With the coming release of our pending Health Data Analytics Value Chain report, we wanted to start a discussion around one of the core elements of the value chain that we present: Data Governance. Data governance is the foundational piece of the analytics value chain, establishing frameworks for managing data and information across an entire HCO.
Data governance sets the rules of the road for integrating data, ensuring that quality and use remains intact across multiple use cases. It also establishes the codes for data stewardship; the approach that organizations use to manage data and links to identities.
Data stewardship concerns are nearly countless ranging from data collection, aggregation, role-based sharing, consent management and security across different stakeholders. For example, those who emphasize data ownership as an organizing paradigm may call for paying for information as a way to incentivize data access.
All of this points to the need for having an organized, coherent data governance approach at the outset in order to ensure not only the integrity of the data used in analytics but to facilitate an overall data analytics driven strategy throughout the organization that adheres to defined principles of use (stewardship).
From “The Importance of Data Governance in Healthcare” from Encore Health Resources
The Data Governance architecture illustrated above by Fleissner and colleagues (for Encore Health Resources) emphasizes the need for a team to work across organizational boundaries to capture the workflows and IT needs of the entire organization. The Data Governance (DG) team identifies the uses of data, stakeholders, outcomes, and workflows that are impacted by data analytics. With proper structuring, they will identify the organizational changes needed to maintain integrity of the data as well as be better situated to leverage data across the analytics and healthcare delivery value chain.
The analytics value chain can also help identify how data assets can be used in multiple areas and where the organization may need to focus on standardization to ensure data liquidity and consistent meanings across data sets coming from different parts of the organization and from collaborators as well.
We discuss this issue in significantly more depth in the report but a brief overview of the dimensions of data analytics follows:
- Organizational Awareness: overall understanding of data and analytics as an asset and an assessment of data literacy
- Risk Management: the ability to identify, prioritize, manage and mitigate risk throughout the enterprise (privacy (HIPAA), security)
- Stewardship: a systematic approach to custodial care and responsibility for data concerned with enhancement and control of the asset
- Data Quality: processes that ensure accuracy and consistency of data across the enterprise
- Information Lifecycle Management: systematic, policy-based approach to information collection, use, retention and deletion
- Security, Privacy and Compliance: controls and technologies to protect data from misuse
- Metadata Management: high order data on the data that provides all relevant attributes of data created and stored
- Audit and Reporting: processes to monitor and measure data value, risks and efficacy of overall governance
Developing a systematic data governance structure could be viewed not purely in administrative or risk mitigation terms, but rather as an organizational design approach to optimizing the use of data across the HCO.
One of the biggest challenges with many health IT tools is integrating analytics within the clinical workflows of the organization. Building a strong data governance program is absolutely essential to designing a more data-driven HCO. It provides the cornerstone piece to the health data value chain that will increasingly extend beyond the HCO enterprise.
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