Data Governance: A More Effective Approach

(US and Canada) Data governance is a fundamental building block of any critical initiative. It is a methodical system that designates the who, what, when and how of taking actions with certain information. Data governance prioritizes prudent decision-making and accountability, making it an essential process in every mission.

Whether you prefer “don’t try to boil the ocean” or “eat the elephant one bite at a time," there is wisdom in these approaches relative to data governance. Too often, monolithic governance programs fail to achieve the desired outcomes. Organizations waste massive amounts of time and money without receiving any value. Experience tells us among the biggest roadblocks to successful data governance is gaining buy-in – not only getting approval from the executive team but garnering support from functional teams across an organization.

The Data Governance Institute defines data governance as “a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods.” Successful data governance requires participation from individuals from all departments – from finance and marketing to IT and operations. Everyone must contribute to make governance successful and maximize data’s value.

The best way to encourage program participation and adoption is by tying governance to strategic use cases highlighting tangible outcome-driven initiatives. Governance requires business-driven and quality-focused strategies to achieve business objectives and deliver return on investment (ROI).

In this article, we will look at use cases that do just that, as well as principles of governance and the importance of a data governance agreement.

Five Core Principles of Data Governance

Recently, a State of Indiana agency, the Family and Social Services Administration (FSSA), commissioned the development of a data governance strategy and stewardship program and the formation of the Data Governance Council to assist with implementing the program. It is an essential and fundamental building block for mission-critical initiatives, including operational and decision-support initiatives.  

Their data governance strategy established the structure that guides the agency policies, procedures, roles and responsibilities for standardizing data, endorsing business rules, controlling data redundancy, managing master data, integrating structured with unstructured data, storing and using data, as well as protecting the privacy of the data. They use this data to inform not only the agency employees but also solution vendors of the agency data management requirements to ensure all parties involved apply standards uniformly.

They based this strategy on the U.S. Department of Health and Human Services (HHS) standards for data management that incorporate information management and develop a data management strategy. This strategy guides the agency in developing a comprehensive plan to manage data throughout the enterprise. It is intended to provide a structured approach to managing data resources at the agency in alignment with the agency’s data governance mission of “ensuring secure availability of high-quality data to enable integrated data-informed decision making with measurable outcomes.” 

The mission of the Data Governance Council focuses on five core principles: 

1. Privacy & Security: The highest priority of the Council is the protection of individuals’ privacy and protected health information. The Council shall ensure the protection of privacy for all individuals whose data is maintained by the agency by developing use restrictions, and enforcing the most appropriate standards and effective practices for data privacy and security. 

2. Strategic Alignment: The Council shall identify analytical priorities to arrange data resources to achieve the agency’s strategic goals and objectives.

3. Quality Standards: The Council shall develop policies and standards for the quality, consistency and timeliness of agency data. The Council shall promote ongoing and continuous data quality improvement efforts to ensure the agency is proactive in securing the availability of high-quality data for key agency initiatives. 

4. Education & Engagement: The Council shall engage appropriate stakeholders to raise the levels of both awareness and utilization of agency data resources. The Council’s efforts to expand involvement include promoting data literacy, which will improve data-related skill sets, as well as documenting and communicating data-related policies and processes within the agency. The Council aims to cultivate a data-informed culture to achieve the agency’s strategic objectives. 

5. Measurable Outcomes: The Council must measure the impact and outcomes of data-driven initiatives to ensure that data is being used as a strategic asset that maximizes its value across the agency.

Organizational Framework

To successfully achieve these five core principles, it was necessary to create the following roles and responsibilities:

1.  Council – Comprised of executive-level  department leads, the Data Governance Council is responsible for:

• Developing and maintaining an enterprise-wide data governance vision and strategy

• Developing and implementing both a 1-year and 5-year data strategy action plan

• Advising and reviewing agency-wide data governance-related policies

• Assisting with the dissemination and communication of policies, technical resources, and educational opportunities within departments related to data governance initiatives

• Establishing priority data governance initiatives and setting analytic priorities for the agency, and 

• Providing direction for the Council Chair and Working Groups. 

2. Council Chair – The Chief Data Officer of the agency will hold the role of the Council Chair. The Chair is responsible for:

• Providing support for the development of an enterprise-wide data governance vision and strategy 

• Leading Council Working Groups in the execution of priority data governance initiatives as determined by the Council

• Communicating feedback and opportunities for data governance improvement derived from the Working Groups to the Council, and

• Communicating progress towards the data governance vision to the agency executive leadership and key partner agencies and external organizations. 

