6 Keys To Measuring Your Predictive Analytics Efforts

With the convergence of Big Data, the Internet of Things and the quest for analytics from different departments, CIOs are being forced into the additional role of coordinating the various data experiments occurring at any given time in an organization. 

A recent presentation to the Society for Information Management's Advanced Practices Council identified a 6-step process that will help manage multiple predictive analytics projects: 

1. Design - Understand the length of the predictive analytics effort and the metrics for the success or failure of it; know the frequency of data collection. For example, your sales team might be running a test of new sales tactics. Know how long the project will last, what sales team members will be involved and how the success or failure of the analytics effort will be determined.
2. Embed - Be sure data is collected properly and that results of the data analysis are shared with the appropriate audience in a timely fashion. Following the sales example, share sales data analytics with your inside and outside sales reps who are participating in the effort.
3. Empower - Train employees how and when to use results from the analytics effort. If predictive sales data identifies a customer that can be upsold, be sure your sales team tracks how they follow up with that customer for a secondary purchase.
4. Measure - Build dashboards for monitoring real=time progress and enable alerts that can be sent to project managers if the effort moves beyond expected metrics. All predictive efforts need a single point of truth - that could be a dashboard or even a weekly or quarterly report.
5. Evaluate - Did the predictive effort generate the intended results? Was it worth the investment of resources? How long will the results of the effort be valid? In our sales example, were additional sales or opportunities generated as a result of the new sales tactics?
6. Re-Target - Is a subsequent analytics effort required? How will the results of the current effort impact revenue and business processes in your organization moving forward? If the analysis of your sales approach indicates effectiveness, what are the next steps or next predictive test you can run to see how this initial effectiveness can be enhanced.

Without a framework that quantifies success or failure of predictive analytics efforts, these efforts will simply remain discrete efforts without any alignment to overall organizational effectiveness or success.