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Businesses, both big and small, operate and generate large volumes of information. Data that is analyzed wisely can be the key to business success.

By leveraging data analytics, businesses can increase efficiency and productivity. Data analytics can also pave the path to a competitive advantage that increases revenue.

While it is true that data analysis calls for additional resources, the results it delivers for businesses are worth the investment.

Let's find out more about why data analytics is so critical for small businesses and what benefits you stand to receive by implementing it.

5 Key types of data analysis techniques used by small businesses

Small businesses benefit from using different types of data analysis techniques based on business and technology. There are five key data analysis methods essential to building business resilience in 2022:

Text analysis, or Data mining helps discover a pattern in large data sets using databases or data mining tools. With its help, data analysts can transform raw data into valuable business information. Data mining allows for extracting and examining data to identify patterns and relationships, and interpret the data to make informed business decisions. Data mining comprises the following steps: data sourcing, data exploration, modelling, and deploying models.

Statistical analysis is the process of collecting, exploring and presenting large amounts of data to discover patterns and trends. Five steps are taken to complete statistical analysis, including:

1. Data description

2. Building connections between the data and the underlying population

3. Creating a model to summarize these connections

4. Proof of the model validity

5. Predicting future trends.

Diagnostic analytics is a form of advanced analytics that examines data or content to answer the question, "Why did it happen?" It is characterized by techniques such as drill-down, data discovery, data mining and correlations ( Gartner). Diagnostic analytics implies using data to determine the causes of trends and correlations between variables. This method can be employed across a number of use cases, such as examining market demand, analyzing customer behavior, improving company culture, etc.

Predictive analytics is used to make predictions about future outcomes based on historical data and statistical modeling, data mining techniques and machine learning. Businesses use predictive analytics to identify future risks and opportunities based on data analysis.

Prescriptive analytics examines data or content to provide a recommendation on the steps a business should take to resolve an issue. This form of analytics employs a wide range of instruments and techniques, including graph analysis, simulation, complex event processing, neural networks, recommendation engines, etc.

How data helps small businesses

With the help of the right technology, data analytics can do its magic. Small businesses can reduce operational costs, improve their business model, increase revenue, and stand out amongst the competition. Moreover, you can replace many of the manual data analysis processes with automated business solutions .

Let's explore 6 ways you can boost your business performance with data analytics.

1. Establish a data-centric approach to business

Source: Amadeus

When companies have the ability to analyze high amounts of information associated with their business and the market in general, they can create a complete picture of the customer journey.

For instance, you'll know how people get to know the brand, what they buy, why they do it, how they shop, at what time, from which device, why they abandon the carts, and what makes them choose a brand again and again.

With this kind of information, small businesses can change the way they interact with potential consumers, stick with the preferred communication channels, and resolve issues faster.

Data analytics provide insights that illustrate a complete business overview, eliminating the need to make decisions based only on guesswork and what others do, allowing companies to act according to their business's and clients' needs.

The critical point here is that such a data-centric approach relates not only to customers but to the overall performance of a business. Having data gathered and analyzed is the way to act in a data-driven way in order to streamline operations, improve HR activities, and more.

2. Add clarity to financial processes

Even small businesses can spend a fortune on advertising, but all of it may be in vain. This can happen due to limited knowledge about your target market and methods for using digital marketing tools .

Digital marketing can be a tricky thing - you have the potential to attract a lot of new customers, yet you need to know how much you are spending on advertising and what your return on investment is in the end.

With the help of data analytics and management, you can track how much money you give away to onboard new consumers and how much profit you receive from them. It is important to bear in mind that the revenue clients deliver to your small business should cover all the advertising expenses.

From a financial standpoint, data management can keep your company's financing and reporting clean. For example, you can set up Excel integrations and get all the financial data from different tools in one place. This helps with reporting, understanding your cross-department expenses, salaries, fixing up financial processes, and more.

3. Improve inventory management

It may seem that inventory issues aren't critical for small businesses. In fact, not having an inventory management strategy, keeping unclear inventory records, over/under ordering, and other mistakes can damage your business. You can improve all these with data analytics.

With data analytics, you can upgrade operational efficiency to avoid stockout, set proper stock levels, empower order fulfillment, set accurate product descriptions, and eliminate excessive warehouse deliveries and errors.

