About 7 months ago, one of Health Catalyst's clients asked for a 90-minute cram course on data governance, including time for questions and answers. They were struggling, like so many other healthcare organizations, caught in the swing of extremes from too much to too little, while equilibrium eluded them. With a last-minute rush, Dale Sanders (President of Technology, Health Catalyst) fell back on his time in the Air Force and threw together a talking points paper to facilitate the conversation. At the end of the meeting, the client was effusive with their appreciation, using words like “incredibly insightful,” “brilliant,” and “hugely valuable.” Dale didn’t think it was that good, but their data governance function was “dramatically better,” and they were happy, so something worked. Since then, Dale has used the same talking points in two other similar meetings, with similar feedback and results. It still doesn’t feel that great or insightful to him, but he's glad to flow with the feedback and share the same style in this webinar in the hope that it’s useful. After viewing this webinar, Dale hopes that you will have some tactical ideas to assess your organization’s data governance strategy. Are you leveraging the data you have? What could improve?

1. Talking Paper On
Data Governance, 17 Oct 2018
1. Technology of data is important, but data governance success is more about the soft,
human skills associated with data and leadership
2. We are missing data and the data we have is poor
a. 80% of healthcare outcomes are attributable to events outside the four walls of
traditional healthcare…it’s the dark zone of data
3. Executive sponsorship: Data is our new asset of the future that will outlive all of us
a. Triple Aim of Data Governance
i. Data Quality: Completeness x Validity
ii. Data Literacy
iii. Data Utilization
4. Vital signs of a healthy data governance function
a. Define your values and principles of data governance, first—maximize the use
and value of data to provide personalized care at the lowest cost possible-- then
your org chart, leadership, and processes
b. Pick 4-5 demonstration projects at the early phases of data governance to
exercise the processes, relationships, and mission of data governance
c. Soft side of being data driven is critical: How are we applying data to the benefit
of our people’s Mastery, Autonomy, Purpose?
i. I would argue that, in the case of physicians, the industry is applying data
in a way that diminishes their sense of each
d. Endorsement from the CEO that data is a critical asset. They send and reinforce
the message in their behavior.
i. From the CEO: “We are a data-driven culture. Digital health is coming.
We trail almost all industries in our use of data to improve our mission.
Our Data Governance Committee is responsible for helping us ensure
that we collect, utilize, and acquire data as a strategic asset to improve
the health our patients, lower our costs, and improve our margins.”

2. e. The person leading the DG effort has a solid vision and expertise for leveraging
data to the benefit of clinicians and administrators
f. They have skills that motivate people around this vision; they are respected in
the organization
g. They realize that data governance is just a means to and ends; it’s not the ends
h. Effective data governance is like effective cybersecurity. When it’s working, it’s
ambient; you don’t notice it. When it’s not working, you have a process for
reacting and resolving.
i. They don’t look for something to govern
j. Data stewards play a very important role. They are facilitators, not watchdogs.
They know their data content area help others turn it into enterprise value.
k. They have a strategic data acquisition roadmap
l. They have a formal data literacy training program
m. They evolve quickly from data governance to include algorithm governance
5. Governance structure: Centralized principles, decentralized execution and adherence
a. Make the Data Governance Committee a subcommittee of an existing Executive
committee
b. As CIO, I was the chair of this subcommittee; it was a natural fit but you mustpick
an executive that’s right for your culture.
i. Don’t overload the subcommittee with C-levels…2 is fine, 3 is almost too
much…they don’t have time…this should be a working group
ii. Authority to resolve the tactical issues
iii. The Executive Committee is the “Supreme Court” when issues loom
larger than the subcommittee
c. Data stewards who are facilitators, not barriers, to data access, literacy, and
utilization
i. Measures Manager and Atlas: Tools for data stewards, data engineers,
and analytics engineers

3. d. Data analysts who are keen to ensure accuracy of reports, and avoid analytic
homonyms and synonyms… sounds the same but is a different report; different
name but same report
6. Tension between Information Security and Data Governance
a. The former wants less access, the latter should want more access to data
7. Problems with data governance in general
a. Too little, too much
b. Govern to the least extent necessary to achieve the common good
c. Data governance being driven from a very techie perspective… “What’s the
standard format for gender?” and asking Executives to resolve every one of
those details
d. Putting too many clicks on the backs of physicians and nurses to collect more
data
8. Digitician: Sits between the clinician and the patients and is responsible for managing
the data profile, quality, and refresh rate of patients
a. Could be an extension of a Care Manager
b. What type of data do we need to manage this specific patient type?
i. The data profiles differ—the data profile for optimizing the health of a
diabetic patient is different than a total knee replacement, etc.
9. Additional Study:
a. Demystifying Healthcare Data Governance, Dale Sanders
i. https://www.healthcatalyst.com/demystifying-healthcare-data-
governance
b. McKinsey Digital Assessments: What’s your Digital Quotient?
i. https://www.mckinsey.com/solutions/digital-20-20/our-
assessments/strategy
c. HIMSS Adoption Model for Analytics Maturity based on the Health Catalyst
model
i. http://www.himssanalytics.org/amam