Like many industries, healthcare is becoming more and more reliant on data. Health systems, provider organizations, and payers alike are investing to establish and mature their data and analytics organizations. If you can harness the power of your data and effectively utilize it to make data-driven decisions, you will excel in comparison to your competitors, especially when it comes to customer experience, patient outcomes, quality, safety, and affordability.
While having the best technology, and team members with exceptional analytics and data skills are key success factors, these things alone won’t unlock the value of your data. To truly unlock value from your data, you must have strong data governance.
But what exactly is data governance? Essentially, it is the management and enforcement of the policies, procedures and standards you set for your organization’s use of its data. Think of it as the traffic laws, enforced by elected leadership and law enforcement (your data governance team), and managing the activities on your organization’s roads and highways (your operating model).
Strong data governance is the backbone of a mature healthcare data analytics organization. If you’re doing it right, you are:
- Protecting the integrity and consistency of your data to ensure trust and credibility in your analytics products
- Guiding project prioritization to maximize resource utilization
- Measuring value realization and capturing the return on your investments of time and money on various initiatives, ensuring you are delivering true value and not working on initiatives that aren’t serving your organization
- Enabling data driven decisions across your organization, radically enhancing productivity, and giving your organization confidence and pride in the impacts they are making
Data Governance Strategies to Implement for Optimal Effectiveness
So, how do you ensure that your data governance organization and practices are meeting the core goals and objectives outlined above? Based on our experiences, we’ve cultivated a list of six strategies for you to implement to achieve optimal impact:
1. Establish a single source of truth – When it comes to enterprise level analytics, it’s imperative that your organization recognizes what data sources and analytics platforms are trusted and validated as the source of truth. Within these systems and platforms, all business rules and data definitions should be well documented and accessible to the organization.
2. Promote data literacy – In order to make the cultural shift to data-driven decision making, all members of the organization should be “data citizens” and be able to interpret the insights from your analytics products. Data governance should play a major role in the education of how to understand and utilize the analytics tools and insights available to them. Specifically in healthcare, it’s important to promote data literacy from both a clinical and an operational perspective, to ensure that the care providers as well as the administrative teams understand how to act appropriately to the insights they gain from the data.
3. Ensure consistent, ongoing validation measures – Define repeatable validation steps and automate when possible to limit the risk of bad data infiltrating your organization. By setting up consistent processes, you will also reduce the need for time intensive data cleansing efforts in the future.
4. Ensure strategic alignment to business objectives – Compelling use cases for analytics in healthcare continue to surface (e.g., clinical variation, predictive care AI, readmissions reductions, supply chain optimization, claims optimization, etc.) therefore increasing the number of analytics product and project requests. With the increase in analytic opportunities, data governance plays a key role to prioritize these new requests and ensure the highest value analytics work is getting pushed to the top of the list. When it comes to prioritization, alignment to your organization’s strategy is key. All analytics products should serve the purpose of providing insights that allows the organization to achieve its strategic goals. Your data governance should define an objective process to evaluate the alignment of an analytics request and help to “break the tie” for competing priorities.
5. Manage a “product inventory” – It’s critical that your data governance organization maintains a product catalog, and avoids having a “dashboard graveyard” of published, but inactive products. Knowing what your organization is looking at is key to ensuring that analytics are driving your organization in the right direction. Especially for large healthcare systems with multiple hospitals and clinics as well as various service lines, it’s key to have visibility across unit specific products and enterprise products to maintain alignment and consistency of care across the organization. Knowing what your organization is looking at is key to ensuring that analytics are driving your organization in the right direction to providing the best patient care and customer experience.
6. Measure meaningful KPIs – Defining and making visible consistent KPIs that measure the value of your analytics products is the best way to confirm that data governance is being effective, and allows the organization to pivot if the metrics highlight challenges or risks.
The importance of data governance for your healthcare data analytics organization is clear, and we’ve shared our perspectives on what strategies you should focus on – but how do you actually put these into practice?
Guiding Principles for Your Data Governance Practices
While all organizations are unique and there is no one-size fits all solution, we’ve established a core set of guiding principles for you to consider when establishing your data governance practices:
- Cultivate shared vision, ownership, and accountability across your organization, inclusive of both clinical and corporate team members
- Right-size all governance activities to ensure the least amount of administrative activity as possible is implemented in order to be effective; reduce unnecessary overhead wherever possible
- Maintain commitment to improving the quality, safety, and cost of care to patients
- Focus on high-value efforts and delivering consistently over time. Do the due diligence upfront for new efforts to quantify expected impact
- Provide maximum transparency into your analytics portfolio, both with ongoing and upcoming projects
Are you needing support to establish or mature your healthcare data governance? Let us know by using the form below.