Getting a Handle on Data Governance
For over a decade, UNC Health has been working to harness its vast amounts of clinical, financial, and operational data to improve the quality and efficiency of patient care. In this article, we’ll explore the key steps they’ve taken to achieve this goal, including the importance of effective data governance.
Key Takeaways
- Don’t call it data governance (at least, not yet)
- Define the people, process, and technology must-haves for an effective analytics program
- Use an analytics maturity assessment model to evaluate and improve data governance
- A hybrid approach (both centralized and decentralized) can be effective for specific use cases
- New data management policies need to be developed and promulgated across the enterprise
The Journey to Analytics Maturity
UNC Health has been working with HIMSS Analytics to develop its analytics capabilities, starting with the Adoption Model for Analytics Maturity and more recently with the updated, outcomes-focused Analytics Maturity Assessment Model. This collaboration has helped the health system to:
- Identify the key elements of an effective analytics program
- Evaluate its current state of analytics maturity
- Develop a roadmap for improvement
Setting the Stage for Success
Greg Kuhnen, System Director for Analytical Solutions at UNC Health, emphasizes the importance of defining the people, process, and technology must-haves for an effective analytics program. He also highlights the need for a hybrid approach, combining both centralized and decentralized models, to achieve success.
Conclusion
Effective data governance is crucial for healthcare organizations seeking to improve the quality and efficiency of patient care. By understanding the key elements of an effective analytics program and using an analytics maturity assessment model, organizations can evaluate and improve their data governance capabilities. With a hybrid approach and the right leadership, healthcare organizations can unlock the full potential of their data and drive better outcomes.
Frequently Asked Questions
- What is data governance?
Data governance is the process of managing and controlling access to an organization’s data, ensuring that it is accurate, complete, and secure. - How do I get started with data governance?
Start by defining the people, process, and technology must-haves for an effective analytics program, and use an analytics maturity assessment model to evaluate and improve data governance. - What is the hybrid approach?
A hybrid approach combines both centralized and decentralized models, allowing for different levels of control and autonomy depending on the specific use case. - How do I develop and promulgate new data management policies across the enterprise?
Work with leadership and IT teams to develop and promulgate new data management policies, and ensure that they are aligned with the organization’s overall goals and objectives.
