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

3. 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?
Judy

4. Administrative
Research
Academic

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

6. 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.

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

8. 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.)

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

10. 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

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

12. 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

13. 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

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

15. 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)

16. 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?

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

18. 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

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

20. 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.)

21. 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

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

23. 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

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

25. Join the ECAR Data Management WG
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We’d love to have you join us!