Why Is Business Analytics Important?

Business analytics provides the system through which companies leverage existing data to produce insights that improve outcomes. Whether reviewing data from the marketing department to identify potential advertising strategies or mining sales data to gather insights on the promise of various markets, businesses use analytics to drive informed decision-making processes. In a world where companies increasingly rely on big data and artificial intelligence to inform business decisions and improve customer experience, job seekers with business analytics skills enjoy a decided advantage in the business job market.

Why is business analytics important?

According to a MicroStrategy study, 60 percent of companies globally rely on data analytics to boost cost and process efficiencies; 57 percent use analytics-driven insights to inform strategy and change management. 52 percent agree that data analysis is essential to improving overall financial performance.

WIth more than seven out of every 10 companies anticipated to invest in data analytics in the coming years, the role of business analytics in making informed decisions, overseeing business operations, and predicting future outcomes should continue to grow. Business analytics metrics deliver concrete, valuable actionable insights businesses ignore at their own peril. Understanding how to wield analytical tools to examine historical data and optimize outcomes is an in-demand skill set for which demand should only grow.

Definition of business analytics

According to Harvard Business School, business analytics involves “using quantitative methods to derive meaning from data to make informed business decisions.” Experts divide business analytics into four categories: descriptive, diagnostic, predictive, and prescriptive analytics.

• Descriptive business analysis: Descriptive analytics focuses on using historical and current data to seek out trends and causations. Many professionals use this method to effectively identify and communicate how things change over time. For example, descriptive analytics offers a streamlined model for demonstrating how web traffic and engagement ebb and flow over time.
• Diagnostic business analysis: Diagnostic analytics looks to identify correlations that explain why something happened. For instance, an analyst may use the diagnostic model to identify why a product or service suddenly experienced increased demand. Diagnostic analytics goes further than descriptive by digging into correlation and causation rather than simply confirming that a trend or change exists.
• Predictive business analysis: Predictive analysis builds on the work of descriptive and diagnostic analytics to identify future trends and shifts. Whether conducted manually or via an algorithm, predictive models use existing data from individual businesses as well as data about the broader industry to forecast future trends. Predictive analysis plays a vital role in strategic planning in such diverse areas as cash flow, consumer behavior, staffing needs, and inventory.
• Prescriptive business analysis: Prescriptive business analytic seeks answers on how to move forward. While other models provide information that can inform decision-making, prescriptive models actually make recommendations derived from massive databases. For example, a prescriptive model can help identify the strongest leads for the sales department based on factors such as search engagement, time spent on pages, and interactions with email newsletters.

Examples of business analytics in action

Data analytics has transformed the global business landscape in recent years, creating a system for processing large volumes of data, providing valuable intel that helps companies gain competitive advantage. But what does business analytics look like on a day-to-day basis and how do companies effectively deploy these tools?

At Netflix, descriptive analytics helps engineers identify trending shows and movies based on users’ platform behavior. By tracking the popularity of individual titles and viewing habits, Netflix can highlight popular options on the home screen. When it comes to developing original content, Netflix can also use these insights to create programming its viewers will value.

The dinner-kit-delivery company Blue Apron uses predictive analytics to predict upcoming purchases to avoid unused inventory and food waste. Engineers created several algorithms that help strategic planners better understand customer behavior based on recipe availability, ordering habits, and times of year. A recent report concluded that the difference between the company’s predictions and actual demand was less than six percent.

Business analytics also has life-saving applications. Harvard’s Wyss Institute partnered with the KeepSmilin4Abbie Foundation to develop technology to detect anaphylactic allergic reactions far more quickly than humans can. Using predictive analytics, scientists developed a piece of wearable technology that detects physiological signs, predicts level of severity, and notifies the wearer. Simultaneously, wearers receive a dose of epinephrine based on predicted allergic reaction.

What is a Master of Science in Business Analytics?

A Master of Science in Business Analytics is an advanced degree that provides required training for managerial roles. Interdisciplinary in nature, the MSBA combines computer science, information technology, and mathematics with traditional business skills. The degree is available both online and in-person.

How long does it take to earn a Master of Science in Business Analytics?

Timelines for M.S. in business analytics programs vary from school to school, with many institutions offering both part-time and full-time learning pathways. At Stevens Institute of Technology, students typically complete the school’s online program of 36 credits within two years.

What are the prerequisites for a Master of Science in Business Analytics?

Prerequisites for admission vary among programs. That said, it’s not uncommon for schools to look for candidates with an academic background in statistics, computer science, and calculus. While learners do not necessarily need full degrees in these areas, institutions will likely require some coursework in these areas before candidates can begin degree work. Some schools also look for candidates with existing work experience rather than recent undergraduates.

Core curriculum

Core courses learners should expect include data mining, visualization and storytelling, predictive modeling and decision-making, data engineering, optimization and process analytics, cognitive computing, and big data technologies. Students should carefully review different core curricula to make sure they find the one that relates most closely to their interests.

Electives

Electives enable students to customize the program to their future professional goals. Options include business forecasting using time series methods, advanced text analytics, advanced predictive models, and advanced analytics using SAS.

Specializations

In addition to core coursework, some schools offer specializations to help learners focus their studies. At Butler University, students can choose from healthcare analytics or business analytics. Other schools offer specializations in financial analytics, human resource management analytics, supply chain analytics, and information management and coordination analytics.

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