Today, most organizations emphasize data to drive business decisions, and rightfully so. But data alone is not the goal. Facts and figures are meaningless if you can’t gain valuable insights that lead to more-informed actions.
Analytics solutions offer a convenient way to leverage business data. But the number of solutions on the market can be daunting—and many may seem to cover a different category of analytics. How can organizations make sense of it all? Start by understanding the different types of analytics, including descriptive, diagnostic, predictive, and prescriptive analytics.
What are each of these categories? Are they related? In short, they are all forms of data analytics, but each use the data to answer different questions. At a high level:
- Descriptive Analytics tells you what happened in the past.
- Diagnostic Analytics helps you understand why something happened in the past.
- Predictive Analytics predicts what is most likely to happen in the future.
- Prescriptive Analytics recommends actions you can take to affect those outcomes.
Let’s dive into each type of analytics and put them in context.
Descriptive analytics looks at data statistically to tell you what happened in the past. Descriptive analytics helps a business understand how it is performing by providing context to help stakeholders interpret information. This can be in the form of data visualizations like graphs, charts, reports, and dashboards.
How can descriptive analytics help in the real world? In a healthcare setting, for instance, say that an unusually high number of people are admitted to the emergency room in a short period of time. Descriptive analytics tells you that this is happening and provides real-time data with all the corresponding statistics (date of occurrence, volume, patient details, etc.).
Diagnostic analytics takes descriptive data a step further and provides deeper analysis to answer the question: Why did this happen? Often, diagnostic analysis is referred to as root cause analysis. This includes using processes such as data discovery, data mining, and drill down and drill through.
In the healthcare example mentioned earlier, diagnostic analytics would explore the data and make correlations. For instance, it may help you determine that all of the patients’ symptoms—high fever, dry cough, and fatigue—point to the same infectious agent. You now have an explanation for the sudden spike in volume at the ER.
Predictive analytics takes historical data and feeds it into a machine learning model that considers key trends and patterns. The model is then applied to current data to predict what will happen next.
Back in our hospital example, predictive analytics may forecast a surge in patients admitted to the ER in the next several weeks. Based on patterns in the data, the illness is spreading at a rapid rate.
Prescriptive analytics takes predictive data to the next level. Now that you have an idea of what will likely happen in the future, what should you do? It suggests various courses of action and outlines what the potential implications would be for each.
Back to our hospital example: now that you know the illness is spreading, the prescriptive analytics tool may suggest that you increase the number of staff on hand to adequately treat the influx of patients.
In summary: Both descriptive analytics and diagnostic analytics look to the past to explain what happened and why it happened. Predictive analytics and prescriptive analytics use historical data to forecast what will happen in the future and what actions you can take to affect those outcomes. Forward-thinking organizations use a variety of analytics together to make smart decisions that help your business—or in the case of our hospital example, save lives.
Learn more about predictive analytics and what it can do for your business in the on-demand webinar: The Complete Predictive Analytics Lifecycle for Application Teams.