Posts Tagged ‘HR’

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 (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).

HR Data Stewardship Team – Data Forum Invite – Minutes

Wednesday, October 3rd, 2012

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]”

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 (rana@stanford.edu) and Matt (mhoying@stanford.edu) 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 (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.

 

 

HR Data Stewardship Team – Program Charter – Minutes

Wednesday, September 5th, 2012

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 19th, 2012.  Please let Rana know if you will be unable to attend in person.

Project Independent Data Stewardship in HR

Wednesday, August 29th, 2012

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

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: 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.

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: http://dg.stanford.edu/wp-content/uploads/2012/04/DG-HRDD-MINUTES-20120410.docx

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)

Minutes

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 http://dg.stanford.edu/wp-content/uploads/2011/11/DG-Pres003.pdf.

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.

HR Metrics Data Definition Meeting Minutes– 3/28/2012

Wednesday, March 28th, 2012

Attendees:   Dawn Freeman (Human Resources), Rana Glasgal (Human Resources), Matt Hoying (Data Governance), Larry Niemeyer (Humanities and Sciences), Martha Wood (Business Affairs)

 

After conversation with UHR regarding the appropriateness of Temp-Casual Headcount on the dashboard, it has been decided that they will request it is removed from the dashboard completely.

As a result of conversation about the calculation of Talent Source, the previously approved definition of Employee Movement was amended to exclude reclassifications and bring it more in line with the current implementation on the HR Metrics Dashboard.  Final definitions were agreed upon for the remaining terms (see below).  Revision of the derivation of terms will be handled as a separate activity as they are reviewed with UHR.

There is no further update on the implementation of the addition of the JRC:PRO action:reason code combination in PeopleSoft.  Communication on the availability of this code for use will come through normal HR communication channels.

  • Research: A research Job is a Job primarily involved with research activities at, or affiliated with, the university.  On the HR Metrics Dashboard Expenditure Type Codes are used to categorize Jobs as Administration or Research.
  • Administration: An administration job is a Job primarily involved with supporting Research, teaching and other university activities. On the HR Metrics Dashboard, Expenditure Type Codes are used to categorize Jobs as Administration or Research.
  • University Employee Movement – University Employee Movement is a count of Employees leaving a Position but remaining in a Position within the University through an action other than Reclassification during a specified time period.
  • Talent Source – Talent Source is the ratio that indicates the degree to which Stanford is developing current Employees vs. hiring new Employees during a specified time period.

As noted in the Homework section we’ll be having one last dedicated meeting for the HR Metrics Phase I Data Group where we’ll review and discuss what went well, what went poorly and how we can improve these meetings if they continue on other projects.  I’d like to get as many people to attend this discussion as possible so I can get a lot of feedback.  I’ll be contacting each of you individually to try and work out a time that works for the most people.

In the next few weeks, I’ll be holding another meeting to discuss the creation of a HR Subject Area Data Stewardship Committee.  Developing data definitions will definitely be a portion of this group’s ongoing responsibilities but I’d like to discuss what other types of stewardship activities would be valuable and reasonable in our organizational environment.  A menu of many of the responsibilities that this Stewardship Committee could assume can be found on the DG website at http://www.stanford.edu/dept/pres-provost/cgi-bin/dg/wordpress/?p=235.  Please don’t hesitate to contact me if you have any questions or would like to discuss this before the meeting.

HR Metrics Data Definition Meeting Homework – 3/21/2012

Wednesday, March 21st, 2012
  1. Find More Participants (especially for the final meeting!)
  2. Review Open Questions
  3. Prepare for “6-Minute Definitions”
  4. List of Org types

 

Please continue to evangelize the Data Definitions/Metadata Development/Data Stewardship process to your peers and invite them to attend one of our weekly meetings.  Additionally, anyone that has an interest in the content of our meeting is welcome to attend (regardless of their business function) and as we move more into stewardship, will become an invaluable resource in understanding the impact of and business requirements around HR data throughout the organization.

While investigating Talent Source, a series of questions came up around the various Job Action:Reason groupings. Larry will be chase down the technical explanations but there are still a number of issues that relate to the business definition.  Please come prepared with definitions on the following terms and consider whether the current technical definition (derivation) accurately captures the intention of your definition.

Term Action:Reason Code(s) (‘*’ represents all)
Hire HIR:*
Rehire REH:*
Hires (as used on Overview/Workforce Activity Summary) HIR:* or REH:*
Reclassification (by logically separating the “Hires” out of Workforce Activity Details/Talent Source Analysis) JRC:JRC or JRC:ZJC, XFR:PRO
Promotions (as used on Overview/Workforce Activity Summary) JRC:JRC or JRC:ZJC, XFR:PRO
Terminations (as used on Overview/Workforce Activity Summary) TER:* or TWB:* or RET:*
Movement (as used on Overview/Workforce Activity Summary) XFR:MOV or XFR:LAT or XFR:ZFF or XFR:ZTF

We are down to our last few definitions!  Beyond the previously mentioned open issues, the only remaining terms are Research and Administration.   Currently the definitions are rather technical so please look to create an understandable business definition that clearly speaks to what these term represent in the context of the dashboard.  Be sure to note that these values are based off of Expenditure Type Code rather than the amount of the actual activities that are performed.

