Type of Business Analytics 

There are four types of business analytics, each increasingly complex and closer to achieving real-time and future situation insight application. These analytics types are usually implemented in stages, starting with the simplest, though one type is not more important than another as all are interrelated.

The following BA examples provide insight into the roles of each type in the analytics process. By leveraging these four types of analytics, big data can be dissected, absorbed, and used to create solutions for many of the biggest challenges facing businesses today.

  • Descriptive Analytics

    Descriptive analytics describes or summarizes a business’s existing data to get a picture of what has happened in the past or is happening currently. It is the simplest form of analytics and employs data aggregation and mining techniques. This type of business analytics applies descriptive statistics to existing data to make it more accessible to members of an organization, from investors and shareholders to marketing executives and sales managers.

    Descriptive analytics can help identify strengths and weaknesses and provide insight into customer behavior. Strategies can then be developed and deployed in the areas of targeted marketing and service improvement, albeit at a more basic level than if more complex diagnostic procedures were used. The most common physical product of descriptive analysis is a report heavy with visual statistical aids.

  • Diagnostic Analytics

    Diagnostic analytics shifts from the “what” of past and current events to “how” and “why,” focusing on past performance to determine which factors influence trends. This type of business analytics employs techniques such as drill-down, data discovery, data mining, and correlations to uncover the root causes of events.

    Diagnostic analytics uses probabilities, likelihoods, and the distribution of outcomes to understand why events may occur and employs techniques including attribute importance, sensitivity analysis, and training algorithms for classification and regression. However, diagnostic analysis has limited ability to provide actionable insights, delivering correlation results as opposed to confirmed causation. The most common physical product of diagnostic analysis is a business dashboard.

  • Predictive Analytics

    Predictive analytics forecasts the possibility of future events using statistical models and machine learning techniques. This type of business analytics builds on descriptive analytics results to devise models that can extrapolate the likelihood of select outcomes. Machine learning experts and trained data scientists are typically employed to run predictive analysis using learning algorithms and statistical models, enabling a higher level of predictive accuracy than is achievable by business intelligence alone.

    A common application of predictive analytics is sentiment analysis. Existing text data can be collected from social media to provide a comprehensive picture of opinions held by a user. This data can be analyzed to predict their sentiment towards a new subject (positive, negative, neutral). The most common physical product of predictive analysis is a detailed report used to support complex forecasts in sales and marketing.

  • Prescriptive Analytics

    Prescriptive analytics goes a step beyond predictive analytics, providing recommendations for next best actions and allowing potential manipulation of events to drive better outcomes. This type of business analytics is capable of not only suggesting all favorable outcomes according to a specified course of action, but recommending specific actions to deliver the most desired result. Prescriptive analytics relies on a strong feedback system and constant iterative analysis and testing to continually learn more about the relationships between different actions and outcomes.

    One of the most common uses of prescriptive analytics is the creation of recommendation engines, which strive to match options to a consumer’s real-time needs. The key to effective prescriptive analysis is the emergence of deep learning and complex neural networks, which can micro-segment data across multiple parameters and timelines simultaneously. The most common physical product of prescriptive analysis is a focused recommendation for next best actions, which can be applied to clearly identified business goals.

    These four different types of analytics may be implemented sequentially, but there is no mandate. In many scenarios, organizations may jump directly from descriptive to prescriptive analytics thanks to artificial intelligence, which streamlines the process.