Data governance is a key enabler in improving the value and trust in information, helping achieve efficiencies and cost savings while playing a key role in patient engagement, care coordination and community health.

As data governance matures within the healthcare industry, organizations need to treat data as an asset. Data is a powerful tool for gaining actionable insights. But like any other asset, data needs to be governed to provide its true value to the organization.

The goals of data governance have long been clear outside of the healthcare industry. Organizations want to enable better decision making, reduce operational friction, protect the needs of data stakeholders, train management and staff to adopt common approaches to data issues, build standard, repeatable processes, reduce costs and increase effectiveness through coordination of efforts, and ensure transparency of processes.

Common data governance drivers include:

  • Data regulatory mandates which are increasing the need for data transparency.
  • The need for accurate data with consistent definitions, which is becoming even more important as patients become data consumers.
  • The migration to enterprise data solutions and less clear ownership of data.
  • The complexity of reporting requirements in health care.
  • An increased need for IT security due to sharing of data across health care organizations.

The following steps will guide your organization in creating an actionable program.

1. Determine your organization’s data governance goals, drivers, opportunities and challenges including which strategic initiatives will require data governance to be successful. Address and overcome potential challenges by using a streamlined approach, establishing focus areas, using existing leadership, tying governance to demands, emphasizing education and making data governance foundational to electronic medical records.

2. Assemble a working group, which should include individuals who will form the nucleus of your eventual data governance steering committee. These individuals should typically include a business leader, subject matter expert(s), and data professional(s).

3. Identify potential data governance projects. Interview stakeholders to identify organization data needs, pain points and/or opportunities. Categorize findings through triple aim goals (cost, quality, patient engagement), regulatory and/or IT. Prioritize by impact to data governance goals, resource requirement and time to implement, and be sure to start with some “quick wins.”

4. Identify individuals and roles and define an organizational structure. As the data governance team is formalized, remember that this is just a starting point – changes will occur as the data governance organization matures. Be realistic about the available resources and scale the org model to fit. Some people may fill multiple roles, but it is helpful to be clear of expectations in different situations. Commit people to the required roles to launch the data governance program.

5. Define the forums, roles and responsibilities. Forums needed for engagement and decision making across the organization are executive steering, governance committee and working groups; roles needed for data governance are data stewards, data custodians and program leaders.

6. Define the organizational model including key data governance bodies, and stakeholders that will be engaged by the data governance program.

7. Define the charter and basic data governance processes. Consider the answers to the following questions as the basis for creating the charter and defining the program.

  • What is in-scope and out-of-scope?
  • What is the organization hoping to accomplish through the data governance program?
  • How will decisions and escalations be made?
  • What is the approach for setting up the data governance program?
  • What work products will be produced?

8. Define the data governance process. A functional governance process can be assembled from four sets of activities:

  • Assessment and prioritization, including a review status and requirements for different subject areas, setting priorities, and assigning resources.
  • Planning including a plan to supervise detailed analysis, approve implementation plans and resources.
  • Implementation of the plan, supervision, tracking, issue resolution and escalation
  • Monitoring and ongoing tracking and reporting of performance metrics and remediation.

9. Define the KPIs to measure data governance program success. Focus on measuring engagement, the active participation and adoption of governance processes by data governance practitioners, stakeholders and leadership, and effectiveness of business outcomes linked to the data governance program.

10. Define the execution plan to define the tasks to stand-up the data governance program. If executed properly, the program becomes a sustainable practice that carries on into the future.

11. Execute first iteration – begin project execution and monitoring, track KPIs for progress and success, make necessary adjustments, and plan for the next iteration.

And finally: remember this is a framework, not a project. If done right, you’ve set up a sustainable practice that will carry on into the future. As the amount of data coming at us increases, so does the technology to manage it, the laws to govern it, and the ways to use it.

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