We’ve written numerous times on this blog about the challenges, opportunities, and best practices for today’s Chief Data Officers (CDOs). In this post, we’ll be looking specifically at key priorities for data heads and why those hoping to be successful in the role must evangelize AI, machine learning (ML), and other emerging technologies throughout their organization.
Priority 1: Promote Data Science
A CDO must be an evangelist, but they also need to be a realist. With a significant percentage of all data science projects ending in failure, the data lead bears responsibility for picking the projects most likely to have the lowest risk of failing. In addition, the CDO must decide how to allocate resources to these projects. Another major responsibility is engaging with the various business units to understand their needs for the coming years and how AI and ML can be expanded throughout the enterprise to support these goals.
Priority 2: Pick Your Projects
Prioritizing projects based upon their likelihood of success is important, but that’s not the only consideration that goes into deciding which projects to green-light. CDOs must also determine which new projects are integral to the company’s growth, which will improve efficiency, and which are merely “nice to haves.”
If a project is mission-critical and will advance the organization, it’s important to build out an internal team to tackle it. The goal is to move activities from the experimental stage as quickly as possible so that the project can begin adding to the company’s bottom line. For projects that improve efficiency, the focus should be on procuring the right tools and platforms that will achieve these goals in complement with existing resources. And for the “nice to haves,” the data lead should determine whether bandwidth exists to handle these projects along with other higher-priority items or whether it’s better to wait until resources are freed up.
Priority 3: Prioritize People Skills
Chances are, you simply don’t have enough good data scientists to handle all the work your organization hopes to complete. One way to address this gap is to implement training programs for staff within various business units to give them the skills required to deliver projects at the business level. It’s also crucial to educate these groups on the value of AI and ML while also level-setting that many of these projects come with uncertainty and that the desired outcome doesn’t always come to pass.
For a more in-depth read on these priorities, check out this recent TDWI article.