Business Analytics – Competitive Advantage Through Data

In today’s business world, it is hard to imagine a successful company that does not leverage data to optimize operations or solve enterprise problems. Data is a powerful tool that ensures planning is backed by facts rather than guesswork. However, one must know how to use this tool—which is not easy when dealing with vast amounts of data without a clear strategy. This is where business analytics comes into play (not to be confused with business intelligence or business analysis—more on that in a moment). What exactly is this field? In this article, we will break down the term and identify the specific benefits it offers.    

Business Analytics – What Is It?

Data analysis in an enterprise has many faces—the most popular and often confused methods are business analytics and business intelligence. We will focus primarily on the former, but we will also touch upon the latter. We mentioned business analysis; while the term sounds similar to business analytics, it is a different discipline, and it is important to know the distinction. Business analysis is similar to BPD (Business Process Discovery), which we discuss in this article, and focuses on researching and gathering information on how organizations operate and what their needs for change or optimization are. After identifying these needs, business analysis creates a plan to implement specific solutions and maximize the value the company provides. Returning to business analytics, it uses enterprise data to predict what might happen in the future—forecasting probable future events. It identifies trends and patterns in data that explain why something is happening now and explores potential future scenarios—what happens if a trend continues (predictive) and what the best possible outcomes are (BPO – Best Possible Outcome). Business analytics requires the support of an experienced analyst who can spot key opportunities in the data and act on them, rather than just reporting on them as business intelligence does. You could say that business intelligence serves as a foundation for business analytics—it deals with what has happened and what is happening now, analyzing the current situation based on the same historical data as business analytics, but focusing on a different timeframe. The aforementioned analytics specialist can incorporate data and reports generated by business intelligence into their analysis to predict future events. In contrast, BI reports themselves are more descriptive (descriptive analytics) than predictive (predictive analytics).

What Are the Benefits of Using Business Analytics?

Reading the definition of business analytics, one can already see the clear benefits of applying it in an enterprise, especially when combined with automation and digitalization (as well as AI and ML). It is crucial for maintaining a strong market position because it allows for more informed business decisions—decisions based on hard data rather than intuition, which can sometimes be unreliable. What do awareness and knowledge bring? Certainly, a much better understanding of the reality we are in—identifying processes that can be optimized or automated, spotting new business opportunities, and ultimately, reducing costs. It also leads to a better understanding of customer needs and the ability to forecast revenue in specific scenarios that we can simulate based on available data. This, in turn, translates into improved overall organizational efficiency. We know what might happen, and we have time to plan our actions accordingly.

What Business Analytics Solutions Can Be Implemented in Most Enterprises?

As mentioned above, business analytics works best when it is not manual but leverages IT technologies and automation. Nowadays, even smaller enterprises process and use so much data that it becomes impossible to analyze and report on it all without using the latest automation technologies—provided we want to fully utilize the potential of our information and create a competitive business plan. What can we analyze? Essentially all the data a company already possesses, even if it is initially unaware of it. If we sell a product, it is no longer enough to simply state whether customers want to buy it or not. We analyze a multitude of other factors—for example, the Customer Journey: the history of searches for our products online, how customers behave on social media, which links or ads they click, how long they visit our website, and how they navigate it. We can also compare competitor prices with our own, analyze buyer geolocation, and review their feedback. These are, of course, not the only areas where we can use business analytics and business intelligence—there are truly many options, and new data sources and ideas for their use are constantly emerging. Analytics can help us define company KPIs (Key Performance Indicators) and determine how to improve results in a given area; predict which roles and employees we will need in the future, which supports recruitment processes; and optimize routes and logistics solutions (if needed). HR and finance departments will also find opportunities to use business analytics. It is important to determine why we need specific data and what we can discover with its help.

Better Decision-Making Based on Analysis

The main, overarching goal of all automation and the use of business analytics for data processing is better decision-making within the enterprise, which usually translates into savings and cost minimization. We do not need to analyze something that will not provide an opportunity to optimize our operations—that is why it is important to take a conscious look at what data can tell us and decide whether it will be useful. At Mindbox, we have case studies that show how automation and well-thought-out data analytics allow for process acceleration and, consequently, optimization—which ultimately generates savings. Our client, Credit Agricole, used RPA (Robotic Process Automation) to speed up the process of collecting and processing the vast amount of data needed by analysts to make credit decisions. Thanks to our solution, analysts were freed from routine, manual work, which translated into faster and more accurate analyses and the ability to redirect human effort to areas that truly needed it. Eliminating the human element from certain parts of the process also eliminates potential human errors, making credit decisions more reliable and contributing to the bank’s overall efficiency. Having hard data will always help us make better decisions. Even if we trust our intuition on a daily basis, it is not the key to success in business—we must look at the facts to be able to prepare for what the future brings.

The Impact of ML and AI on Business Analytics

Taking it a step further, the use of AI (Artificial Intelligence) and ML (Machine Learning) allows for deeper analysis of more data from multiple sources simultaneously, as these technologies work together easily. As a result, analytical tools provide even more accurate information thanks to the latest technologies. Incorporating automation (as mentioned in the case of our client Credit Agricole) and intelligent machine learning allows for achieving great results faster than ever before—most importantly, these results are based on data that shows the world as it is. Implementing AI and ML leads to very concrete benefits—we can save up to 65% of time, and processes see 95% fewer errors than before. If you are curious about the benefits that business analytics combined with AI and ML can bring to your organization, contact us!