IT Forum Perspectives

Three surprising early findings from our Data Strategy Survey

By Taylor Holubar

How have members' data strategies changed in the last three years? What do cutting-edge data strategies look like? Those were some of the questions we hoped to answer by re-launching a 2014 survey of members (you can find our report on that survey here).

 Our 2017 Data Strategy Survey is now open and you're invited to participate! As we wait for your responses to roll in, we wanted to share some of the early findings we're observing in the first handful of responses.

Leaders increasingly recognize that data analytics is an enterprise initiative. Take advantage!

When we surveyed members in 2014, 40% of respondents said their institutional leadership saw data as a technical project, rather than a university-wide undertaking. In 2017, every respondent so far has said their leadership understands data as an enterprise initiative. This change is exciting because that enterprise perspective is associated with better outcomes, like cleaner data and wider acceptance of data governance efforts.

We recommend CIOs and their teams begin conversations about data governance and analytics at the top of the institution, and we're happy to help facilitate those conversations. That said, don't let a lack of presidential involvement stop you from working with willing partners. Localized successes in creating clean data or effective decision support teams can serve as a valuable proof of concept, helping you make the case for broader initiatives.

Members zero in on students with their data strategies. Just don't forget faculty and staff.

Similar to 2014, enrollment management and student success were the top areas tapped as a current focus of members' analytic efforts; likewise, every respondent plans to invest in learning analytics. It's no surprise respondents report that initiatives supported by the provost have better outcomes than initiatives with a different (or no) cabinet-level sponsor.

Perhaps more surprising is the lack of attention to faculty performance and productivity, two areas where no respondents have yet reported current analytical efforts. There's a tight link between faculty workloads and student success—for example, fluctuations in faculty teaching loads contribute to course bottlenecks, which can add semesters to students' graduation timeline. To be sure, any initiative that can be seen as impinging on academic freedom is likely a non-starter, but we encourage members to explore data-driven opportunities to better support deans in deploying their faculty to the most effective use for students.

Leaders expect to hire new employees to support their strategy. Keep these considerations in mind.

All our early respondents in this year's cohort anticipate hiring new staff to build out their data strategy. This may seem like a straightforward proposition, but here are three things to keep in mind if you're considering expanding your analytics staff.

  1. Consider redeploying or retraining your existing staff before hiring outsiders. Institutional knowledge is invaluable for an analyst, who needs to know what data lives where and how it can be used. Rather than letting staff with older technical skills languish, offer them the opportunity to move into the rapidly expanding field of data. It's an area where analytical problem solving skills can serve them well. But…

  2. Make sure analysts can do more than just retrieve data. Data retrieval and manipulation is a foundational component of any analytics enterprise, but it isn't enough in its own right. The goal is to be able to use data to answer questions and that means being able to identify the right questions to ask and distilling numbers into a "human-readable" story. IT Forum members have found that cross-school training programs can be enormously valuable, both as an academic offering and as a venue for up-skilling staff. At one institution, computer scientists and business faculty have partnered to create a multifaceted data analytics training program.

  3. Remember the first finding in this blog post: analytics needs to be an enterprise initiative. That means being thoughtful about how analysts are connected to each other. Members report that a combination of unit-level and centralized analysts provides an effective balance of local knowledge and institutional strategic perspective. Check out section three of our publication From Data To Decisions to learn about "Upskilling Distributed Analytics Staff."

As we continue to collect responses to our Data Strategy Survey (including, we hope, your institution's), we'll look forward to continuing to share our insights with you.


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