Predictive Analytics Simplified

Bruce Kolodziej's picture
 By | June 10, 2015
in Advanced Analytics, Business Intelligence, Business Intelligence, Predictive Analytics
June 10, 2015

Predictive Analytics solutions allow you to predict trends and act on opportunities before they manifest.

Every day I meet with organizations with a need to employ predictive analytics for better business decisions across a wide variety of industries and use cases. These include financial services, healthcare, retail, manufacturing, higher education, government agencies, and insurance customers all working towards increasing revenue and reducing costs and risks. 

To achieve positive results from predictive analytics projects, there are a number of aspects to keep in mind to ensure the results are both accurate and integrated within business processes. Here’s an overview of the key aspects including creation of forward-looking views on business decisions, driving bottom-line targeted projects, and the platform approach for success:

  • Predictive analytics provides insights into relationships within an organization's data, but the real value is getting a forward-looking view into future behaviors. Here, we are able to make proactive rather than reactive decisions, as the results provide direction based on these likely behaviors. Armed with this information, we can capitalize on positive activities and reduce negative ones.
  • Focus on projects that will impact the bottom line by generating profits or reducing costs or risks. Exploring and mining data is certainly valuable to discover unknown patterns, but we don't want to stop there. We need to apply the insights found to create real business value that translates to dollars. These are the projects that get exposure and approval and enable an organization to capitalize on the effort.
  • Taking a broad view of the process and holistic approach to the solution are key points to keep in mind. This means including data access and data preparation along with operationalizing the predictive results, both of which complement creation and testing of the actual model. Studies show data access and preparation are 60 to 70 percent of the entire effort. What completes the picture is ensuring options are available for deploying the predictive results for action. This can only be achieved when a platform approach is used for predictive analytics, and not a one-off tool approach.

Be sure to check out my most recent blog post, "Strive for Impact, Not Just Insight," where I dive deeper into these key points and explore additional topics within predictive analytics.

For more information, register for our upcoming June 24th webinar, Summer Shorts: Introduction to Predictive Analytics. Also, check out a demonstration on how to build and deploy a predictive model in our Daytime Demo on predictive analytics with WebFOCUS RStat scheduled for July 15th.