In the time before our meeting next week, you can leave comments on the pages (by clicking on the “Add Comment” link at the bottom of the page).  Please use the criteria for a well-defined term from the wiki (https://asconfluence.stanford.edu/confluence/x/1wCGFg) as well as your knowledge of the business.  We will continue “6-Minute Definitions” in our next meeting.

The remaining terms are:

On Help Concept (Click for Current Metadata) Status Review Date
X Administration In Progress  
X Research In Progress  
X Talent Source In Progress  

We are still looking to accumulate the various terms for organizations and groupings of organizations at Stanford.  Currently, the HR Metrics Dashboard has School/VP, Area, Sub-area, Department, Sub-department.  What other terms are used in your area of the university?  Please see the homework from January 18th for examples.

HR Metrics Data Definition Meeting Minutes – 3/21/2012

Wednesday, March 21st, 2012

Attendees:   Dawn Freeman (Human Resources), Rana Glasgal (Human Resources), Matt Hoying (Data Governance), Larry Niemeyer (Humanities and Sciences), Martha Wood (Business Affairs)

We believe we have gotten all known, necessary approval for the PeopleSoft JRC:PRO Request for Change (RFC).  We will now work with University HR (UHR) to create a training and rollout plan.  This may include the creation of a Job Aid, a presentation at the next HUG or HRM meeting and/or communication through one of the available mailing lists.  If you have any ideas about other methods or opportunities to communicate this change, please bring it to the next meeting so it can be included in the training plan.

Due to conflicts, we will be (hopefully) presenting a bit about the work of the data team and continuing recruiting more members at the June HRM meeting.

We are still looking for additional information on the following terms.  During the meeting a few more questions about Talent Source came up.  Larry will try and chase down the answers to the open issues prior to the next meeting.  Additionally, we will request that the help file contains links to internal page anchors on its next update for clarity.

See below for more details but there is a basic question as to how we are defining research vs. administration jobs at the university.  One focuses on the types of activities that are actually performed and the other focuses on funding sources.  Please think about which of these is more accurate and appropriate.  Additionally, if there are other potential, mutually exclusive job groupings (for example, IPEDS defines the job groups: Research, Other (maps to our admin kind of), Teaching, Mix and Public Service), the we have to be sure that our current definitions take them into account.  For example, if the Teaching job groups are logically separate from admin and research, then we have to be careful not to define admin as “non-research jobs”.

  • Research: Option 1: A research job is a job primarily involved with performing research activities at, or affiliated with the university.
    Option2: A research job is a job primarily funded through research funding sources.
  • Administration: Option 1: An administration job is a job primarily involved with performing no research support at, or affiliated with the university.
    Option2: An administration job is a job primarily funded through non-research funding sources.
  • Job FTE – The number on the dashboard looked a bit lower than expected.  Before a definition is finalized, the group would like to know how it is currently calculated on the dashboard.  These values may be directly from the PeopleSoft field of the same name, but please consider what are the actual business definitions of Job FTE and Position FTE.
  • Temporary/Casual Headcount – As the group understood it, the HR Metrics Dashboard only includes Benefits-Eligible Employees.  This term should not be relevant to the Dashboard currently.  If one filters the Class Indicator by “Casual” or “Temporary,” a single record is returned.  (The record also shows a job family of “Contingent.”)  Can this be explained?  Larry will find out the source of this record.
  • Talent Source – What should the numerator of talent source include?  This question should be answered both from a business term perspective (e.g. “University Employee Movement” and “Promotions”) as well as from a technical stand point (e.g. JRC:JRC, XFR:LAT, XFR:MOV, DTA:RLS, XFR:PRO, JRC:PRO, JRC:ZJC).  Currently, this value appears to be only Promotions/Hires on the Overview page (data hidden on purpose):
 

Hires Terminations

Hires / Terminations

Flow

XX17

XX60

XX.29%

 
 

Promotions Hires

Promotions / Hires

Talent Source

XX56

XX17

XX.52%

 
 

Movement Terminations
Turnover

XX58

XX60

But appears to be calculated using Reclassifications (agreeing with the current Help file definition) on the Workforce Activity Details page:

Source Event Type is equal to HIRREH
or Source Event Reason is equal to JRC:JRCJRC:ZJCXFR:PRO

… which seems to include reclassifications as well.  Are there any other Action:Reasons that should go into this calculation?  Should DTA:RLS be included or is it always a code that is used in conjunction with one of the other employee movement codes?  Should POS:OWN be included and if not, how should the definition be written to explicitly exclude it?

  • Please review University Employee Movement once again as there were some questions about the current definition brought up in the meeting (Specifically about whether it requires defined scope).
  • Please review Terminations.  While looking to see if the formulas for Flow matched between the Overview page and the Workforce Activity Details page, it appears that the Terminations should include TWB as well as TER.  This is an example of why definitions usually use only business language and separate the derivation (or formula for calculation) from the business definition.

The week after we’ve finalized our last terms, we’ll have a meeting to discuss the team going forward and the transition to more active stewardship.  This will be a very interesting meeting and important in establishing sustainable data governance at Stanford.  Please try and make it to this meeting (probably around 3/29/2012) and extend the invitation to anyone else that you believe may be interested.  This meeting may be rescheduled for a different date and time to ensure the highest level of participation possible.