Why Definitions Matter More Than Technology

Tuesday, July 28, 2026

by Dr. Jennifer Weber

Picture a meeting where a group of leaders are reviewing an organizational dashboard. One person is feeling pretty good because the numbers look positive. Another is concerned because they see warning signs in the exact same data. A third is questioning whether the numbers are even correct.

The dashboard is displaying exactly what it was designed to display. The calculations are correct. Everything is updated and working as intended.

The problem is that each person in the room is working from a different definition of the data.

To make matters worse, no one realized they were using different definitions until the dashboard was already built. The analytics team created the report based on their understanding of the metrics, while everyone else brought their own assumptions to the meeting.

This scenario plays out in organizations every day. When reports do not match expectations, the first reaction is often to question the technology (or the work of the analytics team).

In reality, the issue usually has very little to do with either.

The real problem is often much more fundamental: a lack of shared definitions.

One of the interesting things about data is that everyone thinks they understand it until they start talking about it. Terms that seem perfectly straightforward on the surface can become surprisingly complicated once people begin discussing how they should be defined.

Take common measures such as enrollment, retention, graduation rate, employee headcount, or resident student. At first glance, these seem like simple concepts. Then the questions start.

Which students count as enrolled? On what date? Do we include dual credit students? What about graduate students? Are we talking about headcount or full time equivalencies?

Suddenly a term that seemed obvious five minutes ago requires a whiteboard, three meetings, and perhaps a strong cup of coffee.

The challenge is that none of the assumptions are necessarily wrong. Different definitions often serve different business purposes. Problems arise when those definitions are used interchangeably without anyone realizing it. These situations are frequently described as data quality issues. In many cases, they are actually definition issues.

The results are predictable. Reports do not match. Meetings become focused on debating numbers instead of making decisions. Staff spend time reconciling discrepancies. Leaders lose confidence in the information they receive. Analytics teams find themselves repeatedly explaining why two reports show different results.

Meanwhile, organizations continue investing in technology. New reporting platforms, visualization tools, cloud solutions, and artificial intelligence capabilities promise faster insights and better decision making. Those investments can absolutely add value.

But technology cannot solve a problem that begins long before data reaches a dashboard.

Technology can process information at incredible speed. It can aggregate millions of records, create sophisticated visualizations, and identify patterns that humans might miss. What it cannot do is decide what a term should mean.

That responsibility belongs to people.

This is where governance becomes important. Governance committees bring together stakeholders from across the organization to discuss definitions, resolve differences, and establish official measures. While governance sometimes gets a reputation for creating extra meetings, its purpose is actually quite practical. It creates a shared understanding so that everyone is speaking the same language.

Data dictionaries play an equally important role. They document definitions, calculations, business rules, and data sources. More importantly, they preserve organizational knowledge so that definitions remain consistent even as staff, systems, and reporting needs evolve.

When organizations invest in shared definitions and governance, the benefits extend far beyond reporting. Trust in data increases. Conversations become more productive. Decisions happen faster. Analysts spend less time defending numbers and more time helping people understand what the numbers mean.

Organizations often assume their biggest analytics challenge is selecting the right technology.

More often, the challenge is agreeing on what the data actually means.

Dr. Jennifer Weber is the Director of Institutional Research and Chief Data Analyst for the North Dakota University system.  Her primary functions are to oversee the department and provide system level enrollment reporting to the State Board of Higher Education.  Jennifer also manages system-wide IR Shared Services, works closely with the State Longitudinal Data System (SLDS) and serves as the state coordinator for federal reporting.   As the NDUS-IR is also contracted through the North Dakota Department of Public Instruction (NDDPI) for data analysis and reporting, the NDUS-IR department is ultimately responsible for the data of all students attending public institutions in the state of North Dakota, pre-kindergarten through graduate school.