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 

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. 

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. 

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” 

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.  

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. 

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.

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.

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. 

 

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/

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. 

 

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

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.

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.

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.

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.

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.

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. 

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. 

                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. 

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!