Many healthcare organizations are seeking to increase their data governance efforts today, given the huge increases in data now available to manage. This article profiles one professional’s journey as part of growing a data governance program and some of the lessons learned along the way.
Kim Jackson, MPH, RHIA, is the lead data governance analyst at Edward-Elmhurst Healthcare, a large integrated health system in Illinois that includes three hospitals and more than 50 outpatient locations. In this role, she is responsible for enhancing data literacy and improving data management practices for the enterprise. For 18 years, Jackson has worked in healthcare software and analytics with a focus on developing best practices around software solutions and user adoption. Jackson says the most enjoyable part of her experience has been collaborating with operational areas to achieve data insights and understanding. Given her background and expertise, we asked Jackson to share some of her experiences regarding the data governance work in her organization.
HIM skills and experience are often a good foundation for leading data governance initiatives. Tell us more about your career path to your current position.
I have a bachelor’s degree in health information management from Illinois State University and a master’s degree in public health informatics from University of Illinois Chicago. My first jobs after completing my undergraduate degree were in data analyst roles for a community hospital. I was responsible for pulling together data for reporting purposes, providing system maintenance, end user support, and ensuring accurate input of information into the systems. These positions were in the quality department and clinical operational area. I found that I really like working with clinical data; it is definitely complex, but it’s more impactful to patient care and clinical processes. Following these jobs, I worked on a team that implemented our system-wide electronic medical record (EMR). This experience gave me a great understanding on how the front-end build can guide a user’s input of information, how workflows integrate, and how discrete data is stored in a system on a large scale. It also refined my skills in understanding and designing the tools needed in clinical areas.
My most recent job roles brought me back to working with data, and I have worked for 10 years in the analytics space. I have applied my expertise in working with operations and understanding how information is captured in order to provide solutions that best benefit the user. This career path has provided me with an in depth understanding of the field and has prepared me for a role in data governance. These experiences have highlighted the importance of clearly defining metrics, documenting definitions, and being transparent about data to increase user understanding.
How is the data governance team structured in your organization?
There are currently three members of the data governance team, and we report to the director of business intelligence (BI) and analytics. The director also manages three other teams: data engineering, analytics, and advanced analytics. The data engineering team is responsible for our data warehouse; the analytics team handles development of solutions (e.g., dashboards, reports, etc.); and the advanced analytics team focuses on predictive models. These areas are under the system chief strategy officer, with a dotted line to the chief information officer. When the BI/analytics team went through a reorganization the change was made to have us shift from our departments being under the chief information officer to the chief strategy officer in order to highlight the importance of data as an asset to the organization while addressing analytics as part of our health system’s strategic roadmap.
Can you share a bit more about how data governance got started in your organization?
Our BI/analytics team began a new redesign process about five years ago. The need for data governance was identified due to some underdeveloped processes and infrastructure when compared to industry best practices.
There was a lot of work focused on building a shared vision and mission for the organization around use of data. With the help of an outside consulting firm and understanding best practices, a strategic roadmap was developed for the entire analytics umbrella. This included outlining how we would incorporate a more formalized approach to data governance in this vision.
I am a more recent addition to the data governance team, so a lot of the foundational work was already underway. Several projects were in progress right before COVID-19 hit and unfortunately, they lost some traction early in the pandemic. The projects resumed again in the fall of 2020. This early work included the development of our different committees and workgroups, establishing charters to clearly outline goals and membership, and identifying roles and hiring staff to be a part of the data governance team.
Tell us more about your work on the data governance team.
The data governance team is focused on “building the culture” around data for the organization. Three principles have been established to help accomplish this vision: establish shared stewardship, increase data literacy, and build trust with our data.
We’ve done a lot of work and communication to help the organization understand that the governance of data is not the sole responsibility of the data governance team. Since data is an enterprise asset, the responsibility for governance and integrity of the data is shared throughout the organization. However, my role on the team is to help make progress on those three principles by facilitating the development of any new processes and standards in collaboration with other resources across the enterprise.
