In 2020, everyone is collecting as much data as possible. However, pure data isn’t as helpful as it could be. You can’t act on a table of data without first processing it into actionable insights. Most companies achieve this transformation through an analytics or business intelligence solution that enables raw data to be used to improve business decisions and leads to better, data-driven strategies.

Extracting information from data is essential, so it’s valuable to understand the different kinds of data analytics. The most common categorizations used are Descriptive, Predictive and Prescriptive. Here’s a quick overview of each category, and what they can do for your business:


  • Descriptive Analytics answer the question “What has happened?” It looks at data to tell you what happened in the past. It gives insight into how a business has been performing and can give context to current performance. Descriptive Analytics is a great rear view mirror and allows you to learn from past behaviors.
  • Let’s use a contact center as an example: Descriptive Analytics tell you what your service levels are, how many calls are waiting in the queue, what state each agent is currently in, and other current and past statistics regarding the contact center. It provides you with real-time and historical data describing the state of the center.


  • Predictive Analytics goes a step further and uses historical data to create predictive models. It answers the question “What could happen?” Machine learning models are used to examine the past and predict the future. This enables the business to learn from past behaviors and trends as well as how they might influence future outcomes.
  • Using the same contact center example: Predictive Analytics uses the previously mentioned Descriptive Analytics and feeds it into machine learning models. Management can now use past and current data to create “what-if” scenarios to see how specific changes could affect the contact center. In addition, Predictive Analytics could also alert management to potential future volume spikes based on past trends.


  • Prescriptive Analytics is the next level. It answers the question “What should happen?” It uses optimization models and artificial intelligence to predict multiple futures and allow companies to assess possible outcomes based on their actions. Prescriptive Analytics makes recommendations for companies to change behaviors based on descriptive and Predictive Analytics.
  • Staying with the same example: Now that the Predictive Analytics has alerted the company to a future call volume spike, the user can apply Prescriptive Analytics to streamline scheduling. By building on descriptive and Predictive Analytics, an optimization model can use this data to determine the number of agents needed to handle this spike, while keeping payroll manageable.

As you can see, each category of analytics builds on the previous one. It is important to implement all three in order to obtain the most value and insight from the vast amounts of data the business is collecting. By using a variety of analytics together, your organization will make informed decisions and improve efficiencies.