We are currently putting a lot of energy into population health management, or what we call integrated care practices here in the UK.

The US has been practicing population health management for decades now, since accountable care organizations were first created, but as we drive toward integrated care at the NHS I have noticed that data and analytics team roles as they’re currently configured are unlikely to provide all the support that our integrated care practices will require. I see four roles that would benefit us on our journey. At least a few of these roles may be required in analytics departments regardless of industry. 

  1. Chief Population Health Officers. These professionals would act as integrators to translate what payers and clinicians are thinking into data analytics products, such as dashboards and reports. Their role would include understanding everything from the clinical front end to the system level to the data and analytics required to bring both together.
  2. Data Planners. Planners would bridge the current divide among technical, solution, and business architects and thereby reduce handovers and lost nuance. Although all three types of architects bring different skill sets, I feel the need for them to come together under one umbrella via a Data Planner to reduce handovers and lost nuance in messages.
  3. Data Evangelists. Evangelists would drive greater usage and scaling up of analytical products. We create thousands of dashboards and reports, but don’t currently give sufficient attention to how we are scaling and disseminating them. The unfortunate situation is that you sometimes spend a great deal of time, money and human resource to create a dashboard, then discover that only 50 or 100 people are using it. Evangelists would go about scaling dissemination from the onset, constantly engaging with users to work out what their needs. For example, How are we going to refine this product? Are you ready to receive this product? What do we need to do to make it happen? This link to users is, I believe, critical.
  4. Data Storytellers. These team members would work with internal and external customers to paint a story or vision of what they need and how they can be supported. One of the challenges we have in the analytics profession is understanding requirements. At times the client doesn’t know what they don’t know or need. The art of a data storyteller is being able to say, “These are the things the client needs. Let me help them paint that story or that vision. Let's align on that.” With this information I can go back to my data and analytics team and work out how I can support them. Analysts have traditionally shied away from this activity because it’s not the traditional number crunching or insight role, but I believe we need to start developing this group of professions. If we don't, I think analysts will get stuck in the back office. The time is right where analysts probably need to be leading the transformation, as opposed to just supporting it via numbers or insights.  

Why is all this important? Because creating and building the skills required for these roles and responsibilities will accelerate relevance. Relevance is critical to data analytics because people often struggle to see and appreciate what we create. These new roles also will accelerate the application and utilization of data tools all the way down to the level of the clinician. I for one would love to see our clinicians to start using some of the tools we create at a payer level, or nationally. So when they are looking at a diabetic patient, for example, they may say, “I’m meeting John. Here are his numbers. Wouldn’t it be interesting to see how many other Johns there are in my population? And once I have that data, what I can do about that?" By doing a better job at the center of our organization, we can provide these tools and encourage their use.

As the number of off-the-shelf solutions and the pace of automation decrease the relevance of traditional roles like performance analyst or data warehouse manager, I believe it will lead to a resurgence of new data team roles, skills, and career paths that will drive progress forward to the benefit of all.