In this way, you collect and evaluate data to optimize your existing process and build an effective inventory management system.

You're also encouraged to use data as a basis for predictions. For example, you can conduct stock optimization based on weather forecasts, current trends, and recent consumer queries. Instead of stocking up on too many items, you can only get the most popular things and avoid stock overload. Whenever you can track what's happening on the market and foresee how it will change, you can massively improve inventory management and save costs.

4. How to better understand the market and competitors

Source: Pinterest

If you can dive into your business data, you may be able to identify valuable insights about your competitors and their niche. There are many ways a business process analyst can understand the type of clients your competitors are targeting, what products they buy, and how much profit they earn.

Such information can be of great use for small businesses. They can adjust faster to the market's needs, decrease costs, and gain more consumers by offering the appropriate products and services. Understanding the market's tendencies and the moves of competitors can help small businesses beat the competition and emerge at the very top of their industry.

5. Skyrocket marketing and promotional efforts

With a better understanding of how things work on the market and how competitors win over their clients, small businesses can easily predict where the trends are heading, how much in demand certain products are, and more.

Leveraging a data-centric approach eliminates the guesswork and provides facts that you'll be able to implement in marketing your efforts. Data analytics will give you insights into what your consumers want and allow you to track them down and promote things from their wishlist.

You'll be able to:

analyze website performance

adjust your marketing strategy

choose digital marketing tactics

use suitable channels

set up promo campaigns

You can measure all the data down to the minor details - how people move around the online store, how they interact with your brand, how much time they spend on certain pages, etc.

There is no need to spend money on promoting items you think people might like. Instead, you can invest in marketing products people want at this very moment and lay down a budget for the next season, already knowing what they will search for in a few months.

6. Refine customer service and the customer experience

Using data analysis, small businesses can enhance their customer experience and provide greater customer service. By knowing how people think, behave, and drive their decisions, you can change the way your company is interacting with consumers.

There are many factors that could cause people to leave - a slow website, a higher price, a lack of responsiveness from a customer support representative. Whatever it is, you can use analytics to track where people fall out of the loop and make sure you are ready to catch them next time.

People tend to stick with those brands that make them feel valued. Even a small business can have many loyal consumers when the service it provides is outstanding. Based on data analysis, personalized customer service helps to engage with every client at the right time and through a proper channel, providing clients with a great experience and driving sales.

How many businesses use data analytics?

The use of data analytics among small businesses has been steadily increasing over the past few years. According to SCORE Association, in 2020, only 45% of small businesses were actually using analytics, while 51% of respondents considered data analytics important. On top of that, 73% of small businesses prioritised finding new customers, while customer retention (67%) and customer experience improvement (65%) were named less important.

Allied Market Research predicts that the big data and business analytics market will grow by 13.5% from 2021 to 2030, reaching an estimated $684.12 billion. By 2022, business intelligence investments have increased by as much as 50% across tech firms, manufacturing, retail, as well as finance (Dresner's Business Intelligence Market Study, 2021).

At the same time, the more data is generated every day, the more use cases we see for data management and analytics. Unlike larger enterprises, very few small businesses can source, store, and analyse big amounts of data. Hence, the growing popularity of DaaS (Data as a Service) companies offering subscription-based services for data collection, storage and analysis.

Do small businesses need Big Data?

We described different ways your business can use data analytics above, lots of them intersect and can be empowered with Big Data.

While Big Data refers to large volumes of structured and unstructured information, small businesses sometimes mistakenly believe that they are too small for it, have no particular need to analyze it, or lack the budget for its implementation.

The truth is, regardless of the size of the business and the amount of information it generates, Big Data is the holy grail for those who know how to deal with it.

You can analyze your entire volume of information and leverage it for useful insights that result in better business process management and strategic moves.

On another note, there are Big Data solutions that will fit the tight budget of a small business. They can still provide actionable insights into user behavior, monitor current market trends, and reveal existing issues.

Conclusion

Trying to figure out how the market will change next season or looking for ways to retain clients based on your inner beliefs tend not to net any tangible results unless your decisions are backed by data. You can keep guessing why your business spends a fortune on advertising but gets no consumers, or you can start leveraging data analytics to gain all the needed insights into your small business.

