What is Business Analytics?

As Big Data continues to grow, so does the demand for business analytics professionals. A deluge of abstract information exists, spurred over two decades ago by the internet and accelerating at a significantly faster pace over the past few years. The long-used database management and analysis techniques no longer suffice, particularly where unstructured data like product reviews and social media posts is concerned. Data analysis careers—business analytics, in addition to business intelligence and data science roles—address the new methods for organizing, gaining insights from, and making predictions with this steadily increasing amount of information and frequently incorporate computer science and other technical knowledge.


Whether you’ve spent the past few decades in an analytical role or you’re exploring the myriad of computer science applications, you might be wondering, “What is business analytics?” Here’s a primer on this burgeoning field and the skills needed to start a career.

The Definition of Business Analytics

Business analytics is the process of transforming data into insights to improve business decisions. Data management, data visualization, predictive modeling, data mining, forecasting simulation, and optimization are some of the tools used to create insights from data. Yet, while business analytics leans heavily on statistical, quantitative, and operational analysis, developing data visualizations to present your findings and shape business decisions is the end result. For this reason, balancing your technical background with strong communication skills is imperative to do well in this field.

At its core, business analytics involves a combination of the following:

  • identifying new patterns and relationships with data mining;
  • using quantitative and statistical analysis to design business models;
  • conducting A/B and multi-variable testing based on findings;
  • forecasting future business needs, performance, and industry trends with predictive modeling; and
  • communicating your findings in easy-to-digest reports to colleagues, management, and customers.


How Data Analytics Influences Business Decisions

According to a 2020 NewVantage Partners report, 64.8% of Fortune 1000 companies surveyed have invested at least $50 million into their business analytics efforts, and 91.5% attempted to implement artificial intelligence (AI)-based technologies in some form. While these figures appear to illustrate progress, the other side of the coin is only 14.6% of all responding businesses used these technologies across their operations.

Beyond the technologies and capabilities themselves, making accurate decisions based on facts and past performance remains at the core of business analytics. As the counterpart to this, decisions relying on gut instinct (or, until roughly a decade ago, limited data) result in costly investments, be it strictly in terms of money or the hours put into developing new initiatives that go nowhere.

Within this general framework, the insights gleaned ultimately help optimize and streamline business processes, eliminating any estimates and grey areas in the process. Thus, organization-wide optimization may encompass:

  • shaping and evaluating future company decisions based on the performance of past initiatives or market trends;
  • examining individual departments’ performance within an organization and influencing their growth efforts;
  • monitoring employees’ performance and productivity;
  • determining current and future staffing needs and the market skills needed to perform these roles effectively;
  • assessing and predicting how well potential investments will perform;
  • identifying demand for a particular product or service based on market trends and consumer behavior;
  • scheduling release dates for new products and media;
  • evaluating product sales by location, and using that information to meet future customer demands;
  • creating optimal logistics routes for shipping and delivering merchandise;
  • making product recommendations based on customers’ past search habits;
  • gathering data from vehicles and equipment to improve future performance; and
  • identifying potential growth opportunities for a business, and how these scenarios could play out.

Beyond these more broad concepts, here are how some of these scenarios may unfold:

  • Amazon.com turns sales data into insights by analyzing millions of purchases to find customers like you and predict products you might buy.
  • General Electric can predict in advance from its sensor data when engine maintenance is needed.
  • Based on a survey you filled out, Disney can alert its servers, via your MagicBand, that you prefer a booth to a table and that your favorite character is Minnie Mouse. When you arrive, you are seated at a booth, and Minnie makes it a point to visit you at lunch.

In all three cases, business analytics is being used to better serve the customer.

Business Intelligence vs. Data Analytics vs. Data Science

For professionals using AI, identifying patterns isn’t quite as simple as applying a formula and reading the results. Instead, the process entails:

  • aggregating the data, as more than one structured and unstructured source may be involved;
  • data mining, or classifying the data and identifying trends;
  • forecasting, or using the results of data mining to influence future business decisions; and
  • data visualization, or the presentation of data mining and forecasting into an accessible format that non-technical professionals can understand.

No single professional goes through all of these tasks. Rather, they’re divided up amongst three distinct roles that, while many use their titles interchangeably, have separate responsibilities. Aggregation frequently falls on business intelligence professionals, who examine a multitude of data sources and then clean it up and package it for analysis. Generally, business intelligence professionals spend their days monitoring the amount and variety of data coming in and organize it into reports or dashboards.

The business analytics professional—sometimes called a data analyst—then approaches the data from a higher level, examining it for patterns, making it usable, creating models from it, and finding ways in which these numbers and strings of text can improve department or business processes or make future financial or performance projections.

The data scientist, meanwhile, is the senior-most member of this group. This professional often acts like an investigator, searching for ways business processes could be improved and then backing up any proposals or solutions with data. This role, requiring more technical and computer-science know-how, involves manipulating and creating scenarios based on data and using these insights to develop supporting visualizations.

Demand for Business Analytics Professionals

Information technology is now pervasive in society. For example, cell phones, sensors, retail scanners, and the internet generate massive amounts of data that are of potential value to businesses. We truly live in the age of data, and the amount collected will only continue to grow. The information available is only valuable if it can be converted into insights.

However, there is a shortage of well-trained business analytics professionals. According to McKinsey Global Institute’s “Big data: The next frontier for innovation, competition, and productivity” report, “[B]y 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills, as well as 1.5 million managers and analysts with the know-how to use the analysis of Big Data to make effective decisions.”

Across all industries, opportunities for business analysts have exploded, as major organizations have adopted data-driven and technology-focused approaches. Demand, based on estimates from the Bureau of Labor Statistics, will only increase between 2018 and 2028:

  • Management Analysts, who seek to make organizations more efficient, will see 14% growth.
  • Operations Research Analysts, called upon to solve challenging organizational issues with data, will see 26% more positions available.
  • Market Research Analysts, who examine market conditions to forecast demand for products and services, will see 20% more growth.
  • Financial Analysts, who provide guidance regarding investments and other financial decisions, are expected to see 6% more openings.

Beyond these roles, other examples of business analytics careers at today’s leading employers include:

  • Management Analyst/Consultant
  • Data Analyst/Scientist
  • Business Intelligence Analyst
  • Program and Marketing Managers
  • Big Data Analytics Specialist
  • Research Analyst
  • Manager of Services or Manufacturing Operations
  • Business Intelligence and Performance Management Consultants
  • Pricing and Revenue Optimization Analyst

Because all types of organizations are now harnessing the power of Big Data, several industries need business analytics professionals, including healthcare, marketing, logistics, ecommerce, finance, food service and restaurants, entertainment, professional sports, and even casinos.

Currently, 85 percent of the positions in business analytics require an advanced degree, with 75 percent specifying an MS degree as an educational requirement. To prepare for these careers, Wake Forest Master of Science in Business Analytics (MSBA) graduates develop the deep quantitative capabilities and technical expertise needed to translate technical data into actionable insights, creating business value and delivering impact in a variety of career settings.

Learn More About Business Analytics and Wake Forest’s MSBA Degree

Considering the growth of business analytics, professionals with math, computer science, statistics, and analysis backgrounds are optimally positioned for the next stage of their career. See if the Master of Science in Business Analytics degree is a good fit for your goals, or request more information about our program today.