Like many leading business schools around the world, the University of Tennessee at Chattanooga (UTC) Gary W. Rollins College of Business has fully embraced digital technology and complex information analysis to prepare students to excel in today’s data-driven business environment. In fact, more and more graduate students are choosing to pursue a Master of Business Administration (MBA) with a concentration in data science or business analytics.
If you’re wondering what an MBA in Business Analytics can do for you, read on for critical information about this popular and practical academic and professional focus.
What Is an MBA in Business Analytics?
Like most MBA programs, the average MBA program with a concentration in business analytics will take students approximately two years to complete. It will also expose students to many of the same basic principles as a general MBA. However, it will go on to offer focused training in the field of business analytics.
A massive umbrella term, “business analytics” involves the collection, categorization, processing, and evaluation of information, often using statistical models and methodologies, to produce business insights. Senior administrators and business managers can use these insights to solve specific problems and drive increases in efficiency, productivity, and revenue.
The MBA in Business Analytics program at UTC requires students to complete nine credit hours in areas that emphasize business analytics. This translates to three elective courses that cover subjects ranging from databases and data warehouses to advanced data analytics.
Why Pursue an MBA in Business Analytics?
Let’s face facts: Big Data is king in the modern business world, and global events continue to make the analysis of business data more and more vital.
Take, for example, shipping logistics and the supply chain. Since the onset of the COVID-19 pandemic, local, national, and international supply chains have been in turmoil. This has led to an overwhelming demand for business analytics professionals who can compile and evaluate the information needed to better understand and optimize supply chain networks.
The supply chain industry mirrors many others in terms of its need for data mining and data analytics. Quoting a study by the McKinsey Global Institute, the MBA educational resource provider E-GMAT reports that US businesses currently face a shortfall of “1.5 million data-savvy managers and analysts.”
In even better news for business analytics graduate students, jobs in this field are not only abundant but lucrative. Studies have shown that graduates with an MBA in Business Analytics tend to make more money than graduates with a general MBA.
Skills Gained with an MBA in Analytics
Beyond the significant benefits outlined above, graduates with an MBA in Business Analytics gain a set of highly in-demand skills. Grouped under the general term “business analytics” or “data analytics,” these skills involve the acquisition and use of information to answer questions, identify trends, and generate insights that can help companies make decisions and formulate strategies.
Although you can learn a broad spectrum of data analytics skills in a good business analytics graduate program, these skills can generally be divided into one of four broad categories. Each of the following types of data analytics has enormous potential to aid businesses in virtually all areas of planning and operation:
Descriptive analytics answer the question “What happened?” Business data analysts engage in descriptive analytics through the interpretation and juxtaposition of specific sets of historical and/or contemporary business data to better understand and describe targeted company trends and general company evolution. They can use descriptive analytics to compare performance during different periods of a company’s history or to compare the performance of one company to that of another. To coincide with traditional financial reporting periods, business analytics professionals typically conduct descriptive analytics on a quarterly or annual basis.
From year-over-year (YoY) pricing changes and month-over-month (MoM) sales growth to annual tabulations of consumer subscriber and user rates, most of the global financial industry’s widely reported metrics are products of descriptive analytics. Descriptive analytics also includes monitoring online consumer engagement through social media and web traffic analytics. By keeping an eye on key performance indicators (KPIs), such as the number of unique website visitors you receive per month, you can make progress toward important business goals.
The analysis of routine financial statements is a basic form of descriptive analytics that most people are familiar with. Any company that wants to chart its overall performance will naturally want to compare this month’s balance sheet, income statement, or cash flow statement with their sister documents from previous months.
Diagnostic analytics answer the question “Why did this happen?” After employing descriptive analytics to identify important patterns and trends, business data analysts often turn to diagnostic analytics as a logical next step. If they want to replicate, avoid, or otherwise benefit from the patterns and trends they have identified, they typically must determine the fundamental reasons behind those patterns and trends.
Diagnostic analytics works by establishing cause-and-effect relationships between variables in specific sets of data. While performing diagnostic analytics, it is critical to differentiate instances of logical causation from instances of mere correlation. Correlation is a purely statistical connection involving two or more metric variables that rise or fall together. But any two events that occur together do not necessarily have a cause-and-effect relationship.
For example, a business analyst is studying the relationship between sunscreen sales and ice cream sales. Because these rates rise and fall together, analysts may falsely assume that wearing sunscreen increases one’s appetite for ice cream. But a closer look reveals that both sunscreen and ice cream sales peak during the summer. Therefore, the real cause-and-effect relationship here is seasonal in nature, and the relationship between sunscreen and ice cream sales is purely correlational.
Business professionals have been conducting diagnostic analytics without digital aid for centuries. However, modern computer algorithms and statistical software like Microsoft Excel have made the diagnostic analytic process far faster, more precise, and more comprehensive.
Predictive analytics answer the question, “What might happen next?” While diagnostic and descriptive analytics use data to better understand the past and present, predictive analytics uses data to predict future events and make projections about trends that have yet to occur. However, it often relies on the same historical business data to formulate its predictions and projections.
By measuring this historical business data against relevant aspects of the broader market environment, business data analysts can forecast future developments and predict potential scenarios that can aid in ongoing decision-making and strategic planning. The forecasts and predictions of predictive analytics can help companies anticipate and proactively address issues that may arise as soon as a few hours or as distant as many years into the future.
The practical applications for predictive analytics in the business world are virtually endless and can vary dramatically from industry to industry. An automotive manufacturing facility, for example, might use predictive analytics to prevent assembly line malfunction by predicting the failure of a crucial piece of machinery. A pharmaceutical developer, by contrast, might turn to predictive analytics to facilitate the early detection of potential allergic reactions in patients.
Other uses of predictive analytics range from forecasting company cash flow to determining company staffing needs. Although it can be accomplished manually, predictive analytics generally involves the use of artificial intelligence (AI) and machine learning (ML) algorithms.
Prescriptive analytics answer the question “What should we do next?” Although it shares its forward-looking purview with predictive analytics, prescriptive analytics is goal-oriented in nature, determining what actions must be taken to reach a desired outcome. Like all forms of modern data analytics, modern prescriptive analytics employs technology to analyze often massive amounts of raw information that can help senior administrators make more informed and, hopefully, better business decisions.
Prescriptive analytics leverages the power of AI and ML to transform data about past and current performance, available company resources, and possible situations and scenarios in the marketplace to suggest a course of action. Its proven ability to rise above the noise of immediate operational uncertainty and changing market conditions has made prescriptive analytics an invaluable tool when it comes to limiting risk, increasing efficiency, preventing fraud, driving customer loyalty, and setting and meeting ambitious business benchmarks.
Earn Your MBA in Business Analytics at UTC
An MBA in Business Analytics from the UTC Gary W. Rollins College of Business is a mark of both technical and managerial proficiency in the business arena, with a concentration on collecting, categorizing, processing, and evaluating data, as well as using this data to optimize general operations and accomplish goals. There is no better way to prepare for a business career in data science than to enroll in the UTC MBA program with a focus on business analytics.
For more information about the MBA with a business analytics concentration, visit the official MBA webpage of the UTC Gary W. Rollins College of Business or contact a skilled and knowledgeable UTC graduate school representative today.