Three Keys to Improving Data-Driven Decision-Making

Supporting decisions with data seems only natural, but for many colleges and universities, data-driven decision-making seems like an ideal that is out of reach. We’ll take you through the basics and give you some tips. Since our readers are mostly interested in higher education enrollment and marketing, we’ll use examples from scenarios found in admissions data.

Data-driven decision-making relies on three main components:

  1. Data quality
  2. Data relevance
  3. Access

Data Quality

We discussed this topic in detail in a previous article that offered tips for how colleges could improve their data quality. But, basically, when we talk about quality, we’re talking about data that has been consistently and completely captured, error-free, from a high percentage of the segment in question. For example, in admissions, we’d want to know that nearly all admissions leads had provided complete contact information. Also, drop-down menus can be used where possible on admissions forms to help eliminate typing errors. The more steps you take to ensure data is complete and error-free, the more likely it is that your data will be of higher quality.

Data Relevance

Data relevance pertains to how your data ties in with your school’s goals and key performance metrics. These can be moving targets and many organizations struggle to match the data they have with what are often changing goals and priorities. Although having data within reach has become easier today than ever before, it now comes so quickly and in such bulk that decisions that used to be reviewed once a quarter or less now require almost daily attention. How does a complex institution begin to make sense of it all when resources are short? Read on for solutions. 

 

Sample - Data Dashboard

Sample – Data Dashboard

Access

A lot of schools find that data that might be helpful is not shared across departments. If each department were to create a dashboard of the data it collects and then shared that information with other departments, what insights might be found? Similarly, helpful data is often buried in spreadsheets, unprocessed. Even with advances in data analytics tools that allow for the instant capture of data in the form dashboards populated with charts and graphs, a lot of people feel they don’t have the time to optimize the programs they have in order to translate data into a more useful form.

But if you feel that you have quality, relevant and accessible data and that your school could still do more to harness its potential, read on for three additional tips.  

 Three Tips

  1. Remove barriers
  2. Ensure the data you collect supports institutional goals
  3. Monitor and discern
  1. Remove Barriers

One significant barrier to data-driven decision-making in colleges is that the most critical choices usually occur well past the admissions point — or in other words, well past the point at which the data was gathered.

Another barrier to data-driven decision-making is that working in a fast-paced environment can prevent staff from having the time to evaluate data-collection processes and practices. Most data analysts encourage decision makers to not only audit the data they have, but to ask questions about what data they wish they had. Keep a list of when those “aha” moments arise. They always do, and usually when you’re faced with a fork in the road. (Note: The decision makers are often not closely aligned with the information gatherers. They may work in different departments and never see each other.)

Then, keep those “aha” moments in mind as you evaluate progress and planning for the next round of business to see if there’s not some way to capture the information you wanted as you move forward. In some cases, it may be as simple as submitting a request for existing data to the staff who handle the information gathering. For example, a marketing decision could be informed by data that is gathered by the admissions department. Or, adding a field to an admissions inquiry form could initiate the collection of new and needed information. Chances are that cross-departmental collaboration will shed light on what data is available and how it would be best interpreted.

  1. Ensure the Data You Gather Supports Institutional Goals

Is your data aligned with your school’s goals? Let’s talk through an example of what that might look like. For instance, many schools are looking at how they might improve graduation rates.

Consider this hypothetical situation: While looking at their school’s graduation rates, staff notice that students who live on campus seem to have higher graduation rates than those who do not. In examining how the school might improve graduation rates, they realize that if only there were a way to track which recently enrolled students were not planning to live on campus, they could provide information designed to help commuter students have a better experience.

The school may not have been tracking information about this topic, but adding a checkbox question to an admissions form would be a relatively low-cost, manageable change. Alternatively, an admissions counselor could ask about it during the interview process and make note of it in a data-capturing field within a customer relationship management system. (As opposed to a section of candid notes.) The benefit is that the school would then be able to capture benchmark information, test its theory, and act on the outcome.

  1. Monitor and Discern

Merely having data in hand is not a solution. Data requires monitoring and discernment, and as you develop your data dashboards, patterns will emerge. Becoming familiar with your data’s high and low points, and then developing the ability to translate them into actionable insights, will help you seize opportunities, identify inefficiencies and gain clarity in business decisions.

Here’s another example of data-driven decision-making from Collegis Education’s data analysts. With the goal of understanding the variables that impact enrollment, we conducted a review of inquiries from prospective students (nationwide, for multiple schools). We found:

  • 24% of the inquiries that were contacted within 24 hours enrolled in the school.
  • 4% of the inquiries that were contacted after 24 hours enrolled in the school.

By understanding that enrollments drop off drastically when inquiries sit for 24 hours or longer, schools are able to review their inquiry-response process and look for ways to help staff respond more quickly.

Any school can improve its data-driven decision-making by removing the obstacles that disrupt the ability to capture high-quality, relevant data. Even small-scale changes like ensuring that you are capturing data that is aligned to your school’s goals and continuously monitoring the data you collect can make a big difference.