By Jacob Sinex, Managing Director, Analytics & Informatics at Tegria

Lessons for the journey towards data-driven culture in healthcare organizations.

Since joining Tegria (via Bluetree) in 2015, I’ve had many conversations with healthcare organization data leaders and consumers regarding the state of their state when it comes to data, analytics, informatics, and data-research support. This will surprise no one, and I’m making up this statistic, but roughly 85% of those conversations involved some version of the statement “We want to do more with predictive-prescriptive-AI-ML-data magic, but it’s really hard.” In response, I always show them our trusty analytics maturity model, an amalgamation of maturity models that anyone can find on the internet. I’ll point at it and ask, “On this chart, where is your organization right now, on average?” It’s not important exactly how anyone answers, but it is important to see them self-evaluate against a model and hear them think through two key concepts:

  1. What do organizations want to accomplish?

  1. Do they have the building blocks and tools in place reach those goals?

In late Summer 2021, a focus group of healthcare executives, all CHIME members, was held and I was fortunate to be one of the two speakers from Tegria. We planned to wow the group with our amazing analytics maturity model, get their two thumbs up, and continue down the path to revolutionizing healthcare analytics and informatics.

What happened was much more interesting. Within an hour of conversation in that group – more so than in any webinar or seminar I could have attended using that time – I heard valuable perspectives from like-minded individuals who build data-driven organizations. Much of the discussion felt like a group moment of “What did you think you knew, but later found out you were wrong about?” – the Make Me Smart question.

I’ll share here a few insights from that conversation – along with some suggestions for how we should think about incorporating analytics maturity concepts into all levels of healthcare organizations.

Here’s What I Heard

  • Many healthcare executives in the discussion recognized the conceptual value of analytics maturity models, but they see past high-level models to the challenges of translating concepts into strategy and tactics. Data capabilities tend to be localized and highly variable within an organization and maturity models are usually presented as enterprise strategy.

  • The healthcare organizations (HCO) represented by the focus group may be hesitant to invest in maturing analytics because it’s difficult to find talent with the necessary skills to do so. In other words, they have a lot of people who can develop reports and not that many people who can think in terms of data marts, data models, data visualization, and data science.

  • To the focus group participants, wrangling data is a challenge; but the bigger challenge is creating a data-driven culture within an organization. Analytics and BI teams build a lot of “stuff” ̶ especially in organizations where their output is typically only one report per data request. It’s hard to get anyone to use any of it more than just once.

  • They expressed a common observation — investing in long-term analytics development can be a tough decision amid many immediately pressing concerns. Analytics, like anything an HCO could do, competes for resources, and must demonstrate value to the organization.

Here’s What I Think

Maturity Models

I used to think you could pick a point on a maturity where you’re at, pick another point further along the curve, and then chart a plan to get from point A to point B.

Rather than using this simplistic approach, I’ll now suggest that HCOs do the following:

  • Use maturity concepts to evaluate whether the organization has capabilities that can flex from ad-hoc data queries to prescriptive analytics. If a capability is missing within the organization, develop it, but expect the first try to be challenging and educational.
  • Use maturity concepts to help decide on the approach that is most appropriate for each opportunity. Many times, a quick data pull is all that’s needed, and you’ll already have the developer on your team who can complete the request in a few hours. Keep track of how many times that developer is writing one-off code to respond to similar requests. If the answer is “a lot” then build something for the long term.


As the Great Resignation continues, there is no easy answer to this one, especially in the short term. I suggest considering:

  • Increasing investments in upskilling your existing FTEs, developing talent pipelines through local colleges and universities, and being as open as possible to remote work and a remote-first culture. Many teams within Tegria and many of the highest performing HCO analytics teams that I know of are fully remote, extremely high performing, and great places to work.
  • Use Managed Services offerings to reduce routine maintenance burden and make it possible for FTEs to develop strategic capabilities and stretch their boundaries. Few people have the drive and interest to learn advanced data engineering, data science, and data visualization in their free time, but they’ll thrive in that role if provided the opportunity to learn those skills at work.

Data Culture

I’m excited about the possibilities here where data delivery-people can have a chance to be creative:

  • Build data and analytics into day-to-day job functions with as little friction as possible. Find out what meetings people are having. What data could they use in that meeting to make faster, better, more-informed decisions?
  • Fundamentally, analytics is a service to nudge incremental behavior adjustments and help people make slightly better decisions than they would have without useful data. Analytics teams within HCOs can think about marketing their services differently. Data consumers who use these services can be better at their jobs (and data delivery-people should make sure they do everything possible to make that happen).


There will always be 1,000+ other priorities in any organization. The trick to getting started is proposing a pilot opportunity that is small enough and potentially valuable enough so that the easiest response is “Okay, let’s try”:

  • Start small with pilot projects. Find “data nerds” within your organization, support them, and celebrate their successes. If you’re successful in helping these people succeed, the demand for analytics services within your organization will increase through word-of-mouth advertising.

What do you think about analytics maturity models and developing data-driven cultures? What’s your healthcare data version of “What did you think you knew but later found out you were wrong about?”

Reach out. We’d love to talk with you.