reason why success wasn't achieved. Unfortunately, this is only half the picture - the harsh reality is that we, as managers and analysts, all too often carry a large portion of the blame. In an ongoing straw poll held by the author across more than 1,500 people, only a handful ever feel that they could quantify the value they were creating through applying business analytics. When even the experts can't explain why analytics is important to the organization, what hope does a layperson have?

The value of analytics lies in its ability to deliver better outcomes. And, when it comes to selling the value of business analytics, four mistakes stand out above all others as the biggest blockers of success.

Picture this: You're the person who controls the purse strings for your company. In front of you is a long list of investment opportunities, all of which have very logical and persuasive reasoning behind why they're important. Unfortunately, you only have so much money and resources to allocate. So, how do you prioritize?

Usually, it involves looking at the ratio of economic return to investment, as well as who's supporting the initiative. Unfortunately, far too often business analytics champions fail to capitalize on these as effectively as they might. Instead, they try to gain funding through little more than smart ideas and good intentions.

This lack of value definition may seem paradoxical given the advanced mathematical abilities of most teams - after all, these are the teams that more often than not have the best modeling abilities in the entire organization! Typically though, it's simple oversight combined with a lack of financial domain knowledge that leads to this pitfall. While it's a given that business analytics has the capacity to create value, it's hard to convince everyone else of that fact without being able to define this value. And, it's often after the fact that the team realizes how important this value definition really is.

Business analytics creates value. However, the only way to communicate that value is to quantify it. Defining the value needn't be a complex process. Some of the most important things to consider include:

* Identifying the right measures of return

* Profiling both tangible and intangible return

* Considering a range of benefits

Return can be viewed from a variety of different perspectives. Sometimes, the time needed for an investment to pay for itself is the most critical measure. At other times, the leverage created from an investment is the most important measure. Irrespective of what the appropriate measures are, every business analytics initiative should generate returns in some form, regardless of whether those returns come from cost efficiencies, margin improvements or sheer revenue growth. Capturing and defining these returns in an appropriate manner is often a pre-requisite to even being considered for funding.

Profiling both tangible and intangible returns is also important. While investment decisions often require that the project create economic returns, political support often comes from the intangible returns of a given project. Simplifying process complexity in itself may not provide enough cost reduction to justify investment. However, if it helps the process owner get home in time for dinner two nights a week, their support is almost guaranteed. Given the importance of cross-departmental teams in most business analytics projects, the leverage this support can provide can't be understated.

Finally, good business analytics initiatives typically deliver a number of benefits. One of the biggest advantages of business analytics comes from the ability of an organization to re-apply existing competencies across multiple business problems. Modeling skills can be applied to support improved customer retention, increased revenue through cross-sell and improved returns through better debt collection. A well-designed business case identifies each of these, recognizing business analytics as the broader growth driver it is.

It's fairly obvious that we prefer things we can understand. Unfortunately, it's easy to forget that everyone views things differently. While our perspective may be the most familiar, it may not be the same as the one held by the person we're talking to. All too often analysts will get caught up in the technical detail, explaining the intricacies of one algorithm over another when all their audience is interested in is, "What's the benefit?"

Communication breakdowns can be one of the most frustrating things in any business analytics project. A typical project cuts across multiple groups within the business, often spanning IT, the analytics team and the operational execution team. It's almost inevitable that miscommunications will occur. If these aren't recognized and planned for, it's usually the project (and the analyst's sanity) that suffers.

The best business analytics champions are more than just domain experts; they're also evangelists. A key skill in their kitbag is their ability to understand their audience and tailor their message to suit. Making sure their message is relevant is a critical first step. Equally as important is recognizing that people view things differently and that an effective communication strategy requires working to different perspectives.

Four of the most common perspectives are:

* The analytical perspective

* The process perspective

* The personal perspective

* The strategic perspective

Most analysts are familiar with the analytical perspective. Usually, it focuses on how the initiative will work, often describing the quantitative value that will be created. It's the most evaluative approach and normally revolves around establishing logical relationships and applying rational persuasion. For champions interested in communicating in this way, it's often useful to use formal presentations and other structured methods.

The process perspective usually focuses on what needs to be executed. It tends to emphasize the steps that will be taken, the governance that will be needed and the responsibilities that will go along with the new approach. Quality, execution time and the implied change are often of key interest. Much like the analytical perspective, champions interested in communicating this way often leverage formal presentations and other structured methods.

