Data Nirvana

..My time in the past few weeks have been consumed by supporting continuing activities related to transition to Workday, Strategic Planning meetings and most recently the emergency planning activities related to COVID19. I am not complaining, but trying to explain why I have not written in a while…

Pretty much everything we do these days is data heavy and data driven. Though there is a huge explosion in the collection and availability of data, there is also a lot of misunderstanding and confusion about the data and how best to use it. There are so many external forces that come into play, including cultural sensitivities and politics, which add to the complexities.

We are far from reaching nirvana (one of the definitions for it is “a state of perfect happiness; an ideal or idyllic place”) when it comes to data. However, that is not stopping us from attempting 🙂

When it comes to data and its use, typical conversations go like this. Someone who is a huge advocate for the use of data starts the conversation on how the data can be used to transform what we do; there are so many viewpoints aired and mostly from those who do not have a full grasp of the data itself; most of the times the concerns outweigh the support and it is dead. The other way it manifests itself is someone higher up hears how other institutions are using the data efficiently so why can’t we do it; those who understand the data inform them that there are major issues with the data, but the advocates still want to proceed; project hits so many roadblocks because of lack of alignment between the advocates and the reality of the state of data and this is also dead.

Trying to define the goals early, being realistic and developing a governance structure for the use of data is critically important because no single entity can foresee all of its implications. Wheres deliberations and consultations are extremely important, but, they can also be a deterrent for progress. Finding the right balance is likely to benefit all parties. Some institutions have taken the bold step to just jump in.

This piece titled “Under a Watchful Eye – Colleges are using big data to track students in an effort to boost graduation rates, but it comes at a cost” is about the use of predictive analytics to improve the graduation rates. The article sheds light on the issues so I won’t go into the details. “Boosting graduation rates” has definite benefits for the institution and one never knows whether it is the interests of the institution or the student that is the driver for this. And how would one ever know? These are in some sense experiments on students.

I am a big fan of Freeman A. Hrabowski III, president of University of Maryland Baltimore Campus for his thoughtful leadership in the use of data to improve student learning and outcomes. In “Assessment and Analytics in Institutional Transformation”  he outlines his thoughts

“… how can information technology help? First, IT leaders can align their organizations around an understanding of the institution’s strategic goals and transformational initiatives, including how information technology can help change institutional culture and achieve campus priorities. One important way this is achieved is through the effective use of technology to help build the campus culture for evidence-based decision-making and management. It is impossible for any leader to understand, through personal experience or instincts alone, the challenges encountered by the thousands of students on a campus. Institutions thus need rigorous data modeling and analysis to reveal the obstacles to student success and to evaluate any attempts at intervention. They need to integrate data from a variety of systems—student information, learning management, and alumni systems, as well as systems managing experiences outside the classroom.”

When the president sets a stage with clear set of goals, it becomes easier to achieve some tangible results.

I am very proud of the balance we have struck at Wellesley. We have a very strong analytics tool that we call WANDA which is the repository of most of the student data. We began the process of creating this soon after I began, in response to the call for a better data repository and access to data. I told the senior leaders that frankly, this is not just about technology, but in large part about data governance. I am a strong believer in this and I articulated this in an article I wrote titled “Doing Academic Analytics Right: Intelligent Answers to Simple Questions” in 2011. I suggested the title “Do we know how many students we have: Intelligent answers to simple questions” because depending on who you ask that question on campus, you will get different answers. But the editor changed the title!

We were fortunate to have a very collaborative group that constituted the first data governance committee. We agreed to institutional definitions that everyone would adopt for key student data. It was not easy, believe me. It took us two years to roll out a usable version though we had it ready earlier. Concerns were raised that ranged from equity arguments “some of the users are not trained in this type of technology or even the interpretation of data correctly” to “who should have access to what data”. We managed to come up with a way to answer all these questions – for example, training was made mandatory before anyone can use the system and the committee helped create a high level data access matrix.

Then the use started to climb, not because it was a great technology. It was because, we created a position to advocate for the use of it. We also built a very strong relationship with the Institutional Research staff. The governance committee has grown in size and now not just student data, but all institutional data fall in its purview. This entire team consisting of LTS technical staff, Institutional Research and Data Governance Committee are all collectively responsible for the success of WANDA.

Whereas we are all far from achieving nirvana, assuming that is even possible, we at Wellesley are in a much better place. The data is made available for the right people to ask the right questions and make decisions based on data. Faculty are engaging in interesting research to help the institution understand trends and patterns. We are not dwelling into predictive analytics in any formal way, but that may be next, but we really need to define the questions and problems we want to solve first.


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