Posts Tagged ‘Project’

What is Data Governance?

Friday, May 12th, 2017

Data Governance (DG) is a cross-functional set of roles, policies and enabling technologies that work together to ensure that an organization is getting the maximum net benefit out of its data assets. To be both successful and sustainable, a DG program must be integrated with business and IT processes throughout the organization.

The fundamental purpose of all DG programs is to improve the effectiveness and efficiency of business processes. Critical business processes are especially sensitive to the quality of data, and the failure of such processes may have far-reaching impacts. Quality data that is “fit for use” across the organization can only be developed and maintained through the collaboration of a diverse set of data stakeholders. This group must commit to formalized responsibilities, policies and procedures around the effective management of data. All DG procedures, including data quality remediation and master data standardization, are most efficient and effective when they are understood and performed consistently throughout the institution.

To achieve institution-wide commitment, the strategy and structure of each DG program must be designed with the unique priorities, competencies and goals of that organization in mind. This may lead to vastly different DG implementations, even among similar organizations. Additionally, as a DG program grows and technical and business environments change, its strategy and structure must continually evolve to remain effective.

High-quality data is a critical success factor across all functions of any organization. Proactive data management and a well-defined Data Governance program are required for the full value of this institutional asset to be realized.

HR Metrics Phase I Data Definitions Wrap-up

Tuesday, April 10th, 2012

The MS Word version of these minutes (with nicer formatting and graphs) can be found at:

April 10, 2012

Attendees:   Angela Arroyo (Law), Dawn Freeman (Human Resources), Rana Glasgal (Human Resources), Anh Hoang (Human Resources), Susan Hoerger (Medicine), Matt Hoying (Data Governance), Martha Wood (Business Affairs), Kurt Staufenberg (Administrative Systems)


The purpose of this meeting was to discuss the Data Definition Team meetings that were associated with the BICC HR Metrics Dashboard Phase I project.  More about the activities and purpose of this team can be found in the Developing Business Metadata presentation on the UDG website at

If you have any additional feedback or questions about this effort, please contact Matt Hoying.

Since the first Data Definitions meeting led by University Data Governance on 10/20/2011, the team met 17 times with an average of 4.8 attendees per meeting representing ten schools/VPs/functional areas.  At the end of the last meeting the group had completed definitions for 26 in-scope terms and made progress through an additional 13 terms that had been descoped at some point during the process.

In addition to the definition of terms for the HR Metrics Phase I Project, the group identified a gap in the PeopleSoft Employee Action:Reason code combination and produced a formal request for change.  This improvement, when implemented, will support more accurate reporting in HR, reduce the effort needed to accurately track promotions and support the legal reporting requirements for the Diversity and Access Office.  In terms of data stewardship, this group provided metadata and data support to the SoM BI project.

The remainder of the time was spent discussing the value of defining data, what went well and potential areas for improvement.

Value of data definition activity:

  • “This process is critical.  Questions are always coming up about ‘What does this mean?’”
  • “Knowing that consensus was reached on the definitions by a knowledgeable group, really supports trust in the data.”
  • “It is better if we can have this information [about data definitions] before the go live date.”
    • Further discussion pointed out that the earlier in the process that data definitions were finished, the more efficient the development process would be and the less rework that would have to be done later in the project.
  • “[Data definitions are] pretty critical.  It’s amazing how many different definitions exist on the campus.”

What went well:

  • “The minutes posted were excellent, it allowed me to understand what was going on when I didn’t attend the meeting.”
  • “The wiki and webpage made the information much more accessible.  It also helped in that I knew I had the most recent version”
  • The 6-minute definitions were very effective in keeping us on task and producing the definitions without getting off subject.
  • There was really good enthusiasm for a volunteer-based group.

Areas for improvement:

  • Start process earlier so usable definitions and agreed upon derivations are available before they are needed in the project.
  • “I think it is very important to tie this in with training.”
    • Developing the approved definitions can only really make an impact in the organization if it is followed with training.  We need to come up with a process that can be consistently followed to make sure that the right audience is trained on the new definitions (and processes if necessary) and that we can “close the loop” by getting feedback from the audience on these definitions (and processes).
  • “Communication and Marketing.”
    • Not enough of the information produced by the group was communicated outside of the population involved with HR Metrics Phase I.  Additionally, the existence of this team and the data definitions activity were not well marketed outside of the group actively participating.  “More people would want to participate if they knew this group existed.”
    • Before the next HR data definitions effort begins, we will need to design a communication and marketing plan that leverages, our current group members, scheduled HR meetings and the current HR organizational structure.
  • There are additional questions that we should be asking during this process.  “Are we gathering the right type of data?” and “Where are we pulling it from and is it the right source?”
  • The greater part of the participants agreed that weekly meetings were too frequent alongside daily job responsibilities, but we have to be careful as making it significantly less frequent may negatively impact our momentum.
  • We need to increase the amount of work that is done outside of the meetings (especially if the meetings are made less frequent).  The online tools (wiki, webpage, email) would allow us to be much more productive between meetings if we utilized them more.
    • Rana recommended scheduling 15-30 minutes on your calendar for these tasks between meetings.  Without scheduled time, it is too easy to forget about the tasks between meetings.
  • “We need to include more HRAs.  They are the ones that really know the details of the data in the systems.”  “Growing in Data Analytics.”  Additionally, the group should include members of the Transaction Center of Excellence (COE).
  • There needs to be a formal process for data/functionality issue escalation and resolution.
    • Matt is currently working with Cindy and Rana on developing and documenting this process.
  • In the meetings, we need a process to share the screen for those participating remotely.
  • This activity should be paired with“… data profiling [so we] feel good about the data that we provide.”  This can also expose exceptions within the data and help us make more accurate and complete definitions.

BICC Monthly – DG in 2012 Presentation, 2/13/2012

Sunday, February 12th, 2012

Presentation given on 2/13/2012 to the Stanford BICC: DG at Stanford in 2012

Data Governance Services

Saturday, January 28th, 2012

Please contact Matt if you are interested in taking advantage of or would like more information on any DG Services offered by University Data Governance.

BICC Monthly – DG Maturity Model Presentation, 10/17/2011

Monday, October 17th, 2011

DG Presentation: The Stanford DG Maturity Model

BICC Monthly – DG Maturity Model Newsletter, 10/17/2011

Monday, October 17th, 2011

Newsletter 2: The Stanford DG Maturity Model

BICC Monthly – DG Newsletter, 9/19/2011

Monday, September 19th, 2011

DG Newsletter 1: Data Governance Overview

BICC Monthly – DG Presentation, 9/19/2011

Friday, September 9th, 2011

Presentation: Data Governance at Stanford