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Data Management Future

An ECAR Presentation- Friday Oct 18, 9:10 AM Guy Almes , Judy Caruso, Mike Fary, Curt Hillegas. Data Management Future. Agenda. Overview ECAR DM group and Data Management Administrative, Research and Academic Data – commonalities, uniqueness, future Data visions

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Data Management Future

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  1. An ECAR Presentation- Friday Oct 18, 9:10 AM Guy Almes, Judy Caruso, Mike Fary, Curt Hillegas Data Management Future

  2. Agenda • Overview ECAR DM group and Data Management • Administrative, Research and Academic Data – commonalities, uniqueness, future • Data visions • Discussion - What do you think about the differences, similarities between the different data and where is this going?

  3. Administrative Research Academic

  4. Administrative Data • What does it have in common with Research data? Academic data? • What is unique? • Where is administrative data management going?

  5. Define Administrative Data Data which is used in the day to day running of the business activities of the institution. Examples of administrative data include student course grades, employee salary information, vendor payments, and facilities work orders.

  6. Where Does It Live? Today: Typically, on premises applications Vendor provided University managed (application and infrastructure) Moving Toward: Off premises (SaaS) Many support models

  7. In Common with Other Data Types • Asset of the Institution • Policy, and in some cases law, govern it • Demand for access to it is growing • Core infrastructure (network, IdM, etc.)

  8. Unique • Tends to be structured • Relatively small in volume • Relies on established technologies • Institutionally managed for 50 years

  9. Where is it going? • To the “Cloud” (SaaS, PaaS, IaaS) • Traditional data models (ER) vs.Non-traditional (NoSQL) • Data Services • Predictive Analytics • Data Protection • Public Data Sources • Internal/External Sharing

  10. Research Data • What does it have in common with Admin data? Academic data? • What is unique? • Where is research data management going?

  11. Define Research Data Data that is used by or is a product of research. Examples include: Output from experimental devices Masses of data collected from the internet Results of computation modeling and simulation Results of data analysis Data about the data

  12. Where Does It Live? Today On campus In the cloud At research “centers” Moving toward As the size of data sets grow, research data will need to live near where it will be used Significant portions of data will live distributed in the cloud, and “Big Data” techniques will need to be used to collect and analyze

  13. In Common with Other Data Types • Growing • Asset of the Institution • Policy and law govern some of it • Core infrastructure requirements

  14. Unique • Can be very large (TB, PB, and beyond) • Not necessarily centrally controlled • May require very high performance access • Increasing need to share (both inside and outside the institution)

  15. Where is it going? • Humanities • Social Sciences • Cloud • Research “centers” • Through bigger pipes • Will the data come to the computing or will the computing come to the data?

  16. Academic Data • What does it have in common with Admin data? Research data? • What is unique? • Where is research data management going?

  17. Define Academic Data Data that is associated with teaching and learning. This includes objects and processes. Examples of academic data include course catalog, learning objects, academic program information, learning outcomes, learning analytics, teaching methods

  18. Where Does It Live? Today: On premises applications and in the cloud Scattered – all locations are not known Moving forward: More of the same?

  19. In Common with Other Data Types • Asset of the Institution • Policy, and in some cases law, govern it • Demand for access to it is growing • Core infrastructure (network, IdM, etc.)

  20. Unique • Individual faculty have control over where the data is • What data the institution has is not always known • Course catalog and Program information is critical to Accreditation • Increasing demand for analytics

  21. Where is it going? • To the “Cloud” (SaaS, PaaS, IaaS) – without contracts? • Predictive Analytics • Increased attention to learning outcomes • Data Protection • Internal/External Sharing

  22. Visionary – Technology Trends/Drivers • Rapid exponential growth in capacity over time • Mostly a good thing, but note complex societal implications • Role of the network: • Enables sharing of (even) large data, especially research • Campus-to-campus and/or campus-to-cloud • Sharing raises hard IdM and related federated authentication challenges • Hard technical / infrastructure / policy challenges

  23. Discussion • What do you think about the differences and similarities between the different data? • Where do you think this is going?

  24. Join the ECAR Data Management WG • Contact ECARWG@educause.edu. • http://www.educause.edu/ecar/ecar-working-groups/get-involved We’d love to have you join us!

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