I am in Indianapolis attending the EDUCAUSE Annual conference. You can see my tweets from the conference. We had an early start at 8 AM to listen to the first keynote by Daniel Pink titled “The Cascade Effect: How Small Wins Can Transform Your Organization”. It was interesting and there were a few good take home lessons. The talk was mostly about motivating the staff in your organization. He referred to a collection of social science research in his talk the conclusions from which can be summarized as “If the work involves even rudimentary cognitive tasks, then better rewards by themselves don’t improve performance”.
Some of the recipes provided were useful, such as constant feedback, providing autonomy etc. One thing he mentioned was to give the staff an hour a week for them to explore new things. As you see in one of my tweets, I am very happy to say that we already have this in place in LTS at Wellesley where we encourage the staff to take 2 hours a week to explore new areas. He stressed the importance of weekly meetings where feedback is provided, but change every fourth such meeting to talk about something totally different, such as career ambitions etc.
I also attended a couple of other talks, one on digital scholarship and another abut “what’s next in higher education”, both were a bit disappointing. However, during a dinner last night I was talking to a few colleagues from other institutions and what caught my attention was how many times the “What if?” question came up.
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“Data governance (DG) refers to the overall management of the availability, usability, integrity, and security of the data employed in an enterprise. A sound data governance program includes a governing body or council, a defined set of procedures, and a plan to execute those procedures.” This operational definition, by Margaret Rouse, captures what data governance is all about. I think it is fair to say that in most of Higher Ed, access to data is far less than optimal. The reasons for this are varied.
In “Choosing Governance Models” , Gwen Thomas outlines some of the important things to consider when it comes to data governance.
- Top-down
- Bottom-up
- Center-out, or
- Silo-in
This will provide a window into why access to reliable data in a secure way is a huge issue in higher ed. First and foremost, until recently we have not had any formal governance structures. In many cases, it has been bottom up, decisions were taken in data silos and no one was willing to disturb the status quo. These have resulted in incoherent policies, over control of data and in some cases misinterpretation of the laws governing data. In general there are a lot of users in the institution complaining about problems and not many who are interested in participating in finding a solution.
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Earlier this year, EDUCAUSE published Top 10 issues facing technology organizations. In an opinion piece, Steven Bell, Associate University Librarian at Temple University, posted a similar list for the Libraries “Top 10 Academic Library Issues for 2015 | From the Bell Tower“. These lists provide us a way to look at what we do and gauge how we are doing. Sometimes, it is pretty heartening to see that we are not the only ones facing these issues and other times you are glad that these are no longer “issues” for you.
It is not my intent to go over them one at a time, rather just highlight some of them.
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Data integration is a fact of life for those of us who are in the information technology business. Since we are yet to invent a single system that does everything for everyone in an enterprise, it is inevitable that we have to support and deal with multiple systems. It is equally true that unless the data from these diverse systems are integrated, we will not be able to understand the data in a coherent fashion. With the proliferation of “best of breed” solutions we have a complicated mess in hand.
Most institutions have a large administrative system like Banner or PeopleSoft that is considered to hold the authoritative data. In addition, for the purpose of reporting, we all have a data warehouse or a data mart into which data from the central systems is inserted typically on a nightly basis. The general premise of this is that the administrative systems were originally designed to take in the transactional data and therefore optimized for that purpose. They were not designed for complex reporting. Combining both of these in one system means a drain in resources and everyone suffers. Modern systems like Workday claims to have designed their system in ways that you can do all activities in one system. When you have multiple systems, generally, the data is integrated with the administrative systems, though in some cases, directly into the data warehouse.
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