When everyone on campus wants more data, the solution is often to create fewer reports.
That counter-intuitive thought occurred to me recently as I helped one of our partners build a logistic regression model. Specifically, we were working on a backward elimination model, which starts with a long list of variables and pares them back until only the most significant predictors remain.
That’s a dense introduction, I know—but bear with me, because backward elimination is a perfect lens through which to view reporting at your institution. To get the best answers from your data, you often need to cut back. Eliminating unused reports and irrelevant metrics allows analysts and decision-makers to focus on the data that matters most.
A logistic regression model assesses the statistical value that each variable contributes to an outcome you’d like to predict. The final version of a regression model includes only the variables that most significantly contribute to the outcome.
In this post, I’ll discuss common reporting challenges, outline four benefits of paring back reports, and share guidance on how to achieve more efficient higher ed data reporting based on my experience working with institutions that use Rapid Insight.
Common higher ed reporting challenges
“Reporting,” for our purposes, means organizing or summarizing raw data, whether internal or external, routine or ad-hoc. Your institution likely has multiple report writers in different departments, and reports may serve a single audience or multiple at the same time (e.g., daily application counts, course section availability, or campus service utilization). Put simply, a typical institutional reporting landscape is complex, and it’s easy to see how problems can arise. Reporting issues tend to fall into three groups:
Too little reporting
- Questions left without answers
- Decisions made without access to data
- Misguided decisions made due to irrelevant metrics
- Focus too narrow to include key information
Too much reporting
- Report writers overburdened
- End users overloaded with information
You are likely dealing with more than one of these issues simultaneously. From my experience, the most common combination of reporting woes in higher ed is that there are too many reports, and many speak to irrelevant metrics.
This observation encouraged me to think about how institutions might simultaneously add efficiency, reduce bloat, and ensure that reports stay relevant. That’s when it struck me that regression modeling is an excellent lens through which to view this problem.
4 benefits of thoughtful “backwards elimination” in reporting
At this point, you may be worried about your stakeholders’ reaction to eliminating existing reports. The report you see as least relevant will be someone’s preferred report, which can create friction. However, the benefits of a streamlined reporting system most often outweigh the drawbacks, even for those who lose a report they’re used to in favor of something different (but better).
“Cleaning up” reporting has positive effects that any stakeholder can appreciate. Let’s look at four specific benefits.
1. Quality control
Routine checks on filters, numbers, and data quality become less frequent when a report is no longer seen as critically important.
As a result, when someone comes across that report, they may be left with a poor impression of the data’s accuracy. This person may then opt to use their own spreadsheets, further fragmenting your reporting landscape and creating data detractors. Suddenly, more people are doing more work and agreeing on the outcomes less often.
Proactively eliminating these legacy reports improves your overall quality control, bolstering trust in the use of data around the institution.
Increasingly, reports and dashboards are stored in shared repositories like OneDrive, Google Drive, Box, or even plain old network folders. An easy place to start is by looking at the central locations where you either build or publish your office’s reports, then review them according to publish date. Older reports are often (by nature) the most out of date, so this can be an easy win on your path to consistently high-quality reporting.
2. Greater report-writing bandwidth
Hiring new staff or increasing part-time staff hours is the easiest way to increase reporting capacity, but budget limitations prevent this from being realistic for most institutions. Removing or streamlining existing tasks is a more readily available way to free up staff time for new report-writing initiatives.
As reports age, they often fall out of use. Sometimes, a report designed to fill an immediate need gets continually maintained even though there is no longer a need for it—the updates become routine. Without an active review, minor, perhaps unnecessary report-writing often continues because “we did it last month.” Bandwidth for future reporting efforts need not come from terminating critical reports—you may be surprised what you can clear up with an intentional look at how many open threads you have.
3. Clarity for end-users
Reporting efforts tend to have a natural, predictable lifecycle. At first, there are no reports concerning a critical piece of information. Over time, new and existing reports begin to include that metric. Eventually, several reports may offer their own take on that metric, perhaps broken down by varying windows of time and sub-populations. Before long, stakeholders looking for that key metric have several places to find the answer. The unfortunate end to this lifecycle is that stakeholders get conflicting answers depending on which source they consult.
It’s not feasible to maintain an ever-expanding set of reports. Curating your report offerings ensures a consistent take on key metrics. Start with critical information (like enrollment counts, retention rates, or completions). Next, determine who uses these key metrics regularly and find out what reports they rely on (either by asking stakeholders directly or examining access logs). From there, eliminate conflicting sources of information and redirect stakeholders to a reliable report. This approach serves every party well and sets the stage for the real goal: long-term strategic progress.
4. Improved viability for data and analytics
What is the natural outcome of your regression-inspired curation of report offerings? By controlling the number of efforts you or your office maintain, you can more easily drive the quality and consistency of your reported data. Over time, this helps you (and your office) build a reputation and legacy for effective, strategic institutional support.
By ensuring you can pivot with the institution and the modern challenges it faces, you send a message that stakeholders can (and should!) come to you with their data-related strategic questions. Finally, by managing the user experience and preventing users from needing to sift through unnecessarily overlapping reports, you can continue to build momentum.
Eliminate some of your reports to improve all of your reports
No matter how much reporting you’re responsible for, it’s worth the time to do it right. A thoughtful survey of what reports your institution needs is critical, and assessing the efforts you’re maintaining ensures you’ll have the support and the resources to make that assessment.
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James works directly with organizations bringing data to bear in decision-making, building analytic capacity along the way. His work has involved hundreds of organizations-from single person departments to teams of more than ten. Leveraging a background in statistics and data analysis, James advocates for accessibility of analysis and data literacy. In order to ensure that data-informed insights are actionable, approachable presentation and communication lie at the heart of everything he does, which is how all data work should be! At EAB, James focuses on generating impactful value with partners via Edify and Rapid Insight.
In his spare time, James volunteers at his local library in Conway, New Hampshire. He also enjoys woodworking, reading, trail-running, and correspondence (letter-writing).