Data Governance Has an Inextricable Link to Information Governance

Keep up with the latest on information governance as this key strategy emerges for addressing a myriad of information management challenges in healthcare. This blog will highlight the trends and opportunities IG presents for ensuring information is treated as an organizational asset.


By Robyn Stambaugh, MS, RHIA

 

The delivery of trustworthy information through an information governance (IG) structure is predicated on reliable data—best achieved through proper governance of data. Clarity on the difference between the two disciplines and the intrinsic links that provide the critical components for the creation of reliable, trustworthy information is necessary to implement effective governance initiatives and influence positive change in data and information outcomes.

Useable and trustworthy data is based on the premise of continual data management practices that are comprehensive in nature and adhered to by the workforce on a daily basis. As a critical sub-domain of IG, data governance (DG) is the framework that establishes enterprise-wide management of data assets. Components within DG establish authority, control, and accountability for data as well as the delineation of policies, standards, and practices that allow for effective use of data.1 As the primary contributor to the strength of information, DG establishes data integrity, which in turn allows for actualization of information value.

As a DG structure allows for the transformation of data to an asset, so too does incorporation of an IG framework impact information. IG provides a platform for the articulate management of information; IG is a pathway to highly functional business intelligence and an adept organization. AHIMA defines information governance as “an organization-wide framework for managing information throughout its lifecycle and for supporting the organization’s strategy, operations, regulatory, legal, risk and environmental requirements.”2

What enterprise-level domains within healthcare DG support the structure and mission of IG?

  • Data Life Cycle Management determines standards around data for an organization: collection, capture, retention and storage, access and distribution, archival, and disposition.
  • Data Architecture is a blueprint that is the foundation of an information system; articulated through models and artifacts. (i.e., documentation of the data needs for an organization)
  • Metadata Management enables search, retrieval, and use of data and other information resources (managing the data about data i.e., user access logs).
  • Master Data Management ensures data has a consistent meaning and articulates a single source of truth.
  • Content Management allows data of any type to be organized, categorized, stored, and published for use and reuse through policies and procedures and technology.
  • Data Security Management provides a means to protect and secure data for individuals and organizations in compliance with government mandates and regulatory bodies.
  • Business Intelligence (BI) Management is policies, procedures, processes, applications, and technologies used to gather, store, analyze, and provide a means to use data to enhance organizational decision-making.
  • Data Quality Management ensures that data meet quality standards for business usage.
  • Terminology Management is necessary in healthcare to manage numerous unique language and classification systems; establishes a source of truth for terminology in the organization.3

Consider the following example to better understand how a domain(s) of governance over data and information impacts data input and information output:

  • Data Governance
    • Metadata Management, Master Data Management, and BI Management
      • Facilitates the ability to find and use data effectively using a standardized meaning; provides a structure that enables effective business decisions based on dependable data. Data assets are the building blocks to reliable information outputs.
    • Information Governance
      • Inventory Asset Management
        • Information Asset Inventory
          • Centralized Inventory classifies information into any category deemed to be useful or relevant to the organization. For example, categories may designate which information is business-critical (i.e., information related to direct clinical care or financial operations). The following are examples of the types of information assets found in an inventory:
            • Quality and performance data
            • Claims data
            • Financial data
            • Risk and Safety data, etc.
          • An information asset inventory may include information about each of the asset types including but not limited to:
            • Asset name
            • Asset description
            • Type of asset (paper, electronic, cloud)
            • Classification (public, private, confidential, classified)
            • Security protections
            • Data steward
            • Retention period
          • Inventory asset management allows organizations to keep track of their information assets in one location. This helps to ensure that all information assets are governed and monitored throughout the information lifecycle
            • The management of information assets leads to better decision making from higher quality information outputs.

 

A more comprehensive explanation of an information asset inventory can be found in AHIMA’s practice brief “Information Asset Inventory of Information Governance.”

If one were to dissect every domain under DG it would consistently prove out its critical impact on the integrity of data as well as the ripple effect it has on the purity and usefulness of information for an organization. Clearly, the domains of governance over data impact the ultimate determination of value under the auspices of IG. To gain the full value proposition from an organization’s data and information, both domains of governance are necessary.

Notes
  1. Johns, Merida L. Enterprise HealthInformation Management and Data Governance. Chicago, IL: AHIMA Press, 2015.
  2. “Information Asset Inventory for Information Governance.” Journal of AHIMA88, no.2 (February 2017): 40-43 (Practice Brief expanded online version).
  3. Johns, Merida L. Enterprise Health Information Management and Data Governance.

 

Robyn Stambaugh is an independent consultant.

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