Data analysis can help small businesses change how they promote their products, engage with clients, and manage finances. It can not only provide actionable insights into user behavior but assist with reducing costs, driving revenue, and providing a one-of-a-kind customer experience that makes people stay.

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Why data analytics is critical for small businesses

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Businesses, both big and small, operate and generate large volumes of information. Data that is analyzed wisely can be the key to business success. By leveraging data analytics, businesses can increase efficiency and productivity. Data analytics can also pave the path to a competitive advantage that increases revenue.

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Dario RemigiSenior Research Analyst, BIBA Medical Ltd.

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Data Analytics Services and Solutions I Rishabh Software

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Rishabh Software is a data analytics services company offering cloud-based business intelligence, data visualization & data engineering services across industries.

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Business analytics enables managers to understand their company's dynamics, forecast market developments, and manage risks.

Companies are adopting analytics and rigorous statistical reasoning to make decisions that enhance efficiency, risk management, and profits, rather than "going with gut" when keeping inventories, pricing solutions, or employing people.

Data is provided for all essential company activities, including industry trends, consumer behavior, productivity, inventory, and thorough financial analysis.

Business intelligence software collects information and transforms it into clear insights to enable actionable and strategic decision-making, allowing employees to easily achieve their objectives.

Big data is driving a lot of advancement in a variety of businesses. If you haven't already figured out how it works to make businesses and organizations more efficient through its many procedures, now is the time to do so!

(Suggested Read - Big Data Analytics)

One way Big Data may help with this is through strategic usage in Business Analytics. Don't worry if you've never heard of the phrase Business Analytics; it's very closely related to a term you've probably heard of - Data Analytics.

Data analytics is the process of analyzing data using various approaches, some quantitative and others predictive, in order to get insight into the data. So, what exactly is Business Analytics? Simply said, it's Data Analytics, but it's used to help businesses achieve their goals and make business decisions.

Business Analytics, like Data Analytics, may utilize past data to make sense of current data, discover trends, and forecast which consequence is likely to occur (if at all) and when.

Finally, it may provide crucial insight into what will be the greatest conclusion for your organization - and as a consequence, it becomes an indispensable tool, particularly when it comes to making profitable judgments.

(Also Read: Steps of Business Analytics Process )

As technology has slowly but steadily advanced beyond our expectations, there are now countless data sources and many more ways to categorize and analyze them. Every person, gadget, or organization generates an almost unmanageable quantity of data practically every day, which causes issues with logistical storage.

Businesses use a variety of methods to ensure that issue is resolved, with cloud storage being a significant one. Beyond that, the more immediate problem is the expertise necessary to sift through these mounds of data and uncover the insight that can cause a ripple effect across a business function or branch.

Watch this Video on: Introduction to Business Analytics.

Because that's the beauty of data: it doesn't only help in one area, even if that's how its benefits are typically perceived. It has an immediate influence and then gradually and progressively alters the system around which the data point was constructed.

(Also Read: Techniques used in Business Analytics Framework )

Business Analytics, in particular, uses Data Analytics to enhance efficiency in terms of production and expenses, as well as to determine whether or not the general structural systems that are applied are successful.

Furthermore, Business Analytics may aid in broad strategic direction as well as the formalization of decision making procedures.

Ways in which Business Analytics aids decision-making in businesses

When we say that business analytics assists with decision-making, we mean that it gives information that can help the firm decide the route it will go in the future.

Increasing bottom line and profitability, offering stronger and more sustainable risk management, and decreasing total expenses - including overheads, when possible - are some of the typical ways in which business analytics has been used thus far.

(Related: Real World Applications of Business Analytics)

Here are some additional particular ways in which Business Analysis may generate compelling insights that are sure to impact choices at large corporations:

By analyzing consumer trends, we can provide unique customer experiences

Data analytics is becoming an essential component of the foundation that all organizations must build if they are to prosper. When contextualized more excellently through a business-specific lens, it gives a particular depth that can assist the organization make sense of how consumers are consuming its products or services.

What is a realistic example of this in action in a real-world setting? A telecom business can use analytical models, especially predictive models, to ensure that their consumers stay with them for a longer period of time and to evaluate the effectiveness of their marketing activities.

