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The Data Driven University - Automating Data Governance and Stewardship in Autonomous and Decentralized University Environments
The document discusses implementing data governance and stewardship programs at universities. It provides examples of programs at Stanford University, George Washington University, and in the Flanders region of Belgium. The key aspects covered are: - Establishing a data governance framework with roles, processes, asset definitions. and oversight council. - Implementing data stewardship activities like data quality management, metadata development, and reference data management. - Stanford's program established foundations for institutional research through data quality and context definitions. - George Washington runs a centralized program managed by the IT governance office. - The Flanders program provides research information and services across universities through consistent definitions, roles and collaborative workflows.
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The Data Driven University - Automating Data Governance and Stewardship in Autonomous and Decentralized University Environments
- 1. The Data DrivenUniversity Automating Data Governance & Stewardship in Autonomous & Decentralized Environments Pieter De Leenheer, PhD Cofounder and VP Innovation
- 2. What we talkabout when we talk about no Data Governance Who approved this? I wish these guys spoke our language I can’t understand this report ! I’ve never seen this funding code! Who introduced this ? Are we sure this definition of ‘professor’ is correct ? The Problem This rule is different on our campus! Are we allowed to share this student data with IR?
- 3. Glossary Search • Howfrequently do you look up a word for your business? • To what purpose? Clarification Differentiation • What are your main sources? • Hierarchy-based navigation or key-word based search? • Authoritative Truth or trust?
- 4. Overview • Data GovernanceOperating Framework Data Governance Data Stewardship Data Management • Implementations Stanford University Data Stewardship (SUDS) George Washington University Brigham Young University • The Bigger Picture Inter-university Data Governance in the Flanders Research Information Space
- 5. Data Governance Framework DataGovernance Council: Governance Operating Model Roles & Responsibilities Processes & Workflow Asset Types & Traceability Data Governance Organization Data Stewardship Activities Data Quality Development IT / Operational Data Management Activities Data Modeling Metadata Lineage Establishes& drives Aligns& Coordinates Reports& Escalates Monitors& Remediates Metadata Scanning Reference Data Authoring Data Integration Collibra Business Semantics Glossary (BSG) Collibra Reference Data Accelerator (RDA) Hierarchy Management Business & Data Definitions Business Traceability Semantic Modeling Mapping Specifications Policy Management Business Rules Data Quality Rules Data Quality Reporting Issue Management Reference Data Crosswalks Master Data Stewardship Data Quality Profiling DQ Defect Resolution Collibra Data Stewardship Manager (DSM) Collibra Platform Other Data Management Vendor products ... https://compass.collibra.com/display/COOK/Data+Governance+Operating+Model
- 6. Stanford University DataStewardship (SUDS) • All Materials available here dg.stanford.edu • Establish foundation for Institutional Research • Data Quality How many faculty do we have? • Context and Meaning What does faculty mean in which context? How is faculty data structured and where is it stored? • Data Usage Request Am I allowed to use faculty or student name and age for external reporting?
- 7. SUDS: Approach • Decentralized 1 DG coordinator (also show vacancy) Project staff cross-functional working groups : natural scope and resources focus on BI reporting, with input from above projects sign off by DG coordinator and end user through usage (full cycle) • Step-by step; success by success
- 8. SUDS: First Successin OBIEE reporting REST / JSON / CSV / Excel
- 9. DG Operating Model •What do we want to capture? Asset Type: Business Terms, Policies, Rules, Code Values Attribute/Relation Type: Name, Definition, Example, Derivations, Specializations • Who should be involved in this process? Communities: Finance, HR, Student, Research Domains / subject areas: Task Management Users and User groups • How to execute and Monitor the process? Key events and workflow chains Validation rules
- 10. SUDS Data DictionaryExample +4000 data elements Community context: Finance, HR, Research and Student Custom attribute types and relation types
- 11. What attribute- andrelation-types do we want to capture? Out of the box but also custom attribute types and relation types
- 12. What attribute- andrelation-types do we want to capture? • https://stanford.app.box.com/CollibraQuickReference • https://stanford.box.com/UsingCollibraFields
- 13. Who is involvedin the process? • https://compass.collibra.com/display/COOK/Role+Ty pes ResponsibleAccountable Informed Consulted
