The “what happened stage” of analysis. It’s estimated that companies use only a tiny fraction of the data available to them. So, it goes without saying that they’re not fully understanding the events that have shaped past performance. Descriptive analytics is the first stage in our analytical framework, and it’s critically important to establish a strong foundation here. To start on your digital transformation journey, we’ll connect and organize disparate data silos. With your data integrated, we can now uncover new relationships and insights into past performance.

Why did it happen? Often the answer is not right on the surface and quickly recognizable. Big data and powerful analytical tools, as well as insightful data visualizations, allow us to better identify the root causes that influenced a past outcome. Understanding “the why” sets up our capabilities for predictive modeling.

With a deeper understanding of past performance, we can now develop machine learning models that predict future outcomes. As these models consume new and previously unseen data, their predictive powers become sharper, stronger, and wider. Predictive analytics encompasses a variety of statistical techniques from data mining, predictive modeling, and machine learning to analyze massive amounts of data at the most granular level.

With prescriptive analytics, we leverage our new predictive models to automatically evaluate different courses of action, and then recommend the optimal path forward. Prescriptive analytics moves beyond predictions to identify the specific actions that will most likely achieve the desired outcome. Additionally, in this stage, we establish the framework for analyzing the interrelated effects of decisions across your organization, with the end-game being true business automation.