Similarly, an online eCommerce company may use business analytics data to determine which of their visitors stay and for how long, as well as which of them return - and why.

Questions of this kind can help to build a comprehensive platform or foundation upon which a strong plan can be implemented.

And the greatest thing is that it can be altered as additional data is collected, so it's never too late to make a course correction that may totally impact an organization's destiny!

Thus, the business analysis provides an organization with the proper data to acquire a competitive edge and ensure that they can cater to their consumers in the best possible way, for the best possible conclusion.

( Also Read: What is Cost-Benefit Analysis? )
Process of Business Analytics in making decisions
Business analytics has several applications and benefits, and it would be imprudent to focus just on one element when there are others that can also be useful.

Consider business analytics to be a tree, with roots that go deep into the structural machinery of the organization and branches that touch every area of the organization.

Whatever function or application you can think of, there is a possibility that business analytics may have a beneficial influence on it, and not only that, but it can also alter it for the better.

Consider internal resource management as an example. Business analytics may give critical insight into how to manage expenses, where a certain cost can be restructured so that it does not affect the bottom line, and so on.

( Also Read: AI strategize to maximize Business Revenue )

Furthermore, an organization can conduct an in-depth analysis of firm performance in terms of particular critical performance indicators (these depend on the nature of the organization for the most part).

Then, business analytics may shed light on workers' real performance and show how they might be better managed, such as which variables affect their motivation, how long they are likely to stay in an organization, and so on.

Finally, it has the potential to significantly alter an organization's approach to its people - the fundamental driving force behind its growth.

Taking into consideration numerous data points such as professional performance, experience, age, and a number of other characteristics, it becomes easier to make educated estimates about the performance of present and prospective workers. There will also be information into who is most likely to perform to their full potential in which job.

To summarise, once the insight from business analytics is utilized, there will be a significant increase in the company's overall performance.

Providing insight into risk management and how to improve overall management

Understanding risk is a critical component of optimizing company choices, and business analytics excel at effectively resolving this issue. Business analytics may be used to analyze all of an organization's data, whether it is housed in databases or is unstructured and spread across several sources.

Business analytics may be used to avoid crises or optimize reactions during a crisis period in order to resolve it in the best way possible by providing insight into what is likely to happen. This translates into better risk management, but in the context of the organization's work processes, it translates into overall better management practices.

How does this manifest itself in practice? For one thing, banks may use it to get a better image of who they should lend to by analyzing a potential client's credit ratings and customer loyalty rating.

Furthermore, the business analyst may be used to determine if some high-risk payments are actually worth the risk, or whether particular delinquents on the credit spectrum show no signs of recovery.

In terms of practicality, there are better methods to interact with the end customer and manage internal resources when a specific call to action is required.

Without the power of business analytics, a lot of precious resources would likely be wasted on customers who would eventually default on their payments; however, with the power of business analytics, it becomes super simple to have accurate predictions and to allocate internal resources to ensure that all issues at hand are handled well.

( Also Read: AI in Risk Management )

To be honest, budgeting and investment decisions have a direct influence on organizational success. As a result, you must back up these judgments with statistical data and logic to guarantee that the firm does not incur losses.

In this case, business analytics may assist you in managing tangible assets while also shedding light on a company's financial performance. It emphasizes sectors that consume a substantial portion of profits, such as power bills, tariffs, salaries, and so on.

( Must Read: Use of AI in Business Functions )

Supply chains, believe it or not, are a fantastic area to uncover possibilities and commercial advantages. After all, they contribute significantly to the company's cost structure.

As a result, by employing business analytics, you may discover possible inefficiencies in traditional supply chain systems. Similarly, you may identify abnormalities and check data to guarantee that deliveries are made on schedule.

( Also Read: Supply Chain Management - An Overview )

Furthermore, business analytics employs data and quantitative approaches to improve decision making across the whole supply chain.

To apply statistical approaches to all current data sources, you may integrate enterprise resource planning. Aside from improving front-line operations, data-driven processes may lead to better sales and inventory planning.

Watch this video on: How Business Analytics helps in Decision-Making

Business analytics is one of those industries that is rapidly expanding and will undoubtedly be there for a long time. This is an excellent moment to engage in business analytics, whether as an organization to reap future advantages or as a person to understand it in order to use their services.

