How Cleveland Clinic Made the Business Case to Invest in Data Governance for Analytics

Miller Pav
How Cleveland Clinic Made the Business Case to Invest in Data Governance for Analytics

When talking to customers about analytics, I am frequently asked about how to build the business case for data governance for analytics initiatives. That is a great question and my first response is that it is always easier to design it in up front than to try to retrofit it after the fact. But, let’s get back to the question.

Let’s face it, it is often hard to convince an organization to invest in good data management tools. Data governance can be an even harder sell, for a lot of different reasons. But the main objective is to get your management to understand the business value that data governance will deliver.

Cleveland Clinic’s business challenge

Cleveland Clinic is world leader in healthcare. A nonprofit, it is a multi-specialty academic medical center that integrates clinical and hospital care with research and education.  As one of the world’s largest hospitals, it runs 101 operating rooms (ORs) and seven hybrid surgical suites.  Their business challenge was to improve patient healthcare outcomes, while at the same time improving hospital efficiency. Specifically, they wanted the ability to forecast the demand they would need every day for each OR (pre-op support, medical specialists, assistants, supplies, etc.). They also needed to forecast all of the post-operative needs of the patients flowing though the ORs, such as post-operative recovery, medications, physical therapy, rehab, in-home assistance, etc.  All of this needed to be forecasted and optimized so that the correct resources would be available at the right time to ensure the best and most effective medical care for their patients.

Making the case for data governance

Chris Donovan, Executive Director, Enterprise Information Management & Analytics, and Andrew Proctor, Sr. Director, Business Intelligence spoke at Informatica World 2016 in May and here is how they described their journey: They had been effective at running their BI/reporting environment for several years.  Early on, they built dashboards to show the hospital management how well things were running. As Chris Donovan put it, We learned on the dashboards. We learned early on that it was really the data that sat behind the dashboards that mattered.”

The hospital management really liked these dashboards and came to rely on them. But they would often request new data, or have new questions that they needed the dashboard to answer. (This is a classic example of getting the answer to one question which leads to follow-on questions.)

But instead of getting new data quickly, the managers would instead get responses along the lines of, “You can get ‘pretty good data’ in a day or two or highly reliable data in 3-6 months.”  Now, of course, no hospital manager wants “pretty good data” for anything important relating to hospital operations, so that did not fly. On the other hand, nobody wants to wait 3 to 6 months for the answer to an important question either.

Explaining the need for data governance in business terms

The genius behind what Chris and Andrew did was that they took each of these opportunities to explain clearly, and in completely non-technical terms, why it took that long.  For example:

  • The quality of the data had to be good, particularly for any new data being introduced into the environment. This requires data to be in the right format and to be current, and not duplicate or missing.
  • The quality of the data needed to be monitored to be sure that it stayed at high quality over time. A study by Bloor Research found that data quality erodes, on average, at between 1 percent and 1.5 percent per month if not actively managed.
  • The data needed to have business context. That means attaching a business term and definition to it that everybody agrees on. Another hospital (not Cleveland Clinic) experienced the downside of this problem when managers showed up in meetings with conflicting numbers because they had different understandings of what the term “claim paid date” meant. Did that mean the date the claim was approved, the date the payment was processed, or the date that the payment cleared?

Chris and Andrew built trust and understanding by emphasizing the impact that data governance would have on Cleveland Clinic’s business value (patient health outcomes). They built trust to the point that when it came time to modernizing analytics at Cleveland Clinic, data management and data governance were key components of the initiative.

To summarize, here is what I took away from their excellent presentation:

  • Make sure that you have executive buy-in and sponsorship.
  • Start making the business case early.
  • Explain it in business terms. No technology.
  • Explain how it will impact the business outcome.
  • Try to design it into the initiative up front. Not after the fact.

Everyone’s mileage will vary here depending on the type of analytics initiative, the data maturity of the organization, and the type of business they are in, but this is great foundation.

When Cleveland Clinic started with data governance, they found that it accelerated the delivery of actionable and useful insights across their business. The result? This world class organization got even better at providing business value.

Isn’t that what you’re trying to do too?

To learn more, I highly recommend listening to Chris and Andrew describing their journey in an on-demand webinar, “How Cleveland Clinic is Building the Foundation for a Successful Analytics Program.” It’s on BrightTalk so listen now if you’ve already registered for our channel, or register now for access.