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Enterprise Organization Structure Task Force Charge Letter

(Original on UW Office of the Provost letterhead)

May 8, 2020

Enterprise Organization Structure Task Force
Ann Anderson, Associate Vice President, Finance, Co-Chair
Jason Campbell, Assistant Vice Provost, Planning and Budgeting, Co-Chair
Beth Britt, Director of Analytics, UW Medicine IT Services
Rachel Gatlin, Executive Director, HR Benefits, Analytics and Information Systems
Jim Kresl, Assistant Vice Provost, Office of Research
Karen Matheson, Manager, Integrated Service Center Application Management, Enterprise Services, Finance
Rob McDade, Director, Enterprise Information, Integration & Analytics, UIW-IT
Arlene Murray, Business Systems Analyst, Platform Integration, Finance Transformation, Finance
Jim Phelps, Director Enterprise Architecture & Strategy, Research Computing & Strategy, UW-IT
Paula Ross, Platform Integration Director, Finance Transformation, Finance
Erick Winger, Business Systems Analyst, Finance Transformation Core Financials, Finance

Dear Enterprise Organization Structure Task Force,

Thank you for your willingness to serve on the Enterprise Organization Structure Task Force. As the UW moves forward with Data Governance and Finance Transformation, it is important that we review the processes for managing our enterprise organization structure across business processes, data domains, and numerous IT systems.

Administrative Policy Statement 01.01 establishes clear delegations from the Board of Regents, President, and Provost. However, there is a lack of clarity about the business processes for maintaining this organization structure and how the structure is integrated across disparate IT
solutions. Data inconsistencies, quality issues, and gaps lead to incomplete or inaccurate information that continually has to be reconciled.

We charge you with addressing the following:

  1. Reviewing the current business processes for managing enterprise organization structure, including, but not limited to, policy approval processes, establishment of organization codes that coincide with the policy, implementation processes, communication, and potential downstream impacts.
  2. Identifying what the future governance structure and administrative process should be for overseeing and as needed modifying the organizational structure, including communication to stakeholders.
  3. Identifying options for making the UW overarching organizational structure, as defined within APS 01.01, technically agnostic and also technically consumable by other business process and technologies. The options should not be driven by FT or based on the foundational data model in Workday since that model is not meant to be a digital representation of APS 01.01.

We ask you to focus on pragmatic recommendations that promote efficiencies. Please base both the future state and the suggested path forward on what can be done with existing funding and resources, and what can be done with a modest investment in funding and/or resources. Actual enhancements to the process and a technical solution to support the process will be phase two and, as such, is beyond the scope of this Task Force.

Data Governance uses a maturity model for major initiatives to identify current and desired end-state maturity levels for each major project. Given competing priorities and resources, not all projects will aim for full maturity. The goal for this task force is to take the UW’s process managing its enterprise organization structure from a data governance maturity level of 2, repeatable, to an end-state of 3, defined (see Appendix for the complete maturity model).

To help answer questions and provide guidance, as needed, your co-chairs will meet briefly and regularly with Marisa Nickle, Ann Nagel and Phil Reid in their Data Governance roles.

A report with findings and recommendations should be provided to the Data Governance Steering Committee and Operational Committee as soon as feasible and in sync with the Finance Transformation milestones and timelines. Thank you again for your willingness to help with this critical work.

Janelle Nelson, Assistant to Ann Anderson, will contact you to begin scheduling the meetings. Elise Glassman, Project Manager in Office of Planning and Budgeting, will work closely with and support the chairs with this effort. Thank you again for your willingness to help with this critical work.


Mark A. Richards
Provost and Executive Vice President for academic Affairs
Professor, Earth and Space Sciences


Maturity Model for Major Data Governance Initiatives
The maturity model is used to help measure the value of major data governance initiatives (e.g. data map, data stewardship, country codes). For major data governance initiatives, the data governance committees evaluate the maturity level at the initial (current) state, intended future state, and progress towards the intended future state. These metrics help the establish the baseline, create clarity on where we are going, and align expectations with the steering and operational committee, related governance groups, and our colleagues across all three campuses.

The metrics along with other high-level and pertinent information about each initiative will be published on the data governance website.

Maturity Level 5 Optimized Continuous improvement on major data governance initiatives considered “optimized” help the UW leverage data and data resources to respond to opportunity and change in a consistent and cohesive manner
Maturity Level 4 Managed Major data driven initiatives pursued through the data governance committees will be considered “managed” if they are connected to the UW strategy. Policies, best practices and supporting resources help ensure there’s a common understanding of data governance and management principles across the UW
Maturity Level 3 Defined To be considered “defined,” major data driven initiatives pursued through the data governance committees will be proactive, rather than reactive, and involve relevant data stewards, but are not connected to the UW strategy and priorities
Maturity Level 2 Repeatable Data driven initiatives are considered “repeatable” if they are regularly planned, executed, and measured in silos
Maturity Level 1 Initial “Initial” data driven initiatives are ad hoc, inconsistent, undefined, and/or lead
to incongruent results
Developed by Ann Nagel for Data Governance Operational Committee based on:

  1. Various conversations at UW with the Data Governance Task Force, Data Custodians, and Data Governance Committee Members
  2. High-level review of maturity models published by EAB, Carnegie Mellon, DataVersity, Prosci, Wikipedia, AICPA, Department of Defense, Marisa Sanchez Organization Dev & Change Mgmt Consulting

Adopted by Data Governance Operational Committee at the 3/5/2020 meeting