Archive for the ‘Stewardship’ Category

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

• https://asconfluence.stanford.edu/confluence/display/~mhoying/Organization+Suspense+Account
• https://asconfluence.stanford.edu/confluence/display/~mhoying/Earnings
• https://asconfluence.stanford.edu/confluence/display/~mhoying/Expenditure
• https://asconfluence.stanford.edu/confluence/display/~mhoying/Labor+Schedule
• https://asconfluence.stanford.edu/confluence/display/~mhoying/Actual+Pay
• https://asconfluence.stanford.edu/confluence/display/~mhoying/Base+Pay
• https://asconfluence.stanford.edu/confluence/display/~mhoying/Premium+Pay
• https://asconfluence.stanford.edu/confluence/display/~mhoying/Supplemental+Pay
• Additionally, please continue to review the definitions from the first meeting last week: http://dg.stanford.edu/?p=601

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: http://www.stanford.edu/dept/pres-provost/cgi-bin/dg/wordpress/wp-content/uploads/2012/10/BusinessQuestionWords.pptx

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

• https://asconfluence.stanford.edu/confluence/display/~mhoying/Evolving+Financial+Reporting+-+Payroll+and+Labor+Expense+Management

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 (https://asconfluence.stanford.edu/confluence/display/~mhoying/Evolving+Financial+Reporting+-+Payroll+and+Labor+Expense+Management+Terms) 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 http://dg.stanford.edu and additional documentation on today’s discussion can be found at http://www.stanford.edu/dept/pres-provost/cgi-bin/dg/wordpress/wp-content/uploads/2012/10/Meeting_data_stewardship_101612.docx.  If you would like to listen to a recording of today’s discussion at https://stanford.webex.com/stanford/lsr.php?AT=pb&SP=MC&rID=62651322&rKey=2d45a2545d0a2db7.

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 http://www.stanford.edu/dept/pres-provost/cgi-bin/dg/wordpress/wp-content/uploads/2012/09/DG-Data-Definition-Best-Practices.docx.

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 (https://asconfluence.stanford.edu/confluence/display/~mhoying/Evolving+Financial+Reporting+-+Payroll+and+Labor+Expense+Management+Terms) 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 (http://dg.stanford.edu) website within two days of the meeting.

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 (rana@stanford.edu) your input by 10/15/2012 .

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

1)     HR Data Stewardship opportunities (https://asconfluence.stanford.edu/confluence/pages/viewpage.action?pageId=477757764) and

2)     Identified HR Data Issues (https://asconfluence.stanford.edu/confluence/display/~mhoying/Known+HR+Data+Issues).

October 3, 2012

Attendees: Bryan Brown (FMS), Dawn Freeman (Human Resources), Rana Glasgal (Human Resources), Steve Holoien (Transact COE), Kulneet Homidi (Dean of Research), Matt Hoying (Data Governance), Sal Mancuso (Business Affairs), Larry Niemeyer (Human Resources), Shawna Powell-Blunt (Payroll)

Final tweaks were made to the invite email for the Data Issues Open Forum and all attending approved the content.  The group agreed that we will use the time associated with a HUG meeting.  Kulneet will look for availability of the Clark Center to host the first open forum on (or around October 30, 2012).  Once we have a room, Rana will manage the invitations using Vicky’s HR users mailing list.

The group brainstormed new data issues and opportunities and documented their draft form in the confluence site: https://asconfluence.stanford.edu/confluence/pages/viewpage.action?pageId=477757764.  Additionally, as specific concepts and terms came up, they were detailed on confluence here: https://asconfluence.stanford.edu/confluence/display/~mhoying/Targeted+HR+Data+for+Definition.

In discussing some of the open issues and opportunities around HR data, the group discussed voluntary vs. involuntary terminations.  Although a dedicated discussion was out of scope for this meeting (and will be scheduled for a future SUDS-HR meeting), a few important points were made that should be captured.

1)     Misconduct and layoff are “Involuntary” termination reasons (my original notes said that “misconduct is only ‘involuntary’” but then the next line said layoff was involuntary)

2)     There may be some sensitivity around documenting voluntary vs. involuntary terminations or listing rehire status in official records.  “[Some groups] don’t even like this information in the comments [field]”

Review the most recent draft of the charter that was sent by Rana and submit comments and recommendations to Rana (rana@stanford.edu) and Matt (mhoying@stanford.edu) by Monday, September 17^th.  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 (https://asconfluence.stanford.edu/confluence/pages/viewpage.action?pageId=477757764) and Identified HR Data Issues (https://asconfluence.stanford.edu/confluence/display/~mhoying/Known+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.