It identifies consumer patterns, evaluates potential hazards, and creates feedback and improvement mechanisms. As a result, driving analytical changes can assist businesses in remaining at the forefront of the digital revolution.

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How Business Analytics impacts Decision Making in Businesses | Analytics Steps

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Business analytics enables managers to understand their company's dynamics, forecast market developments, and manage risks. Companies are adopting analytics and rigorous statistical reasoning to make decisions that enhance efficiency, risk management, and profits, rather than "going with gut" when keeping inventories, pricing solutions, or employing people.

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We've heard a lot in recent years about the revolutionary effects of and the internet of things ( IoT ). They have indeed had big effects in a number of areas, from retail to digital marketing and healthcare .

With so much data at our disposal, however, the difficulty is in knowing how to make use of it. This is where big data analytics comes in.

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Data analysis allows us to obtain valuable insights while saving time and effort. Without it, we'd be left searching for needles in the proverbial haystack of unstructured data .

What is data analytics?

In short, the term is used to denote the process of delving into raw data and drawing insights and conclusions from it.

Data analysts use analytics applications (with capabilities like machine learning and automation ) to help them gain useful insights. Open-source data analysis tools like Pandas -a tool built using the Python programming language -are increasingly commonly used.

An exploratory data analysis can provide an overview and summary of the main characteristics of datasets . Businesses can then use methods such as data visualization to present the insights they've uncovered to wider audiences-including customers and clients-and suggest next steps based on those insights.

Armed with the information gleaned from business analytics , firms are better able to meet the needs and desires of their customers (and to win new ones).

Types of data analytics

There are four basic types of data analytics , and these are as follows:

1. Descriptive analytics: Describes what has occurred over a certain period of time. For example, increases in sales or higher page views in a particular month or quarter.

2. Predictive analytics: Predicts what is likely to take place in the relatively near term based on previous data.

3. Diagnostic analytics: Analyzes why a particular phenomenon (such as an increase or decline in sales) has occurred.

4. Prescriptive analytics: Suggests a certain course of action (e.g. a seasonal increase in stocking of a certain product) based on past data.

There's also regression analysis; this term covers a number of statistical methods used to estimate relationships between a dependent variable and one or more independent variables. This can help to provide insights into the strength of the relationships between variables, and model their future relationships.

By analyzing historical data , we can make better business decisions as we're more able to anticipate fluctuations in consumer demand, and to understand why these occur. This, if used properly, should ultimately translate into better profitability and a healthier bottom line.

However, data quality and efficient data management matter a great deal. If your data are substandard or somehow unreliable, you won't be able to optimize your business decisions as you would otherwise. Structured data is indispensable.

Benefits of data analytics

Now that we've defined data analytics and outlined its different types, we need to look at the potential benefits it can offer businesses. There are numerous applications of data , so it's probably best if we concentrate on a few in particular here.

Some of the key benefits of data analytics include:

1. More precise marketing: With the help of data analytics , you can get to know your target audience better. You have a clearer appreciation of what they're looking for and what they need. This allows you to target them more precisely with better-crafted campaigns, including via social media .

2. Better decision making: Following on from our previous point, data analytics allows businesses to sharpen their decision-making skills. Equipped with a more thorough understanding of their customer base and their own performance, they can use the insights obtained via data analytics to make improved decisions as well as making project management more effective.

3. Enhanced efficiency: Thanks to data analytics , it's possible for businesses to streamline many of their processes, thus rendering them more efficient while also enabling them to cut costs. Thus, it also helps them with financial analysis , enabling them to deploy their resources more efficiently. For instance, with regard to targeted marketing campaigns.

4. Improved customer service: Last but by no means least, data analytics can also help businesses to improve their overall standard of customer service. Firstly, it provides in-depth insights into what customers want and their preferences. Secondly, storing data in a single central location and allowing your whole customer service team to access it can help to ensure better consistency of service quality.

How businesses can use data analytics

We've talked about how data professionals - data scientists and engineers-make use of data analytics , but it's worth stopping to discuss how businesses make practical, everyday use of it. This is a challenging undertaking because the volume of data involved is so substantial. It's important, therefore, to give careful thought to applications of data analytics .

