You test your software, why is data different?

Picture a software testing failure at your company. The consequences range from money lost on weeks of rework to life-threatening catastrophe. These are the errors that keep engineers, their directors, and executives up at night.

At some point, those risks came to life in the real world and pushed software teams to develop rigorous testing methodologies.

But for some reason, we don’t think about data the same way. We think of it as reliable, durable, and highly unlikely to cause a big issue. One plus one always equals two. There’s a dissociation that keeps leaders from investing in data testing.

Bad data costs millions in fines and reputational damage. At organizations where data employees say data literacy is a problem, more than a quarter estimate their company loses at more than $5 million per year, according to a 2023 Forrester report.

It’s easy to think of data as the numbers marketing uses to justify an event. But your company relies on its data the same way it relies on its software. So why don’t we test both like it?

Your business runs on both software and data

At a lot of organizations, software teams are built like the company relies on them, with CoE teams, automation tools, and on-call engineers. Data errors get treated as less urgent — but they’re not less important to the functions your business depends on most.

Operational intelligence: Broken software breaks operations — that’s why we test it. But data informs the choices that get built into software. Whether to build a new feature or change the UI are tasks assigned to software teams, but they’re usually data-driven calls first.

Innovation speed: Fast-moving companies invest in reliable software so their tools don’t become a bottleneck. But data is what those tools run on. When the data is wrong, business leaders wait for an investigation and a fix before any decision can move forward. If your competitor isn’t waiting, you’re behind.

Risk severity: Every department at a large organization carries risk, but arguably none more than data and software teams. Mistakes are expensive and occasionally make headlines. An airline’s website outage makes news the same way a bank’s multi-million-dollar fat finger error does.

Software and data are both the business. Software is the “how.” Data is the “what.”

These functions share fundamental testing needs

A sound testing methodology looks the same for software and data: end-to-end, continuous, accessible to business users, and tied to business outcomes. For data, that means the same foundation software teams already rely on: model-based test cases that are reusable and easy to maintain, testing prioritized by where the risk is highest, and tooling that doesn’t require a specialist to run it.

The solution you’re invested in for software testing must be equipped with intelligence and vision that supports the expanding needs of teams in the age of agentic AI. For software teams, that means more code shipped faster without adding headcount. The same pressure is landing on data teams.

Seventy-four percent of companies intend to adopt agentic AI within two years, according to Deloitte’s 2026 State of AI report. That means business users will likely start querying data directly via an LLM — if they haven’t already — and making decisions from whatever it serves them. Is your data ready for that? The insights those LLMs find and relay across marketing, HR, sales, customer service, finance, and compliance could be what accelerates an effort or kills it.

When a human analyst works with bad data, they catch errors and dig deeper. When an AI system does, there’s no guarantee it catches anything. It may just scale the error confidently.

Many organizations haven’t tested their data as rigorously as their software because they haven’t had to. Show-stopping data errors, though memorable, are few and far between.

There’s never been a better time to change that.

Explore Tricentis Data Integrity