Businesses of all sizes rely on data to make decisions—and in an uncertain and fast-paced world, they need to make decisions quickly. Whether business leaders need to set the price of a product, determine how many salespeople to hire, or explore acquisition opportunities, they need to be able to gather, analyze, and interpret the right data to make the best decision for the organization.
That’s where business analytics comes into the picture.
What is Business Analytics?
The IT analyst firm Gartner defines business analytics as the use of a set of software applications to build statistical models that help leaders look at data on past business performance, understand the current situation, and predict future scenarios.
The focus on future outcomes separates business analytics from disciplines such as business intelligence. According to the data analytics company Tableau, business intelligence emphasizes the what and the how, so that organizations can continue what’s working and change what isn’t. Business analytics, on the other hand, focuses on why things happen to enable educated, data-driven predictions. Business intelligence might help a company decide to manufacture more of a certain product to keep up with increased sales, while business analytics would explore the factors that led to increased sales in order to drive additional sales of that product or generate ideas for boosting sales of other products.
Keep reading to explore the common uses of business analytics, the necessary skills for success in a business analytics role, and the career paths for those who earn a master’s degree in business analytics.
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Common Applications of Business Analytics
The data analytics company MicroStrategy identifies four typical uses of business analytics, ranging from the least to the most complex.
Descriptive analytics summarizes data to explain what has happened or is happening. Statistical techniques such as data aggregation (collecting and filtering data) and data mining (using statistical techniques) enable business analysts to identify trends in data. A descriptive analysis is most commonly presented in a report that uses visual aids such as charts and graphs to make the analysis accessible to a wide range of internal stakeholders.
Diagnostic analytics looks at what has happened to try to determine the root cause of those events using mathematical functions such as probabilities, likelihoods, and the distribution of outcomes. This information tends to be displayed in a business dashboard, which is a software application that provides multiple data visualizations in a single screen and offers filters so users can drill down into specific data sets of interest.
Predictive analytics typically combines statistical models and machine learning algorithms to predict the likelihood of various outcomes, such as whether consumers will like a new flavor of sports drink or how much healthcare costs will increase. Because these analyses are used to create sales and marketing forecasts, they tend to be presented in highly detailed reports.
Organizations can make the move directly from descriptive to predictive analytics if they have both machine learning expertise and technology in house.
Prescriptive analytics provides recommendations for the actions that will provide the best results. Accomplishing this requires iterative analysis, ongoing testing, and deep learning. (The software company MathWorks describes deep learning as a subset of machine learning that enables computer models to analyze data and perform complex tasks.) Use cases for prescriptive analytics include audio speech recognition, driverless cars, and e-commerce recommendation engines.
The discipline of business analytics is closely related to that of data analytics, but there are some notable differences. The data analyst is typically responsible for maintaining the database and cleaning up the data so that it can be utilized in reports, while the business analyst uses the data for strategic decision-making. The business analyst role also involves evaluating existing business processes to find ways to improve efficiency or cut costs—something that is not the responsibility of a data analyst.