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Technology

My EDUCAUSE 2013 Mega Post

One the things I try to do when I attend conferences is to make a detailed record of all the sessions I attend, with the exception of keynotes, which tend to get really good coverage from other folks.  I live blog the events as I attend them, which hopefully helps those who committed to other sessions, and then I do one of these “mega posts,” which summarize all the posts I attended.  Based on my itinerary, 2013 seems to be the year of big data and analytics.  I’m willing to bet a lot of my fellow attendees will agree 🙂

I’ve been in higher education for just over seven years now, and somewhat amazingly, this was the very first EDUCAUSE event I’ve ever attended.  Why didn’t anyone tell me about this conference?  It was an extremely worthwhile event, at least for me…one of the meetings I had will likely save my division close to $50,000 each year!  That savings will go a long way toward providing students at CSUN with more and/or better services.  There were lots of great sessions to attend, with lots of smart folks sharing what they’re doing with IT on their campuses.  I’ll definitely be back next year.

Without any further ado, here’s my EDUCAUSE 2013 mega-post…please drop me a line and let me know if this helps you!

 

Friday, October 18 (last day of EDUCAUSE was a half day)

 

Thursday, October 17 (my busiest day)

 

Wednesday, October 16 (spent a few hours prowling the vendor floor and visiting with my accessibility colleagues)

 

Tuesday, October 15 (each session was a half-day long)

 

Categories
Technology

Creating a Data Governance Program

Title:  Creating a Data Governance Program

Presenter:  Mike Chapple, University of Notre Dame

 

This presentation was one of those EDUCAUSE decided to webcast.  Primarily focused on events of last year, but will cover some things done over the last 5 – 10 years.

It All Starts with a Story…

One day, the President was wondering…how many students do we have?

Naturally, a lot of potential answers depending on who you ask.

SLIDE:  how Notre Dame views data governance, using a building to illustrate

Access to Data (Roof)

  • Quality & Consistency (current focus)
  • Policies & Standards (current focus)
  • Security & Privacy
  • Compliance
  • Retention & Archiving

Technology (Foundation

 

Data Driven Decision Making (D3M) = Business Intelligence (as it’s known everywhere else)

  • Definitions need to be agreed upon (i.e. – what is a student)

 

SLIDE:  Governance Model

  • Executive Sponsors (EVP, CIO)
  • Campus Data Steward
  • Unit Data Stewards
  • Coordinating Committees (Info Governance Committee, D3M Steering Committee)

 

SLIDE:  Domain Objectives

  • Data Steward(s) appointment
  • Data definitions and business rules
  • Data quality guidelines and monitoring process
  • Regulatory compliance plan (if applicable)

 

SLIDE:  Building Data Dictionary

  • Term, i.e. “Active Student”
  • Definition:  PLAIN ENGLISH DEFINITION
  • Source System, i.e. Banner
  • Source Detail, i.e. SQL query which explains gory details of how you get the data

 

SLIDE:  Data Definition Components

  • Definition
  • Source System / Detail
  • Possible Values
  • Data Steward
  • Data Availability
  • Classification

 

SLIDE:  Start with Executive Support

This is pretty much an admonition; it really helps.  At Notre Dame, responsibility for this campus function landed with IT.

 

SLIDE:  Identify and Involve Stakeholders

Each item to be defined takes a meeting…it’s very time-consuming because you need to have representation from each area.  Data is owned by the university, not specific departments!

Notre Dame uses a “RACI” matrix for each defined term

R – responsible (office)

A – accountable (who keeps the group on-track)

C – consult (you have a seat at the table)

I – inform (people who need to know)

The matrix is distributed to all stakeholders so they can fill it in with their preferences.

 

SLIDE:  Reconcile Differences Visually

ND had two competing student numbers:  “Registrar Count” and “IR Count”

IR count = Externally reportable enrolled student

“Registrar Students” includes some folks like students on leave, zero credit students, etc.

Use a stacked bar, starting with externally reportable enrolled students, adding registrar student populations on top of that.

 

SLIDE:  Give the group a Starting Point

  • Start with a draft
  • Counting matters!  Definitions help address this possible problem.
  • Don’t use Jargon!

 

Security and Privacy

Risk-based security program

  1. Highly Sensitive (SSNs, CCs, Driver’s Licenses, Bank Accounts, ePHI)
  2. Sensitive (Everything else)
  3. Internal Information (info that would cause minimal damage is disclosed)

 

Compliance

We have to be responsive to new legal realities, since our campuses are like small cities and any law passed probably affects some area on our campus.

All data must be auditable.

  • 75% of orgs have at least one person dedicated to IT compliance
  • 76% of orgs have a corporate executive-level compliance office or council

Build compliance plans

  • Document everything with respect to regulations, i.e. HIPAA
  • Everything should be substantiated

 

Questions

With so many stakeholders, how did you address and resolve differences in data definitions?  We didn’t really have very many of those disagreements, because each area was represented in each set of meetings, and there was a solid bond among the reps from each area.

