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:
- 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.
- 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.
- 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.
- 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.
- 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?
- 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.
- Driving AP Success With Automation Part 3: How to Save Time and Money While Increasing Compliance
- Driving AP Success With Automation Part 2: How to Create More Efficient Processes With AP Automation
- VAT IT Partners With Emburse to Help Companies Save 27% on Expenses
- Driving Success With Automation Part 1: 4 Common AP Management Bottlenecks
- The Future of Finance: 5 Predictions For Digital Transformation in 2022 And Beyond
Our choice of Chrome River EXPENSE was made in part due to the very user-friendly interface, easy configurability, and the clear commitment to impactful customer service – all aspects in which Chrome River was the clear winner. While Chrome River is not as large as some of the other vendors we considered, we found that to be a benefit and our due diligence showed that it could support us as well as any large players in the space, along with a personalized level of customer care.
We are excited to be able to enforce much more stringent compliance to our expense guidelines and significantly enhance our expense reporting and analytics. By automating these processes, we will be able to free up AP time formerly spent on manual administrative tasks, and enhance the role by being much more strategic.