3. Working Groups – Working groups will comprise subject matter experts, data specialists and key policymakers across the agency, as well as data-sharing advisors from partner agencies and external organizations. Working groups should reflect on and implement each one of the agency’s Data Governance Strategy's five core principles. Each working group is responsible for the following activities:

• Setting goals that align with the five core data governance principles.

• Developing specific strategies and action steps to achieve each goal.

• Establishing outcome measures with defined levels of success for each goal and strategy. 

• Providing quarterly progress updates to the Council. 

• Submitting recommendations to the Council. 

Data Sharing Agreements

Why Share Data?

Data-sharing is fundamental in increasing the ability of researchers, scientists and decision-makers to analyze and translate data into meaningful reports and knowledge. Sharing data minimizes duplication of effort in data collection and encourages diverse thinking and collaboration, as others can use the data to answer questions that the initial data collectors may not have considered.

Sharing data also encourages accountability and transparency, enabling data consumers to validate one another's findings. Finally, data can often be combined from multiple sources to allow comparisons across national and departmental lines. 

What Is a Data-Sharing Agreement and Why Is It Necessary?

A data-sharing agreement is essentially a formal contract documenting what data Agency A is sharing and how Agency B can specifically use that data. Such an agreement serves two purposes:

• First, it protects the agency providing the data, ensuring the receiving party will not misuse it.

• Second, it prevents miscommunication on the part of the data provider and the agency receiving the data by making certain all parties involved discuss any questions about data use. 

Before any data passes hands, both the provider and receiver should discuss data-sharing and data-use issues and come to a collaborative understanding they document in a data-sharing agreement.

Two High-Value Use Cases

The State of Indiana’s Medicaid agency demonstrated the power of governance and strong cross-sector data-sharing agreements through their collaboration with the state’s Department of Corrections (DOC). Recognizing the importance of continuity of care for recently released prisoners, Medicaid and DOC entered a collaborative effort aimed at using data to identify and solve issues of:

• Lengthy time-to-suspend Medicaid of newly incarcerated individuals, and 

• Lengthy time-to-activate Medicaid of the post-incarcerated population.

To unravel these issues, Medicaid and DOC connected the siloed and disparate datasets and tables, along with analyzing the business processes behind the data exchange with the DOC. 

This is important because the government suspends an inmate’s Medicaid during incarceration. Upon release (parole), it takes many days to reinstate Medicaid – this is an issue. For example, if a prisoner is diabetic, waiting over 15 days for the government to process Medicaid leaves a gap in necessary insulin coverage. 

Another example would be if an inmate had a history of drug abuse and, while incarcerated, used Suboxone to remain clean. Not being able to use Suboxone immediately upon release is detrimental to their maintained recovery. 

With governance and data sharing agreements in place, Medicaid and DOC now exchange inmate data, and inmates can now apply for Medicaid reinstatement while awaiting release. Upon release, Medicaid coverage is in place, and they can immediately receive life-saving medicines and therapies. 

In response to the results from the initial use cases, Medicaid is reviewing how to overlay presumptive eligibility (PE) onto current processes to improve outcomes even further. Another powerful use case!

Outcomes Matter 

By creating a data governance policy that holds data owners accountable for the consistency and quality of their data, Medicaid is now able to leverage its data assets more effectively. Data governance allows Medicaid to collaborate across sectors, such as DOC, to deliver services more efficiently to those in need. By enrolling prisoners in Medicaid before their release, Medicaid can reduce costs at the state and federal levels by suspending Medicaid enrollees who are incarcerated and reactivating coverage upon release. 

Through leveraging data assets to reduce costs and collaborate with other agencies, this initiative advanced the Medicaid Enterprise Systems through contributions to the growing body of research behind data governance and Medicaid enrollment for other states to develop their own standards and processes.

Conclusion

Data governance, particularly as it impacts cross-agency sensitive data sharing, is never an easy thing for organizations to solve. This example of how Medicaid and DOC built governance because of a need to partner for data analysis shows the power of governance and use cases working to improve outcomes.

If you need assistance developing a governance framework or need a partner to help articulate and implement use cases that drive results, Centric wants to talk with you.

About the Author

Rob Cochrane is an influential IT Executive with over 35 years in the Data and Analytics sector. Based out of Centric’s Indianapolis business unit, Cochrane is the Capability Lead for Centric’s Data Governance practice. His expertise in the development and implementation of innovative data-driven solutions create a lasting change to the clients he serves. Cochrane’s innovations are supported by his passion for data governance and data quality. He has created and implemented data strategies and governance models for Dow Agro, Johns Hopkins, Equitable Life and Casualty Insurance, and The State of Indiana. Cochrane is an alumni of Taylor University and received his MBA from Indiana Wesleyan University.