What do you do with data NOT in the data warehouse?  You just have to find some way to “chunk the work out.”  The output of the program must be pristine, so naturally priorities must be set.

Did ND work with IU, since most of this is the same?  No.

What tools are you using to manage metadata?  Google Docs for now, great for getting started, but it’s not conducive to long-term maintenance.  We’re actually building our own graph database.  This tool will ultimately expose this data for other tools.

Any principle for prioritization?   Steering committees prioritize based on BI needs of the institution.

Is there an ongoing need for a campus data steward versus a department data steward?  In some areas, the data is general or applies to many different populations.  Campus steward plays an important coordination role.

Do you consider your work the beginning of a master data management program?  Yes!

Do you see shadow systems as being a problem?  We’re not really far enough along to have experienced this problem yet.  Data is not widely available yet.  We refer to this phase “taking it from the team to the enterprise.”

This is for administrative data, yes?  Yes, it does NOT include research data.

Categories
Technology

Ethics and Analytics: Limits of Knowledge and a Horizon of Opportunity

Title:  Ethics and Analytics –  Limits of Knowledge and a Horizon of Opportunity

Presenters:

  • James E. Willis III, Ph.D., Purdue University
  • Matthew D. Pistilli, Ph.D., Purdue University

 

(See my related post, “The Edward Snowden Affair: A Teachable Moment for Student Affairs and Higher Ed“)

 

Highly interactive session, sat in groups of five and discussed among ourselves.

SLIDE:  Lack of Discussion of Ethics in Data and Technological Innovation

Why?

  • Cutting-edge technologies are being developed every dy
  • Cost/profit margins are determined with speed of development
  • Typeicall education lines are split between science adn humanities/technolgoy and ethics
  • Difference between what can be done and what should be done

 

SLIDE:  Where to Go from Here?

  • Begin the discussion now
  • Have the difficult converations
  • Bring together the stakeholders:  data scientists, engineers, managers, ethicists
  • Establish a framework to adapt quickly to questions of whether or not an action or research agenda should occur

 

SLIDE:  Ethical Discussions = Innovation

Logic of negation

  • Why shouldn’t we do this?
  • What should we do instead?

Different paths emerge from divergent conversations

  • Stakeholders have different voices and understandings of potential paths of development

 

SLIDE:  Potter’s Box

Definition

  • ID values
  • Appealing to ethical principles
  • Choosing Loyalties
  • Responsibilities and recommendations

How Potter’s Box is useful for ongoing discussions

If you know a student is at risk, what do you do?  What is your responsibility?

 

SLIDE:  Research Questions

  • What do the ethical models look like?
  • How are these models deployed rapidly – at the speed of technology?
  • How are these models refined with time?

 

SLIDE:  Group Discussion 1

What are the current projects going on in learning analytics today?  What are the potential ethical pitfalls that surround these developments?  Why are they potentially harmful?  Are these things always wrong or are they contextually wrong?

Some of the ideas generated by the room:

  • Type 1 / Type 2 error:  will the student pass this class?  What’s my prediction – will the student pass or fail the class?  How accurate is your prediction – did your model work?
  • Is it right to get information from the students?  Where does the locus of control lie?  Does the institution need to take more responsibility for this?
  • University One (FYE equivalent in Canada) – informal collection and sharing with the next year.  Are we branding / labeling students appropriately?  Are we sharing this information appropriately?  Who should know what and at what point?  Will that affect their future studies?
  • Top-down approach using a product with funding from the Gates Foundation (similar to the InBloom project).  Goal is to make a product that analyzes what students should be taking.  Pitfalls:  don’t know model, don’t know the raw data.

 

SLIDE:  Group Discussion 2

What is the role of “knowing” a predictive analytics – once something is known, what are the ethical ramifications of action or inaction?  What are the roles of student autonomy, information confidentiality, and predictive modeling in terms of ethical development of new systems / software / analytics?

  • How much do we really know?  If we have a high level of confidence, what is our responsibility?
  • Could it be solved by giving students opt-in versus opt-out?
  • Discovering things, going down certain paths that may raise ethical questions about students who may not succeed…i.e. financial burdens that may be incurred due to failure.

 

SLIDE:  Group Discussion 3

How might we affect the development of future analytics systems by having ethical discussions?  What are possible inventions that could come from these discussions?

  • See more active participation from the students, with a better understanding of how their data is going to be used.
  • Obligation to allow students to fail; channeling people into things they can be successful with.
  • EU regs:  disclosure, portability, forget
  • Portability:  health care analog; your info can go from one hospital to another, and that data has real-world benefits to patients.
  • Information can also say a lot about faculty too.

 

SLIDE:  Group Discussion 4

What are the frontiers of ethical discussions and technology?  Are there new frameworks?  How can we affect the future by today’s discussions?

  • Maybe you can redefine what student success is?  Maybe it isn’t graduation rates…
  • How do we affect the future?  It’s all of our roles to be a part of this conversation!  You don’t really have the option to say “I don’t have the time for this”

 

 

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