- 14. Who? User groupsand Dashboards
- 15. Who? – Usergroups and Dashboards
- 16. How to executeand monitor? From Best Practice to Auto-Validation Rules http://web.stanford.edu/dept/pres-provost/cgi-bin/dg/wordpress/?p=577 (generic example – not from SUDS)
- 17. How to executeand monitor? • Status Types and Workflows E.g., For Domains, Terms, Users, and later for Issues and Data Sharing Agreements, we first define a “finite state machine” and then a set of workflows that each define a transition between states. This means workflows can trigger each other and form a complex chain. BUSINESS SEMANTICS GLOSSARY Candidate In Progress Under Review Accepted In Revision Rejected Term requested on the domain page 11 1 2 2 3 3 2 3 Depricated 4 5 Workflows 1 2 Propose Business Term Edit Business Term 3 Onboarding Business Term 4 Deprecate Business Term 5 Reactivate Business Term
- 18. How it itto be governed? Onboarding Workflow (Not Stanford content - illustrative example only)
- 19. How it itto be governed? Approval Workflow (not Stanford content - illustrative example only)
- 20. Stanford DG ProgramKey Results (from http://web.stanford.edu/dept/pres-provost/cgi-bin/dg/wordpress/wp-content/uploads/2014/11/Stanford_DS_CAIR_v2.pdf • Understand data from multiple perspectives • Central repository of verified information (and better data infrastructure) • Easier access to information; less reliance on ‘oral tradition’ • Improved data quality, consistency • Increased understanding; thoughtful decision-making around data
- 21. SUDS Future Directions •Continue building engagement around data governance (define policy), in addition to data stewardship (enforce policy) • Continue building engagement, especially by executive-level leadership • Continue increasing visibility and consumption of definitions and other metadata
- 22. George Washington University (bycourtesy of Ron Layne, GWU) • centralized • run by the DG Office division of IT • mapping data dictionaries, rules and metrics and data sharing agreements • Integration with Informatica Data Quality
- 23. Flanders Research InformationSpace • Providing Scientific Research Information and Services • Easy • Transparent • Open • Timely • Unambiguous • Supported by Data Governance • Qualitative meta data: e.g., definition for project, funding codes, mappings, classifications, etc. • Roles and responsibilities for Information Providers and Stiweto • Collaborative workflows between Information Providers and Stiweto By courtesy of G. Van Grootel, EWI
- 24. FRIS’ Data-driven InnovationEngine By courtesy of G. Van Grootel, EWI
- 25. The Data providerslandscape 25 Universities Research Institutes Funders Others Strategic Research Centers Universitiy Colleges By courtesy of G. Van Grootel, EWI
- 26. FRIS Metamodel: anexample By courtesy of G. Van Grootel, EWI
- 27. Traceability diagram Node Description JRC(Joint Research Centre) The Business Term representing the Funding Source Zevende Kader Programma.. The Business Term representin the parent Funding Source 3723 Generation 1 Funding Code Value 258 Generation 2 Funding Code Value G3 The Funding Stream Code Value By courtesy of G. Van Grootel, EW
- 28. Conclusions • Case byCase, success by success • Identify key events and design workflow ‘chains’ to automate governance • To support your specific use case and the growing DG platform you need extend asset, relation, attribute types • Collaboration and business user friendliness • BOK http://compass.collibra.com
- 29. Questions For Audience •How much % of data user need to look up the definition of a term? • How many % wants to know where data around a term is stored. • How many business terms do you have? • Who is in charge for data quality / governance ? • How much % of data definition decisions depends on business?
Editor's Notes
- #2 Audience from various academic institutions before collibra I was a researcher and assisttant prof in 3 univ In fact collibra is a university spinoff valorising the research on data govenance, ontologies and semantics web.
- #3 We should know how universities tick. From my own experience as an employee and as vendor I think I know how university departments thrive as decentralized and autonomous entities. No data governance does not mean data quality can be managed good. It us just that globalization and increased data servicing between university entities that makes quality and truth of data relative, and we more and more have to rely on mutual trust.
- #4 Put yourself in the user of your data. An consider these questions ?
- #6 Stewardship activities doing: scattered, no unified operatnig model and no clear sight on the results of doing it.
- #24 Een goede cae om dit te illustreren is de Vlaamse departement van Economie, Wetenschap en Innovatie.
- #26 Logo’s van alle Hogescholen post fusie
- #27 Illustrates the implemented FRIS Metamodel in the DGC operational model. Allows for formal named relations between the different FRIS Business leveling model concepts