Having the backing of analytics is the cornerstone of everything HealthPRO Heritage does. In order to progress partnerships with patient outcomes, the value of analytics has never been more clear. Learn how not all analytics are created equal from global researcher and analytics leader Gartner below.
There are four types of analytics: Descriptive, Diagnostic, Predictive, and Prescriptive. The chart below outlines the levels of these four categories. It compares the amount of value-added to an organization versus the complexity it takes to implement.
The idea is that you should start with the easiest to implement, Descriptive Analytics. In this blog, we will review the four analytics types and an example of their use cases, and how they all work together.
Image from Gartner.
The baseline and the place that all organizations should start is with Descriptive Analytics. This type of analytics is when an assessment of data, often historical, is used to answer the fundamental question “what happened?”.
It looks at the events of the past and tries to identify specific patterns within the data. When someone refers to traditional business intelligence, they are often describing Descriptive Analytics.
The next step in analytics is Diagnostic, a form of advanced analytics that examines data or content to answer the question, “Why did it happen?”. It is characterized by techniques such as drill-down, data discovery, data mining, and correlations.
This is the second step as you must first understand what happened to be able to identify why it happened. Typically, once an organization achieves descriptive insights, it can apply diagnostics with a little more work.
Once an organization can effectively understand what occurred and why it happened, they can move up to the next tier in analytics, Predictive. Predictive Analytics is another type of advanced analytics that looks to use data and information to answer the question “What is likely to happen?”.
The step between Predictive Analytics and Diagnostics Analytics is a big one. Predictive Analytics involves techniques such as regression analysis, forecasting, multivariate statistics, pattern matching, predictive modeling, and forecasting.
These techniques are harder for organizations to accomplish as they require large amounts of high-quality data. Additionally, these techniques require a deep understanding of statistics and programming languages such as R and Python.
Many organizations may not have access to the expertise needed internally to effectively implement a predictive model.
The final level and most advanced level of analytics is Prescriptive.
Prescriptive Analytics is a method of analytics that analyzes data to answer the question “What should be done?”. This type of analytics is characterized by techniques such as graph analysis, simulation, complex event processing, neural networks, recommendation engines, heuristics, and machine learning.
Contact HealthPRO Heritage to learn more about how the industry’s newest tool, CMI PRO Prescriptive Analytics will take your CMI strategy & success to the next level.