Viewpoint: Having bad data often isn’t a ‘technology problem’

I have worked with many different schools on reporting and institutional research projects. The common question I am asked from Information Technology (IT) and Institutional Research (IR) offices is, “What tool should we be using to pull data from our system?”

People continue to struggle to get the information they need out of their institution’s administrative systems. In their minds, the problem—and answer—always seems to be the technology.

I have seen schools that have been successful with a variety of reporting tools, ranging from Microsoft Excel to the most advanced business intelligence (BI) and statistical analysis tools. I have also seen many schools fail with the exact same tools.

My conclusion is that institutional reporting is not a technology problem. The key to success is communication, collaboration, knowledge of the data, and trust between all users—all of which are categories of best practices in data management.

“Data management” is a broad term and often includes each of the following:

1. Data definitions: Do we agree on what is meant when we talk about our data?
2. Data governance: Who owns and manages the data in different areas on campus?
3. Data knowledge: How do we find the data we need?
4. Data quality: Are the data “good”? Are they accurate?
5. Data access: Who is allowed to see or change the data?
6. Data integrations: How do we manage data across disparate systems?

These are all important concepts, but when extracting data from administrative systems, the most important concepts are data definitions, governance, knowledge, and quality.

Data definitions: Have a conversation

For anyone outside of a college or university, it’s hard to understand the complexity of higher-education data. How is it possible to argue over the definition of a “first-time freshman?”

To understand what this term means, specifics such as minimum registered credits, add/drop periods, transfer credits, non-degree seeking students, registration status, census dates, academic levels, and dual degree students need to be considered.

Even individuals familiar with higher-education data can lose sight of these intricacies. In this environment, remember a simple fact: Computers are not as smart as their users; they don’t understand the assumptions and details that go into a data request.

It is important that users have a conversation about the data in order to avoid assumptions and uncover underlying details. When IR, IT, functional offices, faculty, and administration talk about data, everyone must understand and communicate all of the details for the data definitions.

Campuses should have conversations about data definitions every day and will need a place to capture this information. These definitions must be saved in a single, transparent location where all parties can see the results of these conversations.

Many schools have started initiatives for creating data dictionaries.