The personal perspective focuses on how the initiative will impact individuals within the organization. Of key interest is often how the initiative will make people better off, regardless of whether it's through personal or professional benefit. Intangible value is usually emphasized along with how existing teams will extend their responsibilities. Champions interested in communicating this way often prefer more informal methods.

The strategic perspective focuses on the holistic impact of the initiative. It's the most "big-picture" approach, usually focusing on competencies, competitive advantage, and overall systems. Often, a key focus is on how the initiative will make the organization better off supported by evidence of best-practice, linkages to strategic objectives and reference to market influences. Champions interested in communicating this way often prefer a highly interactive approach, usually involving white-boards and open conversation.

In addition to evangelism, effective business analytics champions avoid the pitfalls of miscommunication by weaving these different perspectives into a holistic communication strategy, tailoring it to suit their audience. By communicating the value of business analytics in a way their audience can relate to, they gain traction and build support. Business analytics isn't about statistics; it's about delivering organizational change.

Business analytics is a double-edged sword. On one hand, it has almost unlimited potential for re-use across multiple business problems. On the other, there's so much it can do that it's easy to take on more than can ever be delivered. Achieving the balance between meeting strategic goals and delivering tactical returns is challenging. Too much of one and the team runs the risk of being perceived as a group that's always "almost there." Too much of the other and the team runs the risk of being seen as a fire-fighting unit that's never in charge of their own destiny.

Few teams effectively link their tactical activity to larger growth opportunities. This often leads them down a path of operational firefighting and constrained investment, often because they can't demonstrate a clearly defined growth path. If left unchecked, the team eventually suffers rising rates of churn because of general frustration and a heavily constrained budget.

For a business analytics team, the pinnacle of success comes from not only delivering real economic value but also creating and leveraging sustainable competitive advantage. Key to this is understanding how tactical activity maps into strategic outcomes. Increasing the odds of success is relatively simple. For many teams, it involves:

* Establishing direction through creating a roadmap

* Freeing resources through leveraging tactical revolutions

For many teams, the end-goal is the creation of a sustainable competitive advantage for their organization. Common examples include the ability to respond to market conditions faster than their competitors or achieving the highest level of customer satisfaction through one-to-one engagement. Effective use of business analytics helps the organization create a differentiated approach, one that's often hard to replicate. However, these competitive advantages must be nurtured; if they were easy, they wouldn't be a sustainable competitive advantage.

Few organizations, however, can afford to have their analytics team spending a number of years working on creating this advantage without any immediate economic return! For many teams, success comes from understanding the interdependencies between enabling initiatives and growth initiatives, as well as how these map into competitive advantage in the longer-term. Ideally, these are then be mapped into a roadmap that balances short-term value creation with longer-term competitive advantage.

While the roadmap is critical in defining direction, most established teams struggle to get investment based on their roadmap. Usually, their growth needs to be organic with incremental investment based on historical returns, not future. Normally, the key challenge for these teams is figuring out how to do more work without any additional investment; without demonstrated returns, they can't get more funding. However, the perception is often that without additional funding, they don't have sufficient resource to generate greater returns!

One approach that can successfully overcome this Catch-22 is through leveraging incremental efficiency improvements to improve productivity. In sufficient number, these productivity improvements can be re-invested in growth initiatives, delivering economic returns and justifying further investment. Often, these improvements stem from more effective data management processes, streamlined model development or improved operational deployment of models.

These tactical revolutions help the team free up enough resource to start creating incremental economic returns. And, the roadmap helps create a tangible vision of their transformation. Together, they help many teams deliver value.

It's one thing to deliver; it's another to demonstrate to others that you've succeeded. Even teams that successfully achieve their outcomes run the risk of perceived failure if they can't convincingly demonstrate their success to their detractors.

Measuring the value needn't be complex - in practice, the best measurement frameworks tend to be the simplest. At a minimum, however they need to:

* Be able to be captured

* Minimize the overhead imposed on the team

* Be comparable across projects

Measures are only useful if they can be captured. That may seem obvious, but it still remains surprising the number of times teams establish outcomes which, despite being intellectually attractive, fail to have the underlying systems necessary for their capture. A classic example is trying to measure reductions in customer churn rates due to analytically based targeting. While this seems straightforward, it's often the case that operational customer-relationship-management systems lack the ability to capture whether or not a recommended offer was made by contact center staff. In the absence of this, it's impossible to know whether poor results are due to the targeting being used or simply because contact center operators neglected to make the offering the first place!

