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 Katherine Downing, MA, RHIA, CHPS, PMP
Data governance (DG) is the sub-domain of information governance (IG) that provides for the design and execution of data needs planning and data quality assurance in concert with the strategic information needs of the organization. Data governance includes data modeling, data mapping, data audit, data quality controls, data quality management, data architecture, and data dictionaries. DG collaborates with enterprise information management (EIM) in functional components essential to the enterprise plans for information organization and classification.
AHIMA’s Information Governance Adoption Model (IGAM) calls for data governance as one of several tenets of IG structure. One of the best practices for maintaining DG is through the use of data stewards; these individuals are central to the establishment and sustainability of DG.
The data steward is responsible for either a specific system or set of data. They serve as the data governance subject matter expert to the business unit they represent. They are trained and enabled to lead the execution of data quality initiatives, including remediation where it is needed. They are acting in a cross functional method through policies, processes, and procedures regarding data. They enforce these policies as appropriate and provide training and expertise wherever needed. They may not manage the technical aspects of metadata management or master data management, but they serve as a business owner of the data and work to make sure that high quality data is produced. According to the AHIMA Data Quality Management model, data quality attributes include:
AHIMA cites these data quality charasterics in the “Data Quality Management Model” Practice Brief.1
Data governance efforts are working to ensure data is consistent, trusted, and usable across the organization. This may require data dictionaries, business rules, data flows, and accountability as well as issue identification and prioritization for issues with the data in their unit. They may, along with the IG committee, be keeping quality dashboards or a roadmap to get the data to a certain point. These data stewards may be working in a project-oriented way during initial start up and then their work becomes part of the business model. They lead during both phases.
The data steward is a bridge between the information governance committee and the workforce. The stewards help to set priorities and impact assessments for identified issues. They would create a plan and may provide recommendations for issue resolution. They are the experts but they may need to research and analyze any identified issues to gain a better understanding of how to remediate. The data steward would develop and maintain business definitions and coordinate consistent definitions with other business lines.
The data steward is a champion for information governance topics within the organization. They discuss why information governance activities are important to the organization. They are known to the rest of the business unit as the initial point of contact for questions about definitions, training, and actions of the workforce, as well as a mentor.
How do you ensure data steward success?
The named data steward needs to be an expert in the business unit they will represent, but this individual also needs to be a leader in the organization. They will be expected to perform their current role and the data steward role so they must be an experienced member of both the team and the organization. The data stewards need a model from the information governance committee that includes requirements and responsibilities for the role so they can succeed. They need support of the senior leaders in their individual business units/departments. They need to be accountable for education, training, tools, and resources that will be used for their department. These tools should be shared across the organization for consistency.
- “Data Quality Management Model (2015 Update).” Journal of AHIMA 86, no.10 (October 2015): expanded web version. http://bok.ahima.org/doc?oid=107773#.V0dcEHp_KR4.