Expectations are rising for CFOs and the finance function to become strategic partners to their organization. And in a world with more data than ever, the speed with which businesses must digest data and adopt new technologies to use that data is crucial to staying competitive. 

Predictive and prescriptive analytics still have room to grow at companies across the nation, but it’s not purely the price of technology that’s causing a lull in data maturity progress. In fact, the answer lies in building a culture that’s bought in and motivated to transform.   

Stuck at diagnostics

Data is—and should be—the foundation for business decisions and strategic planning. There are four key levels of data analytics—descriptive, diagnostic, predictive and prescriptive—each of which builds on the last to create a deeper understanding of where the business has been, where it’s going and how to get there. It’s not enough for businesses to understand what happened and why. They need to understand what will happen and how to optimize that trajectory. 

For tomorrow’s finance function, being able to give real-time, actionable recommendations to leadership and individual departments will be table stakes. Yet only 40% of businesses currently deploy predictive analytics, according to Gartner research, and even fewer (26%) deploy prescriptive analytics. 

Why businesses are stalling on evolution of their data analytics

Managers that use strictly descriptive and diagnostic analytics take a manual approach in determining next steps. While very common, this can allow the introduction of bias and intuition to steer the company. For the finance function, and especially for CFOs, owning the evolution from hindsight to foresight by using predictive and prescriptive analytics is important for minimizing risks and finding new opportunities. 

But there are barriers to creating a data-driven environment, and finance functions are struggling to realize business value, even from record levels of data investment. This is for a number of reasons, including: 

• Poor data literacy
• Improper change management
• Little evidence of ROI for stakeholder buy-in

What CFOs need to better implement predictive and prescriptive analytics

Predictive and prescriptive analytics require two things: technology and people. But it’s the latter, the culture, that determines the usefulness of the technology.

Build buy-in with more thoughtful needs-based assessments

While machine learning technology may be the company’s first foray into delivering advanced forecasts and actionable recommendations, the strong ROI it can deliver is attracting more interest to it daily. However, for executives that don’t see immediate value to their organization, they may be incorrectly assessing tech investments. Too much too fast can lead to poor results, whereas businesses that invest gradually based on their needs will see faster results.

Consider how improved analytics align with your strategic vision and company culture, as well as how they can be deployed in various forms to optimize your investment. When you can prove their usefulness and acquire full buy-in, you’re more likely to develop a culture that’s ready and motivated to use the data.

Prioritize training to fill talent gaps

Preparing your finance function for the future requires expertise in data analytics and integration, emerging technologies and, most obviously, advanced forecasting and scenario planning. While finance professionals don’t need to become data scientists, an understanding of where the data comes from and how predictive or prescriptive tools make their conclusions is essential. 

For financial leadership, this also translates to skills in leading digital transformation, operational change and professional development and training. For some businesses, this may require specialized fractional expertise, especially in newer areas, such as AI and machine learning implementation. 

When your team understands the tools they’re using, and leadership is given realistic expectations around what the technology can or can’t do, you can build a culture that’s ready to master predictive and prescriptive analytics.