Posts Tagged ‘Metadata’

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.

SUDS-FIN Homework – Payroll and Labor Expense Management – 10/16/2012

Tuesday, October 16th, 2012

Review the definitions that were started during the meeting Tuesday and please provide any feedback through the comments functionality.

We’ll continue to work through the terms in the business questions and work on creating unambiguous definitions.  You can view the current questions (and terms in question) here:

Feel free to begin to critique other definitions and provide feedback through the comments functionality.

SUDS-FIN Minutes – Payroll and Labor Expense Management – 10/16/2012

Tuesday, October 16th, 2012

Attendees: Bryan Brown (FMCS), Rana Glasgal (UHR), Matt Hoying (FMCS), Marissa Lavelle (FMCS), Lillian Lee (IRDS), Nancy Lonhart (Medicine), Jamie Lutton (FMCS), Elaine Moise (FSS), Lily Ng (FMCS), Shawna Powell-Blunt (Payroll), Tim Reuter (OSR), Kurt Staufenberg (PMO), Andy Zell (FMCS)

Thanks to all of those who used the comment functionality on the wiki ( since the last meeting to continue the discussion.  Unfortunately, it is unlikely we’ll be able to discuss all of the terms in the course of the meetings so it is critical that we all find time to continue the discussion online between meetings.

Starting this week, Matt will start sending out the definitions that have been discussed for final approval.  If there are no issues voiced within the following week, the definition status will be updated to “approved.”

These minutes can be found at and additional documentation on today’s discussion can be found at  If you would like to listen to a recording of today’s discussion at


SUDS-FIN Homework – Payroll and Labor Expense Management – 10/11/2012

Thursday, October 11th, 2012

Review the definitions that were started during the meeting Thursday and please provide any feedback through the comments functionality.

Feel free to begin to critique other definitions and provide feedback through the comments functionality.

SUDS-FIN Minutes – Payroll and Labor Expense Management – 10/11/2012

Thursday, October 11th, 2012

Attendees: Isabel Alverez-Valdez (FMCS), Bryan Brown (FMCS), Dora Brown (OSR), Jesse Charlton (RFCS), Cathy Downs (FMCS), Nick Hartman (UG Admission), Marilou Hemenway (RFCS), Matt Hoying (FMCS), Lillian Lee (IRDS), Nancy Lonhart (Medicine), Jamie Lutton (FMCS), Cindy Martin (UHR), Lori McVay (FSI), Elaine Moise (FSS), Lily Ng (FMCS), Samir Pandey (DMR), Andrea Perez (FMCS), Tim Reuter (OSR), Nguyet Sin (OSR), Marilyn Smith (Earth Science), Kurt Staufenberg (PMO), Abhijit Tambe (FMCS), Kelly Wright (Payroll), Andy Zell (FMCS)


Over the next four weeks we’ll be meeting to discuss and come to consensus on the definitions and derivations of data associated with the Payroll and Labor Expense Management (PLM) Reporting Project.  The goal isn’t to necessarily change the terms that are used in the payroll and labor expense management business process but to produce an unambiguous lexicon of terms that can be used in the course of this project as well as later training and support.  Developing a business glossary early in the project can accelerate the development cycle, minimize rework and take significant pressure off of QA.

To assure the team is building robust and consistent definitions, please review the Data Definitions Best Practices document which can be found at

Unfortunately, there is little chance that we will be able to go through every term in the course of the four weekly meetings.  For this reason, please review the list of terms on the wiki ( and provide input in the time between meetings.  Even the terms that we do cover in the meetings may not get as many revisions as they should.  For this reason, we are going to focus on definitions that are fit for use in terms of the PLM project and will avoid wordsmithing definitions in the course of the meetings.  For non-substantive changes (such as fixing typos or adding words to improve the readability of the sentence) please make your edits in-page using the “Edit” button.  For questions or recommendations around the actual content of the definition or the derivation, please use the comment functionality by clicking the comment button that appears at the bottom of each page.

After a week of online review, any term that has been discussed in a meeting will put up for pre-approval by this group.  If there are no objections to the wording within this group, the term will be considered approved and reviewed with management within two weeks.  When a term is sent out for approval, a non-response will be considered approval.

Next week we’ll focus on terms that are used explicitly in the key business questions that appeared in the survey.  Thanks again for dedicating your time to this critical task.

Going forward, minutes will be posted on the Stanford Data Governance ( website within two days of the meeting.

HR Data Stewardship Team – Data Forum Invite – Homework

Wednesday, October 3rd, 2012

Next week we’ll be discussing two items:

1)     What is the “Reports To” field currently used for?

  1. What should it be used for?
  2. What distinct types of “Reports To” should exist?

2)     How one can identify managers in the current system and/or logically.

Both of these items will involve agreeing on data definitions and analyzing current practices around these concepts.  Please come prepared with insight on these two items.  If possible, please email Rana ( your input by 10/15/2012 .

Additionally, please continue to contribute to the confluence pages to track:

1)     HR Data Stewardship opportunities ( and

2)     Identified HR Data Issues (

Data Definition Best Practices

Thursday, September 20th, 2012

Stanford DG recently created a draft of data definition best practices for our data stewardship groups.  This is still in draft form so please let Matt know if you have any feedback.

Link to Data Definition Best Practices

HR Data Stewardship Team – Program Charter – Homework

Wednesday, September 5th, 2012

Review the most recent draft of the charter that was sent by Rana and submit comments and recommendations to Rana ( and Matt ( by Monday, September 17th.  We’ll review the final draft of the charter at the beginning of the next meeting.  Additionally, two pages have been created on the confluence page to track HR Data Stewardship opportunities ( and Identified HR Data Issues (  In addition to data definitions, these will be the subject of our next meeting, so please begin to identify areas of focus for the first few weeks of work.



HR Data Stewardship – Scoping Meeting – June 14, 2012

Thursday, June 14th, 2012

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:

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: (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, we were able to get some experience with the activities around defining data (, assessing data gaps and making a formal request for change (RFC) (

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.

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.