Who Is Doing Our Data Laundry?

Presenter:

  • Brad Wheeler, Ph.D., IU VP for IT, & CIO; Professor of Information Systems, Kelly School of Business

The world is deluged with data, but you may be asking yourself, what should I do? If they don’t do anything to inform decisions to meet the goals we’re pursuing, what good are they? Are you trying to

  • Rapidly remediate info reporting?
  • Enable better financial decisions?
  • Accelerate student success goals?
  • Empower advisors?
  • Benchmark yourself?

The act of working on data to get what you want is a bit like doing laundry:

  1. You put in capital
  2. You put in labor
  3. You add consumables

…and from this, we expect clean, organized clothes.

By “data laundry,” I’m referring to legitimate process of transforming and repurposing abundant data into timely, insightful, and relevant info for another context. It is a mostly unseen, antecedent process that unlocks data’s value and insights for the needs of decision makers.

Our institutions are often quite data-rich and insight-poor.

Two distinct phases to doing data laundry

  1. Data cleaning
  2. Presenting data as information in a context in which it can be used

Data Cleaning

Discovering > Extracting > Re-coding > Uploading > Normalizing

Information Presentation

Enriching > Comparing > Presenting (this is the “Magic Bundle”)

Insource or Outsource

You can buy the equipment and do the work ourselves, or go to the dry cleaners. Even if you go to the dry cleaners, you still have work to do… If you go to a vendor, which is common in higher ed, you’re going to have a significant amount of work. Companies like Apple, Google and Tesla have chosen to do a lot of insourced work.

IU’s Data Laundromat

IBM did an assessment of our organization and they told us that a) we had a lot of data, b) our data was not in the most usable format and c) we were lacking in ability to perform effective analysis.

Decision Support Initiative (2015)

  1. Enable report and dashboard “discovery” via search
  2. Created a factory for Decision Support Initiatives
  3. Agile Methodology (then run, run, run!)

The initiative goal: Improve decision making at all levels of IU by dramatically enhancing the availability of timely, relevant, and accurate info to support decision makers.

It will:

  • Affect people and orgs
  • Affect Data and Technology
  • Improve decision outcomes

Will clean data lead to good decisions?

Maybe, maybe not…

Caution

From Ackhoff’s Management MISinformation Systems, Management Science, written in 1967:

  1. In many cases, decision makers suffer from an overload of irrelevant information more than a lack of relevant information for a decision.
  2. In many cases, decision makers often request vastly more info than needed to optimally make a decision.
  3. In many cases, decision makers do not have refined models of the relevant information that best predict desired outcomes.

What’s up with YOUR data laundry? (Q&A)

How importance is data governance? Boiled down:

  1. Who has input rights? This should be broad.
  2. Who has decision rights? This should be narrow.

At IU, the data belongs to the trustees. Within compliance with laws (FERPA, HIPAA, etc.) and policy, it can be made available to the appropriate folks.