CC BY 4.0 · ACI open 2021; 05(02): e80-e83
DOI: 10.1055/s-0041-1730383
Invited Editorial

Perspective: Data Governance—Making Data Great

Keith F. Woeltje
1   Institute for Informatics, BJC HealthCare, St. Louis, Missouri, United States
2   Washington University School of Medicine, St. Louis, Missouri
› Institutsangaben


Health care delivery in the United States is undergoing massive changes. Although the pace at the local level can often be imperceptible, the overall momentum is relentless. Multiple factors are driving these changes—the rate of cost increases for care in the U.S. is unsustainable, the outcomes of care are worse than other countries that spend less, and a large portion of the population does not have real access to ongoing care, leading to enormous societal costs. On the health care delivery side, there has been a shift from stand-alone care delivery through stages of collaborative and value-driven health care toward a more accountable care/population management approach ([Fig. 1]).

Zoom Image
Fig. 1 Evolution of care delivery.

Politicians, policy experts, and health care administrators long recognized that changing models of health care would require significant amounts of data. This was a major factor in the founding of the Office of the National Coordinator for Health Information Technology (ONC), and later the Health Information Technology for Economic and Clinical Health (HITECH) Act which brought about the Meaningful Use program.[1] With the increased use of electronic health records (EHRs), the availability of electronic data in health care has risen dramatically.

Organizations increasingly see their data as a strategic asset. To most effectively leverage this asset, data from diverse sources (e.g., revenue cycle, supply chain, quality measures) must be combined for analysis. The use of data from diverse sources has always occurred, but in many organizations, this was often accomplished through establishment of locally curated data collections. Unsurprisingly, organizational leaders found they could get different answers for the same question depending on which group they asked.

Because data definitions can be quite nuanced, a team can become reluctant to share data with the rest of the enterprise. There is a concern that others would not understand “my data.” This can then lead to users on other teams to be creative in finding sources for the data they need. By acquiring data sets from diverse acquaintances across the organization, analysts may be using data with somewhat unclear provenance (often many steps away from the original sources) for important organizational reports that drive executive decision making. This can exacerbate the issue of getting different answers from different groups.

Technical approaches to improving the situation include establishing a “single source of truth” for enterprise data, for example, via an enterprise data warehouse or a data federation approach. This can help with some of the notions of “my data” versus “our data” in an organization, but does not in and of itself ensure that the data are well characterized and of high quality. As health care organizations struggle to use their data effectively, they realize they need an approach to addressing these issues. Whether they formally label it as such, what the organizations are grappling with is data governance.[2] [3]


Eingereicht: 16. Juli 2020

Angenommen: 20. April 2021

11. Oktober 2021 (online)

© 2021. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (

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