The overall principles make sense to users, but they are best demonstrated on a smaller scale. Progress is made one step at a time. I facilitate a lot of discussions and completion of work at a project level. As we tackle one data quality issue or establish better management of a key record, we can then take those use cases or projects and highlight how they contribute to the larger principle, helping reinforce through example how the organization can treat data as an asset.
How is data governance managed across your organization?
Committees have been established and are a part of the data governance framework. They define how we engage operations in this shared approach. The involvement from leadership is important to guide our analytic priorities, promote change/adoption, and ensure we are making progress on our initiatives. We have our existing Executive Steering Committee, which represents the senior leadership for the health system, and they oversee strategic direction as well as ensure support for key initiatives. The Analytics Steering Committee, which falls under the Executive Steering Committee, helps guide priorities for analytics specifically, and has leadership that will help promote change around analytics. The Data Governance Council, is where the data governance team coordinates much of its work, ensuring there are champions in operational areas where engagement is needed. The data governance team has meetings with this leadership throughout the year to keep them updated on priorities and engaged in initiatives.
There are project teams that support the BI work and produce analytic solutions for the different operational areas. As part of best practices, the data steward role was created to establish a dedicated resource from an operational area that can participate in project work as needed. This is for both BI product development and data governance work. They are meant to remain embedded in an operational area so they have a continued understanding of build and workflow, but we can call on them as a resource when needed. They have been a great liaison, as they understand the data for their area, but also gain analytic skills over time, which improves their ability to collaborate with the data governance team on initiatives.
To help continue progress on our three principles, data management workgroups that the data governance team oversees, are in place. These workgroups are used to bring together multidisciplinary teams on key topics. The Analytics Certification Workgroup may certify content, for example, providing a stamp of approval for data or reports that are published externally. The approval signifies trust and makes it easy for end users to tell that data has been validated. The Data Quality Management Workgroup addresses data quality issues (e.g., not captured risks), works with operations, and asks leadership to reinforce needed workflows. The Data Architecture Workgroup focuses on standards and the Data Security Workgroup oversees how access is provisioned.
Some of our accomplishments to date that have come out of these workgroups include defining data domains to identify “owners.” Examples of data domains include imaging, revenue cycle, and patient access. We also have developed a Power BI report catalog, a business glossary, a monthly newsletter, and launched a data-driven gateway intranet site and data governance SharePoint site. All of these being projects that have helped support our vision.
Now that you have been on your data governance journey for a while, what are some of the key lessons learned from this work?
Education on this topic is important. We need the organization to understand what we are trying to achieve with data governance before we can expect them to contribute. This is where we have emphasized the need for stewards in the organization, so they can help promote our efforts in their own areas.
There also have been some challenges around competing priorities on any task that requires collaboration. Many of our projects require support from resources outside of the data governance team, and if priorities are not aligned, that makes it hard to complete projects and meet goals.
Clinical areas have struggled to identify data stewards to work with the data governance team on initiatives. They may not have an individual that is responsible for data in their area or, if they do, we run into barriers with their capacity and availability to work with us. It has been easier in the revenue cycle areas, as there are existing analysts who work with data and already produce reports. Their engagement in our efforts have been pretty successful.
Finally, data governance falls under the BI/analytics area, but our mission requires progress in how we use data across the enterprise. Our team is primarily sourcing data from Epic, but we know there are many other data systems across the organization that need to be included in these efforts. Our future focus is to better collaborate and engage with areas that are big producers of data and align some of our standards.
Melanie Meyer has over 25 years of experience in health informatics and information management, including data governance, and currently co-chairs the AHIMA Data Analytics and Data Use Practice Council.
Kim Jackson is the lead data governance Analyst at Edward-Elmhurst Healthcare where she is responsible for enhancing data literacy and improving data management practices for the enterprise.