Stanford Human Resources (HR) has approved the creation of a project independent data stewardship team to provide a renewed focus on the effective management of critical HR institutional data assets. In addition to the stewardship team, the Policy and Process Committee has accepted the responsibility for the executive aspects of data governance in the HR subject area. Further details can be found in linked presentation: Project Independent Human Resources Data Stewardship
Attendees: Mario Acquesta (University HR), Rana Glasgal (University HR), Matt Hoying (University Data Governance), Cindy Martin (University HR)
The purpose of this meeting was to discuss the scope and direction of a project-independent data stewardship (DS) effort around the Human Resources (HR) data subject area. This effort will ultimately include stakeholders from each of the schools and VP areas and is not intended to be a strictly University HR (UHR) effort.
Data Stewardship can be defined as the formalization of accountability for the definition, usage and quality standards of specific data assets within a defined organizational scope. (This definition, along with more information on Data Governance and its relation to Data Stewardship, can be found in our first DG at Stanford newsletter: http://dg.stanford.edu/wp-content/uploads/2011/11/DG-News001.pdf.)
Many of the responsibilities and activities associated with data stewardship occur today, both as part of day-to-day operations and formal projects. What we want to do is document and refine these processes, clearly define the roles, and assign responsibility and accountability to specific individuals in order to assure that we are consistently managing key data. These activities can be divided up into six primary categories: Metadata, Administration, Data Quality (DQ), Audit, Technical, and Support. While the DQ activities could be distributed among Metadata, Audit, Technical, and Support rather than forming a separate category, here they are combined to ensure that there is a concerted focus on DQ.
- Metadata includes the activities around documenting data, instances of data and relationships between data.
- Administration includes the prioritization of data stewardship and data quality activities and the definition, execution and enforcement of data policy.
- Data Quality includes activities related to identifying and analyzing data quality problems and developing metrics, thresholds and remediation strategies to guarantee fit-for-use data.
- Audit includes the ongoing operational activities that compare data policy and data standards with implementation.
- Technical includes the design, development and maintenance of the technical infrastructure (both hardware and software) that enable efficient and effective data stewardship.
- Support includes training and communication activities that ensure the consistent understanding and implementation of data policies and standards.
As it would be unreasonable to expect that we would be able to implement all of the tasks and roles associated with DS immediately, our focus is on selecting the activities that will give us the most benefit with the least effort. The following diagram and chart were used to discuss the relationship between DS activity categories and some of the representative activities within each category: http://dg.stanford.edu/wp-content/uploads/2012/06/DG-Stewardship-Matrix.xlsx (first two tabs).
The beginning of a program like this is often one of the most difficult phases as participants are still learning the purpose and procedures related to each of these activities. Fortunately, through the HR Metrics Dashboard-Phase I project (lessoned learned can be found at http://dg.stanford.edu/?p=497), we were able to get some experience with the activities around defining data (http://dg.stanford.edu/?tag=data-definitions), assessing data gaps and making a formal request for change (RFC) (http://dg.stanford.edu/?p=465).
All attendees agreed that formalization of data stewardship with the HR area should be pursued, as this class of activities potentially has significant value across the university. There will probably need to be two separate teams: one in charge of the general data policies and scoping activities, and a second responsible for developing the specific definitions and data quality metrics. The initial effort will focus on a specific business function where the data is relatively narrow in scope and there is a clear connection between data quality and business impact. This will allow us to demonstrate value and establish baseline policies that can be later applied much more broadly.
A follow up meeting has been scheduled for Thursday, June 21st at 1:00 PM PDT to finalize primary and secondary tasks and goals, business function of focus and the composition of the identified teams.
A key aspect of data governance is the formalization of roles and responsibilities. Although it may not be feasible to move directly to the goal state in the near term, having a well defined target and agreement upon this strategic goal will help align stakeholders and assure that tactical activities have a consistent direction. For this reason, we designed our vision of the Stanford Data Governance Organization. Note that the core councils (Data Governance Council and Data Stewardship Council) are compact and separate from the more operational Data Stewardship Committees and Working Committees. Additionally, to more clearly manage the adoption of centralized policies throughout the schools and functions, the Data Policy Review Board members are asked to become gate keepers for the approval of data policies within their portion of the organization.
Please email Matt with any questions or comments.
DG Presentation: The Stanford DG Maturity Model
DG Newsletter 1: Data Governance Overview