What is Business Analytics?

According to Harvard Business School, there are four primary types of business analytics: descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics.

Descriptive analytics: what happened?

Descriptive analytics entails analyzing data to understand what has happened in the past. The most fundamental type of business analytics, descriptive analytics often only requires software like Excel or a database-querying language like Structured Query Language (SQL) to wrangle larger data sets and identify important trends.

How’s it being used: Every business uses descriptive analytics at some point in time — it would be impossible to measure growth otherwise. While the analytics behind Goldman Sachs’ decision to lay off over 3000 employees at the beginning of 2023 were no doubt complicated, much of the impetus arose from an insight arrived at by descriptive analytics: a recent downturn in investment banking compared to previous performance. 

Diagnostic analytics: why did something happen?

Diagnostic analytics entails analyzing data to understand why something occurred: its root cause. For this reason, diagnostic analytics is sometimes called root cause analysis. Diagnostic analytics generally requires a business analyst to dive deeper into a data set — or even reference external data sets — to develop and test hypotheses about what is driving a certain pattern or trend. Often, techniques like regression will be used to determine whether a relationship exists between two variables, which can point to a potential causal relationship.

How it’s being used: Diagnostic analytics is employed whenever a company wants to ascertain or demonstrate causation of a phenomenon. One area this can be particularly useful is in understanding the motivations behind customer behavior. When Spotify seeks to characterize listening history within larger listening habits to make their product more attractive to advertisers, they are employing diagnostic analytics to do so.

Predictive analytics: what will happen in the future?

Predictive analytics entails analyzing historical data in order to predict future events, either through more rudimentary methods like regression or advanced statistical modeling and machine learning . Looking into the future can be particularly useful for businesses wanting to make decisions about entering new markets, initiate a merger or acquisition, manage their supply-chain or production schedule, or forecast earnings for investors.

How it’s being used: Predictive analytics are a crucial way for businesses to mitigate risk when embarking on new initiatives, like entering a new market or launching a new product. Skullcandy, a headphones manufacturer, utilizes predictive analytics to estimate return rates of new headphone units, which gives them insight into potential future costs.

Prescriptive analytics: what actions should be taken?

Prescriptive analytics entail analyzing data to determine the best future course of action. As with predictive analytics, in many cases machine learning algorithms are employed to analyze big data sets and output — or even automate — optimal actions.

How it’s being used: Prescriptive analytics are used in industries as varied as manufacturing, finance, retail, and media. Venture capital firm Hone Capital has recently gained notoriety for using a prescriptive machine learning model that outputs an investment recommendation for each deal the firm is considering, with positive results so far.

When researching business analytics, there’s a good chance you’ll run into the term business intelligence. At their core, both utilize analytics to produce information that can support businesses, but beyond that there can be disagreement as to how exactly they differ. Some draw a distinction between the work products of each. Business analytics produces insights that more directly support decision-making, they argue, while business intelligence focuses on producing informational resources like reports and dashboards.

Others consider business intelligence to be focused on past events — the domain of descriptive and diagnostic analytics — with business analytics instead focused on the future through predictive and prescriptive analytics. Finally, others find no difference between the two, considering them simply synonyms referring to the analysis of data to produce business insight. Ultimately, you’ll have to use context and content to determine how each term is being used in a particular situation.

Business analytics is also frequently confused with data analytics, and for good reason: in many cases it’s difficult to distinguish between the work a business analyst or a business intelligence analyst does and the work a data analyst does, especially at a smaller company where employees have greater range of responsibility.

At larger companies, the distinction between business analytics and data analytics is usually one of emphasis rather than essence. At their core, both are concerned with employing data analytics to support a business, but business analytics will more often emphasize bigger-picture business concerns while data analytics will emphasize more siloed or operational concerns.

To extract actionable business insights from big data sets, business analytics professionals must be proficient in computer science, statistical analysis, data management, data visualization, and business fundamentals, in addition to assorted soft skills.

Computer science

Business analytics professionals must at least be able to run Excel formulas and script in SQL. For more advanced analytics, however, they will need to be able to write machine learning algorithms and train machine learning models using a programming language like Python or R. They’ll also need to be versed in machine learning and deep learning libraries like Pandas or Tensorflow.

Statistical analysis

Statistical techniques essential for business analytics include regression analysis, factor analysis, optimization, cohort analysis, cluster analysis, and time-series analysis. You can find descriptions of each of these with examples in our data analytics explainer.

Data management

Business analytics professionals must be competent across the entire data pipeline, from data mining, to data wrangling and data cleansing, to data processing, to data storage.

Data visualization

Data visualization skills are crucial for business analytics professionals to be able to compellingly communicate insights to stakeholders. Tableau is a popular business analytics tool for creating graphs and other visualizations.

Business fundamentals

To be able to develop and implement useful analytics projects and communicate findings, business analysts must be conversant in business basics, including macro- and microeconomics, accounting, finance, M&A, and industry-specific fundamentals.

Soft skills

Crucial soft skills for a business analytics professional include critical thinking, communication, leadership, and data storytelling.