3 keys to simplifying data governance efforts
Every organization needs ready access to timely and trustworthy information, and more healthcare organizations are realizing that their strategic initiatives can’t succeed without a base level of data quality and the ability to understand and manage a rapidly growing volume of business information.
These capabilities are the province of data governance—a discipline that leverages the business value of data by improving its availability, usability, integrity and security.
Data governance has a reputation for being a daunting task that is prone to delays and failure. Surveys report that as many as two-thirds of all initial data governance efforts die on the vine. The major reason is that many well-intentioned efforts mushroom into such complexity during the planning and design phases that they are abandoned before they have the chance to deliver any value.
The challenge for fast-moving organizations is to capture the high value that data governance can deliver without getting mired in theory, analysis and complexity. Successful data governance takes a more agile approach than many organizations have traditionally employed and should leverage the following three key guiding principles.
Start with clear roles and strong support
High-level decisions should be made by a data governance council that brings together representatives from business and technical groups across the organization.
The daily work of turning policy into practice is usually entrusted to data stewards—existing team leads or power users in the organization who are already deeply involved in data. Data stewards are responsible for bringing data governance policies and standards to life on the business side.
Organizations may want different data stewards to oversee various data domains, such as patient data, financial information and provider locations. Stewards may come from the ranks of either producers or consumers of data; we find that consumers of data usually have deeper knowledge of requirements and specific issues. As data users, they also have a vested interest in getting it right.
On the IT side of an organization, executives will want to have complementary roles to the data stewards. These IT experts, often called data custodians, are technical contributors tasked with ensuring that your data is correct, up to date, useful and secure. It’s important that these leaders have the time, direction and organizational support to succeed. Their roles should be clear, visible and demonstrably valued by executive leadership, and their new responsibilities should be clearly reflected in their metrics and job descriptions. While they may be part-time, they are set responsibilities essential for long-term success.
Build success on shared understanding
Successful data governance begins with baseline education that builds shared understanding across the organization. Shared understanding also depends on a common language. Data governance has a wide variety of terminologies. While it’s helpful to adopt widely accepted definitions, the most important consideration is that everyone in an organization understands and uses the same terms and definitions when they communicate about data governance.
If possible, position data stewards to serve as the primary contacts and sources for delivering education. Leading these initiatives will help them build their own knowledge base, visibility and leadership. They can launch several baseline educational activities early on, while decisions about process and organizational design are still being made.
With this initial foundation— data stewards, data custodians and education around a common language—the organization can begin to make the detailed decisions that shape the initial data governance effort.
Clearly, data governance is not only about IT and data ownership. As an organization moves forward with the process, it will be making significant decisions about processes to improve and maintain data quality. These decisions will affect people across the organization every day, and gaining their understanding and support is critical. It takes a strong organizational change management effort to move data governance from planning and design to successful execution.
Start small and learn as you go
Data governance thrives on efforts that have clear meaning, practical application and momentum early on. Showcasing business value as soon as possible helps build support, learn what works and sustain a healthy program over the long term.
Begin by piloting a small set of data elements as quickly as possible—even if all the data governance roles are filled or processes are worked out. This early “apply/learn/apply” experience will be invaluable in making the final data governance organization more effective.
Some organizations are comfortable and experienced in using this nimble approach and don’t need to be convinced of its value. Others will be hesitant about “jumping the gun.” If an agile approach is new to an organization, executives may want to introduce the initial effort as a pilot, proof of concept or training exercise.
- Start with tools and processes that have worked elsewhere in the healthcare industry and adapt them to an organization’s needs rather than creating them from scratch.
- Let “good enough is good enough” be the predominant mantra. Keep things as simple as possible. It’s easy for complexity to increase exponentially. Don’t let a valiant effort get bogged down in long lists, detailed system analyses or elaborate processes.
- Focus on the practical. Use actual examples from the organization to frame discussions in relevant issues that demonstrate the tangible value of data governance. Keep long-term success in sight. Using a project as a launching point can be highly effective. But data governance must have an ongoing, independent status including a dedicated budget, resource commitments and processes to deliver business value for the long term.
A small-scale data governance effort will teach a healthcare organization things that can’t be discovered by any amount of theoretical analysis. What is learned can immediately feed back into the next iteration until you have grown a mature, effective data governance capability that’s finetuned to its needs.