Equally, some of the worst measurement frameworks are the most comprehensive. During the design phase, it's often highly attractive to build as many measures as possible into the reporting layer. Unfortunately, unless these measures are transparently and automatically created by the underlying systems, every additional measure that requires manual input by definition reduces the time spent on actual value-creating activities. At best, the heaviest frameworks are simply ignored. At worst, they actually reduce productivity.

Finally, these measures must be comparable across projects and initiatives. The biggest value of a measurement framework lies in its ability to benchmark highly varied initiatives. Without this ability to benchmark initiatives and activities, the measures captured are meaningless.

An effective measurement framework covers a good selection of business, analytical and technical measures. Business measures help demonstrate the value that's been achieved, not the effort that's been needed to get there. Analytical measures help focus attention where it's needed by measuring the quality of analytical assets. And, technical measures help optimize by measuring the effort and time needed to achieve outcomes. Taken together, these help analytics professionals demonstrate their value-creation to the rest of the organization, prioritize their effort and investment, as well as identify potential sources for future optimization.

The Power of Information

Competitive advantage comes from capitalizing on uniqueness. Every organization is different and every organization has the potential to exploit that exact uniqueness in a way that no one else can match. Doing this means taking advantage of their single biggest resource: their data.

This tsunami of information is a real challenge at every level in society. At a personal level, we struggle to keep on top of everything that's happening around us. Alvin Toffler coined the term "future shock" as early as 1970 to describe the overwhelming and disorienting impact from information overload. And, at a professional level, where we once struggled with a paucity of information, we now struggle to pick which pieces of information are important out of the millions of measures at our fingertips. Regardless of where you start, this ever-increasing amount of information has changed the way we view the world, the way we live and the way we do business.

Dealing with this data deluge requires being smarter. It requires developing the ability to selectively process information based on value, not sequence. It requires, more than anything else, the realization that brute-force and manual effort are, in the long run, an impossible solution. Quite simply, it requires the effective application of analytics.

The people who know how to manage this data deluge are our future. Being able to translate massive amounts of data into real insight is beyond magic - it's competitive advantage distilled. Nothing else offers an equivalent level of agility, productivity improvement or renewable value. Being "smarter" than your competitors isn't just hyperbole, it's a real description of how significant the impact of applied analytics can be.

Armed with the ability to quantify, communicate, deliver and measure the value they create, these modern day magicians understand that statistical expertise alone is not enough. Instead they become change agents, transforming the organization around them.
Evan Stubbs (Evan.Stubbs@sas.com) runs the Advanced Analytics Lab for SAS Australia/New Zealand and has more than 10 years experience helping organizations extract value from business analytics. A recognized expert in innovation, Stubbs has a background advising as a management consultant with KPMG Consulting, providing architectural strategy with Deloitte, as well as managing innovation within General Motor's research and development activities. This article is based in part on Stubbs' book "The Value of Business Analytics."

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The value of business analytics - Analytics Magazine

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The intersection of statistically based insight and the realization that information can be an asset has had and will continue to have serious reverberations in the business world. Being smarter has always meant being successful; as far back as the 19th century, analytics was already generating competitive advantage.

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How Does Business Analytics Provide a Competitive Edge?

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How Does Business Analytics Provide a Competitive Edge?

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Kira Hunter
The article I chose begins by talking about how there has been a major shift in businesses recently, business analytics used to be considered as just being “nice -to-have” and now are considered by most businesses as being mission- critical. This change, it asserts, is due to the valuable data that business analytics can store and predict providing insight into the future and lowering the margin for error.  The article goes on to explain the difference between business intelligence and business analytics: business analytics simplifies data and amplifies its value with predictive modeling, forecasting, design experiments and mathematical optimization whereas business intelligence just summarizes data into reports. Over the next decade business analytics is going to be the standard and it will be a requirement for companies to maintain a competitive edge over the competition.

