Data Management Future

Data Management FutureAn ECAR Presentation-Friday Oct 18, 9:10 AM

Guy Almes, Judy Caruso, Mike Fary, Curt Hillegas

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?

Administrative Research Academic

Administrative Data What does it have in common with Research data?

Academic data?

What is unique?

Where is administrative data management going?

Define Administrative DataData 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.

Where Does It Live?Today:

Typically, on premises applications

Vendor provided

University managed (application and infrastructure)

Moving Toward:

Off premises (SaaS)

Many support models

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.)

Unique Tends to be structured

Relatively small in volume

Relies on established technologies

Institutionally managed for 50 years

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

Research Data What does it have in common with Admin data?

Academic data?

What is unique?

Where is research data management going?

Define Research DataData 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

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

In Common with Other Data Types Growing

Asset of the Institution

Policy and law govern some of it

Core infrastructure requirements

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)

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?

Academic Data What does it have in common with Admin data?

Research data?

What is unique?

Where is research data management going?

Define Academic DataData 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

Where Does It Live?Today:

On premises applications and in the cloud

Scattered – all locations are not known

Moving forward:

More of the same?

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.)

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

Where is it going? To the “Cloud” (SaaS, PaaS, IaaS) – without contracts?

Predictive Analytics

Increased attention to learning outcomes

Data Protection

Internal/External Sharing

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

Discussion What do you think about the differences and similarities

between the different data?

Where do you think this is going?

Join the ECAR Data Management WG

Contact ECARWG@educause.edu.

http://www.educause.edu/ecar/ecar-working-groups/get-i

nvolved

We’d love to have you join us!