Health systems increasingly recognize data as one of their top strategic assets, but how many organization have the processes and frameworks in place to protect their data? Without effective data governance, organizations risk losing trust in their data and its value in process and outcomes improvement; a 2018 survey indicated less than half of healthcare CIOs have strong trust in their data. By following five steps towards data governance, health systems can effectively steward data and grow and maintain trust in it as a critical asset: Identify the organizational priorities. Identify the data governance priorities. Identify and recruit the early adopters. Identify the scope of the opportunity appropriately. Enable early adopters to become enterprise data governance leaders and mentors.

1. Five Practical Steps Towards Healthcare
Data Governance

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Healthcare Data Governance
As data gains more prominence in
healthcare as a top strategic asset,
health systems are increasingly
understanding the importance of a
data governance strategy.
Healthcare analytics leaders are
learning, however, that there’s a big
leap from knowing they need data
governance to implementing a strategy.
Even expert data analysts benefit from
guidelines as they move towards data
governance.

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Healthcare Data Governance
This presentation discusses the importance
of data governance in healthcare and how
organizations can transition from simply
doing analytics to doing analytics under a
strategy that safeguards and maximizes
one of their top assets—their data.

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The Urgent Demand for Balanced Healthcare
Data Governance
According to the HealthIT.gov, by 2015 more
than 80 percent of U.S. hospitals had figured
out how to collect their clinical data.
But, just as health system data has increased,
so has mistrust in their data. According to a
2018 survey, less than half of healthcare CIOs
have strong trust in their data.
They often don’t know how their analytics
are derived or on what data they are based,
and they rarely have any easy way to alleviate
their concerns.

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The Urgent Demand for Balanced Healthcare
Data Governance
Balanced data governance builds trust and
effectiveness.
In years past, analytics teams emphasized
data security. Protecting data was paramount,
and it continues to be important today.
But what good is data if only a few people can
use it? To paraphrase Henry David Thoreau,
that governance is best which governs least.
Effective organizations secure their data from
misuse. Beyond that, their job is to liberate
data and facilitate its best use by everyone.

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Five Manageable Steps to Healthcare Data
Governance
As a subject with many facets—from data
security to data quality to data stewardship—
it’s beyond the scope of this article to define
the details of data governance.
Data governance is more than a little
overwhelming, so much so that many
organizations struggle to get started; or
they do start, but the scope is too broad,
making it difficult to gain traction.

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Five Manageable Steps to Healthcare Data
Governance
According to data management expert
Will Bryant, “Surveys report that as many
as two-thirds of all initial data governance
efforts die on the vine.”
Bryant explains that these efforts
“mushroom into such complexity during
the planning and design phases that they
are abandoned before they have the
chance to deliver any value.”

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Five Manageable Steps to Healthcare Data
Governance
Healthcare organizations can navigate the complexity
of data governance set-up by following five practical,
manageable steps:
1. Identify the Organizational Priorities
2. Identify the Data Governance Priorities
3. Identify and Recruit the Early Adopters
4. Identify the Scope of the Opportunity Appropriately
5. Enable Early Adopters to Become Enterprise Data
Governance Leaders and Mentors

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Five Manageable Steps to Healthcare Data
Governance
1. Identify the Organizational Priorities
For analysts, report writers, and other
data practitioners, it’s often easy to
identify the data problems.
But deciding where to focus data
governance based on data awareness
alone is usually a recipe for failure.

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Five Manageable Steps to Healthcare Data
Governance
1. Identify the Organizational Priorities
Governance done for the sake of governance
is a hollow endeavor that rarely finds enough
leadership or grassroots support to gain or
sustain momentum.
If it does, it often becomes a law unto itself
with little regard for the analytic needs of the
organization.
Such governance may be worse than no
governance at all.

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Five Manageable Steps to Healthcare Data
Governance
1. Identify the Organizational Priorities
The purpose of data and analytics is to serve
the strategic goals of the organization.
So, the first step in developing a new data
governance program is to identify the
organizational priorities.
This requires an understanding, for example,
of the top five targets the executive team
wants everyone in the organization to help
deliver over the next year.

