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Office of Data Governance

Data Governance: 

"Planning, oversight, and control over management of data and the use of data and data related sources."

- Definition from the Data Management Association International (DAMA)

 

Data Governance Quick Links

Organizational Structure Data Governance By-Laws Data Entry Standards for General Person 

Data Governance Lifecycle Management & Foundational Activities

Graphic made up of two charts. First chart is Data Governance logo chart. There is a circle with the following words making up the circumference: Policy, Stewardship and Ownership, Culture Change, Strategy, Principles and Ethics, Data Valuation, Data Maturity Assessment, and Data Classification. Within the circle is a four level pyramid titled Lifecycle Management. The topmost segment is labeled Plan and Design. The second level is labeled Architecture, Modeling, and Design. The third level is split into right and left halves. The left half reads Enable and Maintain, and the right half reads Use and Enhance. The fourth level is also split into left and right halves. The left half reads Big Data Storage, Data Warehousing, Master Data Management, Data Storage and Operations, Reference Data Management, and Data Integration and Interoperability. The right half reads Data Science, Data Visualization, Data Monetization, Predictive Analysis, Master Data Usage, Business Intelligence, and Document and Content Management. There is also a section along the bottom rim of the circle labeled Foundational Activities. Within it are Data Protection: Privacy, Security, Risk Management, Metadata Management, and Data Quality Management. In the bottom right of the figure there is a notice of Copyright 2017 DAMA International.Second chart is Data Governance wheel chart. The central circular section contains the title Data Governance, and the spokes make up the different things that comprise this. The spokes are labeled: Data Architecture, Data Modeling and Design, Data Storage and Operations, Data Security, Data Integration and Interoperability, Document and Content Management, Reference and Master Data, Data Warehousing and Business Intelligence, Metadata, and Data Quality. At the bottom is a label reading Copyright 2017 DAMA International.

Data Governance at WSU

Mission:

Several data systems1 exist which the university community can use to inform decision-making, planning and reporting.  The mission of the Data Governance Council (DGC) is to provide oversight to these data systems to ensure data integrity, best practices in data management, reporting standards, information consistency, and security access.  In addition, the Data Governance Council is charged (see Appendix A for official charge) with identifying data and reporting needs related to strategic planning priorities and the sharing of business knowledge across divisions to ensure data and reporting optimization related to the latest business practices within units.  The Data Governance Council provides compliance with the Higher Learning Commission (HLC) requirements related to institutional data used for accreditation.

Data Governance Principles:

  1. Data must be recognized as a valued and strategic enterprise asset that must be managed effectively.   
    •  Accurate & timely data are the critical foundation for effective decision-making, strategic development, customer-service and are the basis for reducing cost and maximizing returns.
  2. Data must have clearly defined accountability.       
    • Data are a by-product if business practice.  Therefore, data owners, data stewards, and data custodians must control classification and use of data including change management.
  3. Data must be seen as cross-functional and not siloed.     
    •  Data exists in an ecosystem of cross-functional dependencies where shared business practice knowledge is necessary for data processing and deployment.
  4. Data integrity must be defined and managed consistently across the data life-cycle.       
    • Data must have defined business use validity, reliability, and quality from data entry to data retention.
  5. Data must be managed to follow internal and external rules.       
    • Security over data compliance and access to data is required to protect data of individuals and the institution.

1 Data systems encompass 成人头条 ERP system-Ellucian Banner, non-Banner Enterprise systems and managed data systems including Business Intelligence and Predictive Modeling (BIPM), University Assessment Data Storage (UADS) and External Reporting Data (ERD).  While largely dependent upon transactional databases (e.g., Banner), managed data systems are curated data configurations designed for ETLs, data quality audits, reporting and statistical analysis, and include data customizations, aggregation, imputation, forecasting, simulations, and AI related machine learning.