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UM Data Deep Dive

UM Data Deep Dive. IUG Presentation June 2011. UM Background. Multiple data sources Datacom mainframe Locally developed applications Optix documents Sheets of paper Minimal documentation Dispersed knowledge among functional users Single point of technical knowledge

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UM Data Deep Dive

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  1. UM Data Deep Dive IUG Presentation June 2011

  2. UM Background • Multiple data sources • Datacom mainframe • Locally developed applications • Optix documents • Sheets of paper • Minimal documentation • Dispersed knowledge among functional users • Single point of technical knowledge • No single unit responsible for ETL

  3. UM Priorities • Document what we currently have • Identify “dirty” data • Clarify areas where multiple concepts are interwoven in a single data point • Understand where current data elements fit within Kuali Student • Identify gaps in KS • Identify areas for manual data entry • Have it done yesterday

  4. UM’s Strategy • Weekly sessions with homework • Difficult to get momentum, maintain focus • Homework doesn’t get done • No distinction between what is needed now and later • “Deep Dive” Approach • Day-long workshop - Food • Focus on a single source/table • Prioritize activity • Assign jobs including notetaker • Finish with “Data Mapping Smackdown”

  5. Guiding Questions • Is this the primary source of this information? • Is this the ONLY source of the information? • What is the “shelf life” of this information – how long is it useful? • Who “owns” the information – i.e., who can make changes to it? • Why do we need this information – what decisions does it support, questions does it answer, etc.?

  6. Sample Agenda • Introduction (5 minutes) and Assignments • Identify Important Data Elements • What elements specifically relate to the Curriculum Management functionality? • Cursory discussion leading to initial list of elements • Build out data documentation for each element. • Create an action list for follow up.

  7. Work Products • Data sources brainstorming spreadsheet • Combined sources documentation and mapping sheet

  8. Results • Most curricular data sources are documented • Found additional sources along the way • Changed priorities • Identified pain points – shaped implementation strategy • Focused mapping on initial implementation – course proposal process • Identified areas for manual data entry • Course & program rules • ECON learning objectives & categories

  9. Next Steps • Identify ETL resources • Make decisions around pain points • Old proposals – how best to deal with Optix • Special topics courses • “Curricular” decisions outside of course/program approval • Additional CLU types not currently exposed – exam, project, experiental learning

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