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Five Manageable Steps to Healthcare Data
Governance
1. Identify the Organizational Priorities
These could include lofty goals, such as,
increase patient engagement.
Or more specific goals, such as, decrease
adverse drug events by 5 percent or
increase patient use of telehealth services
by 9 percent.

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Five Manageable Steps to Healthcare Data
Governance
1. Identify the Organizational Priorities
A simple list of the organizational goals,
however, is likely insufficient.
Effort should be made to understand the
rationale behind the goals, the discussions
that led to them, who was involved, and the
nuance of their positions.
Understanding these last two points will be
important for step 3.

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Five Manageable Steps to Healthcare Data
Governance
2. Identify the Data Governance Priorities
Step 2 in developing a new data
governance program is to identify the
data governance opportunities that
overlap with the organizational
priorities step 1 identifies.
These organizational priorities may
even have been developed into
improvement initiatives already.

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Five Manageable Steps to Healthcare Data
Governance
2. Identify the Data Governance Priorities
All analytics depend on timely and reliable
data, so it’s likely that finding overlapping
data governance opportunities will be easy,
especially in organizations whose data
governance is still maturing.
For example, if decreasing adverse drug
events were one of the organization’s
priorities, proposing the creation of an
accompanying data validation process to
ensure the accuracy of the analytics would
be an easy sell.

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Five Manageable Steps to Healthcare Data
Governance
2. Identify the Data Governance Priorities
Keep in mind that enterprise data governance
is the end goal of these efforts. If possible,
choose data governance opportunities that are
relevant beyond the initial, narrow focus.
It may be appropriate to scope narrowly at first.
The new data quality validation process in the
above example could be focused on adverse
drug event data initially, but this should only be
the first draft of what would eventually become
a shared process to effectively validate data
quality anywhere in the organization.

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Five Manageable Steps to Healthcare Data
Governance
2. Identify the Data Governance Priorities
Otherwise, it may become just one of many
redundant related processes throughout the
organization that gets reinvented every time
another team needs something similar.
Such variability is not only very wasteful, it’s
also virtually impossible to govern.

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Five Manageable Steps to Healthcare Data
Governance
3. Identify and Recruit the Early Adopters
The next step is to survey the list of intersecting
priorities identified in step 2, note the account-
able leaders for each and identify which of these
leaders are likely early adopters of data
governance.
Energetic leaders who truly understand the
importance of data governance should be
top of mind.
Once each group establishes its governance
objectives, these leaders often naturally
become the champions of the organization’s
new data governance program.

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Five Manageable Steps to Healthcare Data
Governance
3. Identify and Recruit the Early Adopters
Creating a list of desired characteristics for a data governance leader
launches the search for early adopters.
Geoffrey Moore’s book Crossing the Chasm offers valuable insights into
important characteristics of effective early adopters:

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Five Manageable Steps to Healthcare Data
Governance
3. Identify and Recruit the Early Adopters
Connections: Early adopters need to be connected
to the right resources.
Enthusiasm: Because early adopters motivate others
to get on board, they must understand what needs to
be done and why and be excited to get to work.
A deep understanding of data governance:
Early adopters should understand data governance
on more than a casual level and be aware of the
challenges and benefits. Such awareness fuels the
drive necessary to break through the inertia early in
the program’s development.
>
>
>

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Five Manageable Steps to Healthcare Data
Governance
3. Identify and Recruit the Early Adopters
As the list of candidates grows, some questions
that probe each person’s understanding of the
benefits of data governance will help narrow down
the list to those most likely to carry the data
governance program across the finish line
Some of these questions may include:
What is the importance of data stewardship?
How would you approach metadata management?
What is the role of data lineage?
What business rules are needed?
What about master data management?
>
>
>
>
>

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Five Manageable Steps to Healthcare Data
Governance
4. Identify the Scope of the Opportunity Appropriately
Many organizations tackling data governance try
to do too much too soon and get bogged down.
It’s important to scope the opportunity narrowly
enough to get traction with a small pilot team that
is nimble enough to improvise and redesign
processes on the fly.

