Public Health Data Modernization has stated goals of transforming public health data infrastructure, aiming to replace outdated systems, moving away from siloed systems and data, improving interoperability and ultimately enhancing data quality, improving surveillance capabilities, facilitating data sharing and empower public health at all levels to make data-driven decisions that safeguard the health of all Americans. Inherent in attaining these goals is the implementation and integration of robust data governance processes.
Data Governance provides a structured system for ensuring trust and reliability of an organization’s data. This structure includes defined policies, procedures, roles, responsibilities, and technology all aimed at guaranteeing data accuracy, consistency, security and adherence to rules and regulations. The fundamental objective of establishing this structure is to build trust in the organization’s data throughout its lifecycle, for both internal and external stakeholders.
Data governance practices apply to all aspects of the data lifecycle, from creation/collection/retrieval, processing, transformation, definition and storage, use, sharing, archiving and destroyal. For example, many data modernization activities consist of implementations of technologies to better combine, share and visualize data. Data Governance also ensures that data decision makers are clearly identified when questions arise about the data at any point in the life-cycle. Finally, data governance practices inform us what can be done with the data once it has been transformed or combined with other data and how that impacts how the data may be shared with others.
For example, If we were to combine two datasets such as vital records (birth and deaths) and data about traumatic brain injuries (TBIs) to be able to identify people dying from TBIs, we would use data governance practices to determine the ‘what’ and ‘how’ to combine the two datasets, if translation of the data is needed, how to perform that translation, tracking changes made to the data, who made those changes and when,and who are the decision makers when disagreements arise as to the use and applicability of standards. Data Governance practices are also used to determine how and for what reasons the data is to be used for, upon availability, and who may have access to all or portions of the data.
Public Health has long prioritized the need to protect its data and practices to safeguard and build trust is a strong component in public health data management. However, applying data governance to all of an organization’s data assets, especially in the context of the simultaneous implementation of data integration and sharing platforms, can be overwhelming and quite complex.
HLN worked together with the Public Health Informatics Institute (PHII) to create a Data Governance training guide for public health (A guide to agency-wide data governance through the lens of data modernization). This guide provides an interactive learning opportunity to address the challenges faced by data modernization teams in implementing data governance. While initially developed through the lens of data modernization, the principles and tools in this guide can be applied more broadly to data governance projects throughout public health.
There are a lot of useful other resources if you are interested in data governance:
- Data Management Association International (DAMA)
Provides education, certification, and career advancement in the Data Management profession. The DAMA Wheel and the DAMA-DMBOK (Data Management Body of Knowledge) are well used resources for data management. - Data Governance Institution (DGI)
Provides education via Dataversity Training Center. Supports and partners with International training and events. On-Line bookstore and training. This link goes to the DGI’s version of the Data Governance Framework.
- Data Governance Professionals Organization (DGPO)
A non-profit, vendor neutral, association of business, IT and data professionals dedicated to advancing the discipline of data governance. Its objective is to provide a forum that fosters discussion and networking for members and to encourage, develop and advance the skills of members working in the data governance discipline.
The implementation of data governance is a focus for many public health agencies as part of their data modernization efforts. However, Public Health’s unique relationship with data, coupled with the organizational change management efforts required to implement effective data governance practices, can make these initiatives challenging. HLN is supporting multiple public health agencies in addressing these challenges, leveraging our expertise in both data governance and public health to ensure successful outcomes.