Attendees: Bryan Brown (FMS), Justin  Fiske (SoE), Rana Glasgal (Human Resources), Steve Holoien (Transact COE), Matt Hoying (Data Governance), Sal Mancuso (Business Affairs), Larry Niemeyer (Humanities and Sciences), Shawna Powell-Blunt (Payroll), Renee Sombilon (H&S)

The meeting focused on reviewing and editing the initial draft of the charter provided by Rana.  We had a lot of good discussion around how we would correctly scope the responsibilities of the team and how data stewardship activities could lead to operational improvement.  Key activities would include:

• Defining terms and concepts that support key HR Business Processes
• Developing data quality and business process improvements that increase the effectiveness and efficiency of the HR function
• Supporting the data aspects of system and application projects that include or are dependent on HR data

In addition to the core group, subcommittees may be formed to focus on specific mini-projects.  These subcommittees may bring in additional SMEs that are not part of the ongoing Data Stewardship team to support their specific project goals.

The discussion then moved to how we can assess the effectiveness of our data stewardship efforts can be measured for the purpose of baselining and goal setting.  Suggestions included:

• Count of users with create/update rights at data touchpoints
• Number of people trained
• Survey results

Finally, to assure that forward momentum is maintained, each topic will be limited to discussion during only one face-to-face meeting.  Further work on the topic will be limited to online collaboration using the wiki.

The next meeting will be held in two weeks, on Wednesday, September 19^th, 2012.  Please let Rana know if you will be unable to attend in person.

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 21^st at 1:00 PM PDT to finalize primary and secondary tasks and goals, business function of focus and the composition of the identified teams.

Attendees:  Sonia Barragan (RMG), Matt Hoying (Data Governance), Colleen James (RMG), Angel Mayorga (RMG), Kathleen Thompson (RMG)

Deliverables:

1. List major terms that will be considered to be in the scope of the project (all)
2. Create wiki pages for known terms (Matt): https://asconfluence.stanford.edu/confluence/display/~mhoying/Home
3. Schedule weekly meetings (Kathleen)
4. Send PDF of 2001 definitions to team (Matt): http://dg.stanford.edu/wp-content/uploads/2012/06/Research_Data_Definitions_Summary.pdf
5. Research composition of 2001 UMG Working Group on Data (Matt)
6. Draft information flow diagram (Matt)

This meeting focused on developing the scope, effort duration and deliverables to be produced as part of this effort.  The discussion began with specific examples of the impact of inconsistent/unclear data definitions and other data quality issues.  As data definitions are developed and data quality risks and errors are identified, this group will make a significant effort to document the associated business impact or operational risk.

The definitions developed in the course of this activity will not be considered “Approved” or “Institutional Definitions” until they receive formal sign-off from all necessary business and technical stakeholders.  A formal approval process and executive data stewardship council in this domain will be developed shortly.

An additional key activity will be the communication of these definitions to a broader set of stakeholders.  This training will improve institution-wide understanding of this information, reduce operational risk, and increase trust in the underlying data while providing a valuable source of feedback for the definitions produced by this group.

The focus of the definitions (https://asconfluence.stanford.edu/confluence/display/~mhoying/Metadata-Definition) will be on the major data entities associated with the lifecycle of a proposal and award.  Using the sample lifecycle (http://dg.stanford.edu/wp-content/uploads/2012/06/SAMPLE-LIFECYCLE.docx) produced by RMG two weeks ago, the team will work on listing the concepts and terms that will be defined as part of this effort.  In this phase we will not be focusing on defining all of the attributes of these entities or fully describing the details/derivations of the entity subtypes.  The aim will be to make this an eight to ten week effort with weekly meetings of an hour and a half.  Kathleen will schedule the next meeting, most likely, next Thursday, June 14^th in the early afternoon.

In addition to this list of terms, the team will work to develop a graphical timeline of the proposal and award process, pointing out significant state changes and key dates.

The final deliverable of this short project will be a high-level information flow and CRUD (Create, Read, Update and Delete) matrix that displays where key types of data reside and the activities at those locations.  Matt will be sending out a draft diagram to the team shortly to use as a starting point.

Finally, at the end of the meeting, the team developed a draft definition for the term “Proposal.”  This has been posted on the Data Governance Wiki and all team members are encouraged to further refine the definition on the wiki or make comments regarding the fitness of the definition.  Please also feel free to share the draft definition (on the wiki) with other subject matter experts and get their perspective.

Defined Terms:

Proposal: A proposal is a formal funding request on behalf of the University for external funding to support a scope of work defined as a Sponsored Project. https://asconfluence.stanford.edu/confluence/display/~mhoying/Proposal

In the course of daily activities, if any of the team members come across data quality issues, opportunities or develop out-of-scope definitions, please forward them to Matt for compilation.  Please reach out to Matt with any questions, corrections or additional information about this subject.