1. Planning and strategy: Before doing anything else with regard to data analytics , businesses must ensure that they have a long-term plan and clear objectives in mind. They must ask themselves questions about their data requirements : Namely, why they want to collect certain types of data (to learn more about customer interactions, for example) and what they hope to achieve.

2. Collecting data: Once businesses are clear in their own mind about the purpose of data analytics , they then need to determine which data sources they'll be using, which data points they'll be concentrating on and how to collect that data. Some simply use transaction and social media data, while others use high-tech sources including GPS and RFID chips.

3. Ensuring data is relevant: We mentioned earlier that raw data tells us very little at first glance. Businesses have to ensure that the quantitative data they collect is relevant and that they know how to make sense of it. Simply hoovering up huge quantities of data does little good-and may prove to be actively counter-productive.

4. Making effective use of data: Businesses which intend to deploy data analytics must think carefully about how they intend to do so, and provide sufficient resources for the purpose. Which metrics do you plan to use? Some businesses employ in-house data analysts , which can give them the edge over competitors, but for smaller firms, employing their own data specialists is unlikely to be viable.

5. Presenting data: Earlier, we mentioned the importance of data visualizations in presenting findings and rendering them more comprehensible. Tools like Tableau can help businesses with visualizing data in the form of charts and graphs. These can then make data presentation aids. For instance, in video tutorials and webinars , as well as for the eye-catching infographics that prove popular on sites like LinkedIn .

6. Acting on new insights: It's one thing gaining all these insights via data analytics , but businesses must have an action plan in order to put them to practical use. How might certain findings help your business improve the service it provides to customers? How might you use it to reach new customers?

Data analytics technologies

There's a wide range of data analytical tools that businesses can use, and more are being introduced all the time. Some of the most widely-used data analytics technologies include the following:

1. Predictive analytics: We briefly defined predictive analytics earlier, as using old data to forecast future outcomes. Based on statistical algorithms and machine learning (about which, more later), it provides businesses with a clearer idea of what to expect in the relatively near future, allowing them to prepare and position themselves accordingly.

2. Data management: Collecting data is one thing, but you need to know how to manage it if you're going to make effective use of it. Analytics processes and tools are therefore indispensable for managing the flow of data and organizing it. They can help your business ensure that you have high-quality data and that it's easy to obtain when your teams need it.

3. Data warehouse: A kind of management system used to facilitate data analytics and other business intelligence processes. Businesses can use data warehouses to centralize data drawn from various sources, thus making it more manageable and easier to draw insights from. It can also come to serve as a useful historical record which data analysts can use to make long-term comparisons.

4. Machine learning: A subset of artificial intelligence (AI), machine learning essentially involves the simulation or approximation of human intelligence in completing tasks. As the name implies, it involves applications which can analyze data and learn from it over time, ensuring that it gradually becomes more and more accurate at predicting outcomes.

Why is data analytics so important?

If you're still wondering what all the fuss is about when it comes to data analytics , perhaps this will convince you: Just look at what some of the biggest businesses in the world are doing, and how seriously they're taking it. Firms such as Amazon are eagerly seizing on the opportunities provided by big data to help them know their customers inside out.

Data analytics is widely used in retail today, and in the healthcare sector. The travel and hospitality industries have also enthusiastically adopted data analytics , as it enables them to develop a much more detailed understanding of what their customers want, and how to resolve potential problems.

The stark reality is this: If your business isn't making good use of data science , then there's a high chance that its rivals already are. This could leave you at a serious competitive disadvantage. But if you take advantage of the opportunities data analytics offers you, your business could be the one to steal a march on them.

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Why data analytics are crucial for your business

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We've heard a lot in recent years about the revolutionary effects of and the internet of things ( IoT ). They have indeed had big effects in a number of areas, from retail to digital marketing and healthcare . With so much data at our disposal, however, the difficulty is in knowing how to make use of it.

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4 Types of Data Analytics to Improve Decision-Making

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Learning the 4 types of data analytics can enable you to draw conclusions, predictions, and actionable insights to drive impactful decision-making.

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How Data Analytics is used to Improve Decision Making?

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Data is one of the most powerful tools available for orgnisations today and is considered a gold mine today. The proper usage of data can help you make strategic and critical business decisions that will immensely improve organizational performance.