According to a recent survey, nearly 100 percent of the companies surveyed reported investing in AI, machine learning, and automation. However, that’s only the first step. For these organizations to derive business value from these investments, they must work toward further integrating analytics into their culture and accelerating data-driven insights so that decisions can be made in a matter of minutes rather than days.

As companies embark upon this next phase of analytics growth, there are some important questions to consider, which we have outlined below:

What are the business problems you plan to solve?

This question sounds like a simple one, but there are times when a company’s stakeholders aren’t closest to the problem and, therefore, not best suited to answer it. That’s why it’s important to go to the source and ensure that those most familiar with the business issue at hand articulate it and what they hope to achieve through the project. From there, leaders can better understand the root causes and drivers and set metrics around outcomes.

How many people are you investing in?

Once the goals are established, companies must then determine how to allocate resources. If the business issue requires highly analytical skills, perhaps a small investment in a data-science-specific team is appropriate. But generally speaking, arming a broad base of knowledge workers with analytics skills is a more effective strategy for driving data maturity throughout the organization. For more on the importance of these “citizen data scientists,” check out this previous APEX of Innovation post.

Who will lead the effort?

Doing digital transformation right is not only a matter of technology; it’s also a change management process. Companies need to choose a project leader who will drive the effort across the organization while simultaneously helping implement best practices and encouraging innovation. Another critical consideration here is thinking through how employees may perceive the change, how it will impact their job on a daily basis, and any changes that should be made to ensure this process goes smoothly.

How do you measure success?

What are you hoping to achieve from your data science project? Is it top-line growth, improved efficiency, or an increase in data skills among your knowledge workers? Whatever the desired outcome, it’s essential to be clear on the return you’re expecting from your investment—and how you will measure whether or not it’s been successful. Whenever possible, set high-level goals that can deliver a more transformative impact across the company rather than a single project in one area.

Head over to Solutions Review for more on the above considerations and how they can set your analytics project up for success.