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Five Manageable Steps to Healthcare Data
Governance
4. Identify the Scope of the Opportunity Appropriately
Focus on a specific data governance opportunity
(e.g., data quality, data usage, etc.) within a
specific area (e.g., clinical domain, patient
population, geographic region, etc.) to target the
effort on a manageable subset of the
organization’s data governance—versus
trying to fix all of it at once.

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Increase usage of
the patient portal
by 10 percent of
patients in the Tri-
Cities region.
Becomes
Five Manageable Steps to Healthcare Data
Governance
4. Identify the Scope of the Opportunity Appropriately
If the data governance team chooses one of the
loftier organizational goals, it will need to refine
this into something more precise.
Increase
patient
engagement

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Five Manageable Steps to Healthcare Data
Governance
5. Enable Early Adopters to Become Enterprise Data Governance
Leaders and Mentors
As the first couple of teams achieve their
data governance objectives, it may be time
for them to generalize their processes for
broader adoption.
This shift in perspective from local to
organizational naturally coincides with each
early adopter’s transition from team leader
to enterprise data governance leader.

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Five Manageable Steps to Healthcare Data
Governance
5. Enable Early Adopters to Become Enterprise Data Governance
Leaders and Mentors
With new success under their belts, they are
well-positioned to champion data governance
generally and to recruit and mentor others;
their message is not just conceptual—it’s
based in their own hard-won experience, which
gives them instant credibility and a host of
concrete examples to drive home the how and
why of data governance.

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Data Governance: A Vital Tool Organizationwide
By following these five steps for doing data
governance, a small team can kick off a data
governance program, marry it with an
organizational priority, possibly in the form of
an existing improvement initiative, and make
progress toward enterprise data governance.
By recruiting enthusiastic early adopters to
champion the program, starting small, and
focusing on the overlapping organizational and
data priorities, data governance will be widely
recognized as a vital tool for advancing the
whole organization.

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For more information:
“This book is a fantastic piece of work”
– Robert Lindeman MD, FAAP, Chief Physician Quality Officer

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More about this topic
Link to original article for a more in-depth discussion.
Five Practical Steps Towards Healthcare Data Governance
Making Quality Your Core Business Strategy: A Foundational Element
Brent James, Former Vice President and Chief Quality Officer, Intermountain Healthcare
The Data Maze: Navigating the Complexities of Data Governance
Tom Burton, President of Professional Services
Data Governance Talking Points: Simple Lessons from the Trenches
Dale Sanders, Chief Technology Officer
Four Critical Phases for Effective Healthcare Data Governance
Health Catalyst Editors
Emergency Department Quality Improvement: Transforming the Delivery of Care
Health Catalyst Editors

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Other Clinical Quality Improvement Resources
Click to read additional information at www.healthcatalyst.com
Stephen Hess joined Health Catalyst as a Data Architect in January 2012. Prior to Health
Catalyst, he was a project manager and a certified Lean Six Sigma black belt at Xerox
Corporation where he worked for thirteen years in the business-services division. He holds
a Bachelor of Arts degree in English from the University of Utah.
Stephen Hess

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Other Clinical Quality Improvement Resources
Click to read additional information at www.healthcatalyst.com
Health Catalyst is a mission-driven data warehousing, analytics and outcomes-improvement company
that helps healthcare organizations of all sizes improve clinical, financial, and operational outcomes
needed to improve population health and accountable care. Our proven enterprise data warehouse
(EDW) and analytics platform helps improve quality, add efficiency and lower costs in support of more
than 65 million patients for organizations ranging from the largest US health system to forward-thinking
physician practices.
Health Catalyst was recently named as the leader in the enterprise healthcare BI market in
improvement by KLAS, and has received numerous best-place-to work awards including Modern
Healthcare in 2013, 2014, and 2015, as well as other recognitions such as “Best Place to work for
Millenials, and a “Best Perks for Women.”