Solving Data Challenges with a Data Governance Program: Putting Business Needs First

Situation

Many of the data issues that healthcare entities encounter on a regular basis can be drastically reduced with the implementation of data governance, a system of decision rights and accountabilities to standardize, manage, and enhance the use of their data. Data problems may range from security and privacy concerns to questionable data quality to confusion regarding who makes decisions about the data. A data governance program provides the infrastructure that defines who can use the data, how they can use it, and when.

Typically, data governance (DG) programs initially focus on a few key data issues to create the greatest opportunity for success. Addressing all data problems at once in a new program will likely fail. In this case, our client had a number of data concerns critical to clinical practice and business operations. Without defined data stewardship, multiple people would often manipulate the same data set, causing data differences from application to application. With no data inventory, there was no efficient way of determining what data was available or where it resided, so the client often struggled with reporting or troubleshooting information.

Our client was aware of the importance of data governance and had long wanted to implement a program. Freed was engaged in a two phase project: first, to systematically assess data issues throughout the organization and provide recommendations for developing an enterprise data governance program; and second, to work with a leadership team to develop the framework and road map for the organization’s data governance program.

Solution

To fully understand and document all data issues affecting our client’s business and clinical practice, Freed developed a standardized data assessment tool based on our client’s business needs and corporate strategy. Freed conducted the assessment across the enterprise, and findings were aligned with our client’s business drivers. Freed suggested four potential models for developing a data governance program, each of which had specific foci to address our client’s key data issues and meet current or foreseen business needs:

1. Policy, Data Ownership, and Data Quality
2. Data Architecture
3. Policy, Strategy, and Data Architecture
4. Business Intelligence and Analytics

Each outlined the specifics for the specifically focused approach, and included timelines, staffing estimates, commitment requirements, and likely barriers with mitigation activities.

Our client chose the model that focused on policy, data ownership, and quality. Freed worked with a small leadership team representing key data users in clinical, finance, operations, and reporting areas. Freed guided the development of the data governance framework and roadmap through presentation of key concepts, best practices, and specific examples. We also led the client’s discussion of advantages and risks for each infrastructure component, based on their needs, their organizational culture, and our experience. The framework defined specific roles and responsibilities, data business requirements, accountabilities, and scope of data to be governed. Freed created the roadmap and implementation plan, including a program charter, program processes, quality standards, metrics to measure program progress and success, and communication plan.

Results

Freed provided our client with the blueprint for a robust and fundamental data governance program, including framework, roadmap, and implementation plan. We drafted several data policies and provided sample communications materials. Once fully implemented, the governance program will yield consistent, trustworthy, and high-quality data that our client can use to make the best possible business decisions. Fewer people will need to be involved to answer data questions and the culture of accountability will help turn data into a true business asset.