This article built upon what we have already learned in this course about business intelligence and analytics. It makes sense that when companies have better and more accurate data is easier to predict future trends and make better business decisions

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How Does Business Analytics Provide a Competitive Edge? | Articles | IBM

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How Does Business Analytics Provide a Competitive Edge? Gary Cokins' takes a look at how business analytics is narrowing the margins of error

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Five Ways Data Analytics Will Shape Business, Sports and Politics in 2015

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Five Ways Data Analytics Will Shape Business, Sports and Politics in 2015

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Matthew Schimmelfennig
I came across a Forbes article from 2016 that turned out to be very accurate. As the title suggests, the article described 5 ways in which business analytics (BA) will impact various entities. The first prediction claimed that BA jobs will be in great demand. A quick search for BA jobs proved that prediction to be accurate. The second prediction was that analytics will become more self serve. They are basically saying it will grow beyond the job of IT, and various employees within an organization will use BA tools themselves. The third prediction is that BA will go mainstream. It will no longer be an expensive, unobtainable task, and will be accessible to everyone. The fourth prediction was that the "Internet of Things" will increase interest in geospatial analytics. That basically means the collection of location specific information will increase greatly. The last prediction was that analytics will win the gold medal, and the US Presidency. I think that has shown to be true, with the surprise victory in the election and the formation of dominant sports teams (Penguins, Warriors, Patriots).

The article helped tie in the practical uses for Business Analytics with what I learned from the textbook. It gave perspective on the increasing use of business analytics, and how it will continue to change the landscape of our country and the world.

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Over the past few years, we've seen a steady rise in the importance of data analytics, in organizations as varied as consumer goods companies, professional sports franchises, political consultancies, medical research institutions, and financial firms. At the steering wheel of many of the analytics these organizations perform is not necessarily the ever-elusive data scientist but often a data analyst who understands his or her business and department in addition to the data it uses to drive decisions. In fact, the US Bureau of Labor Statistics reports that the job market for various data analyst disciplines is growing at 27% annually-far exceeding the national job growth average, which is a mere 11%. Many factors are lining up to make 2016 the "Year of the Data Analyst."

Here are five predictions of how analytics will shape the business, sports and political landscape in 2016:
Prediction #1: The hottest corporate job of 2016? Data analyst.
Demand for data analysts has never been higher-and 2016 will continue to see demand rise as more organizations look to deal with the explosion of data and the opportunity presented by having better analytics.

For start-ups to multinational corporations looking to hire qualified data analysts, the great news is that there are plenty of them. According to the US Bureau of Labor Statistics, between 4 and 6 million people are employed as data analysts. What's more, the pipeline for the future is bright: Universities are pumping out more graduates skilled in data analytics than ever before. Specialized degree programs in analytics are cropping up all over and courses in analytics are now not only standard fare for computer science students, but also business and economics students as well. The growth in this already abundant supply of data analysts is not particularly surprising, as the role offers an intellectually fulfilling and lucrative (salaries for business analysts start around $75K and climb rapidly from there) career path.

The combination of growing demand and supply in the market for data analysts will make it by far the hottest corporate job in 2016.
Prediction #2: IT will embrace the self-service analytics movement.
The increasing demand for data analytics has placed unprecedented pressures on IT to provide the infrastructure and tools that enable users across the business to get the answers they need. And the users will not only include individuals with a data analyst title, but also those in the organization who work with data every day - in marketing to operations to finance to HR - users who you can think of as empowered data workers.

In 2016, IT will embrace the concept of self-service analytics and relish the important role they can play in enabling business users with access to critical data. By providing business users with leading-edge analytics tools that allow them to create sophisticated analytics without requiring programming assistance from data scientists-and without waiting for data from IT-companies will find themselves on new competitive footing and ready to uncover previously hidden trends that catapult them into leadership positions.
Prediction #3: Predictive analytics goes mainstream.
Not only has the emergence of big data increased interest in predictive analytics, it has also significantly broadened the market for this kind of advanced analysis, since every function in an organization can see the value in investing in predictive analytics to improve business performance. Until now, though, actually running predictive analytics required hiring expensive data scientists with Ph.D. degrees, but with the demand for predictive far outpacing the supply of data scientists, we think that's all going to change in 2016. When you combine the number of new college graduates who want to use the newest, cutting-edge tools without being computer programmers with the real-life business experience of existing data analysts, this is the year that predictive analytics will become accessible to everyone.

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Five Ways Data Analytics Will Shape Business, Sports And Politics In 2016

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Photo: PRNewsfoto Over the past few years, we've seen a steady rise in the importance of data analytics, in organizations as varied as consumer goods companies, professional sports franchises, political consultancies, medical research institutions, and financial firms. At the steering wheel of many of the analytics these organizations perform is not [...]