Twenty Years of Test Data
- a Missed Business Opportunity

Test Data: The Most Underused Data Asset

Test data sits at the intersection of quality, cost, and scale in electronics manufacturing. Yet for many organizations, it remains fragmented, inconsistent, and difficult to use. The challenge is no longer collecting more data but unlocking the value that has been there all along.

At the start of 2026, it feels like the right moment to pause and ask a simple business question: why is one of the most valuable data sources in electronics manufacturing still so underused?

More than 20 years ago, when we started working with manufacturing test data, the setup was simple. Test stations were local. Data lived on individual machines. Reports were generated, archived and often never to be seen again unless something went wrong.

Two decades later, a lot around electronics manufacturing has changed. Production is global. Volumes are higher. Margins are tighter. Competition is fierce. We talk about AI, cloud platforms, digital twins, and smart factories as the future of the industry.

And yet, as we enter 2026, one of the biggest opportunities in electronics manufacturing remains largely untapped: Unlocking the business value of test data.

So why does it still fall short?

Because many organizations still view test data as an engineering output, not a strategic business asset, and without standardization, it remains fragmented and difficult to use at scale.

Why Valuable Test Data Still Goes Underused

Test data is generated at every stage of electronics manufacturing: ICT, AOI, boundary scan, functional test, and system test. Every unit produces data. Every failure, and every pass, tells a story.

When treated as a strategic asset, test data has the power to improve decision-making, reduce costs, increase quality, accelerate scaling, and protect margins.

Yet in most organizations, test data is still treated primarily as something you look at after a problem appears. Fail or Pass is still at the center and companies accept yield rates far below potential.

Why?

Because the data itself is hard to work with.

It’s fragmented across sites, test systems, and vendors. It’s stored in formats optimized for human-readable reports, not for analysis. And it’s rarely structured in a way that allows test engineers, or the business, to compare results across products, factories, or time.

So instead of being a strategic asset, test data too often becomes reactive evidence.

The Core Problem: No Common Structure

If there is one issue that has persisted from the early 2000s until today, it is the lack of standardization.

• Every test system outputs data differently.
• Every factory configures reports in its own way.
• Engineers are struggling to integrate data into one single format.

Excel files. Text logs. XML. JSON. HTML reports. Databases. Custom schemas. Slight variations that make a big difference.

Even where standards exist, they are often so flexible that two reports from the same test platform are structurally incompatible. This makes aggregation, comparison, and automation unnecessarily difficult.

I’ve seen this repeatedly over the years. When talking to test and production equipment vendors, the answer to questions about data formats is often the same: “That’s up to the customer.”

The result is predictable: valuable data, locked into silos, that cannot easily be reused at scale.

At Scale, Small Differences Become Big Problems

As manufacturing has scaled globally, the challenge has multiplied.

OEMs work with multiple factories. Contract manufacturers operate across regions. Test setups are copied, modified, and adapted locally. Over time, small differences accumulate.

Suddenly, the same product tested in two factories produces data that looks similar but isn’t.

When you try to zoom out and answer simple questions such as:

• are yields improving or declining globally?
• is this failure mode isolated or systemic?
• are test limits applied consistently?

You quickly realize how fragile your data foundation really is.

Without a shared structure, there is no single source of truth, only many partial ones.

AI Is Only as Good as Your Test Data

There is a lot of excitement around AI in manufacturing, and rightly so. But AI does not solve data problems by itself.

AI systems depend on consistent, structured, and reliable input data. If the underlying test data is fragmented or inconsistent, the output will be unreliable, no matter how advanced the algorithm.

In many cases, the barrier to better analytics isn’t a lack of intelligence. It’s a lack of standardization.

Before asking what AI can do with test data, manufacturers need to ask a simpler question:
can we even compare today’s data with yesterdays?

Why Standardization Is the Turning Point

This is where real progress starts.

Standardization doesn’t mean forcing every test system to be identical. It means agreeing on how results are represented, stored, and interpreted, regardless of where they come from.

When test data follows a consistent structure, results can be aggregated across sites and trends become visible. Root causes are easier to identify, automation becomes possible, and with that, advanced analytics can be applied with confidence.

Over the years, this belief has guided how we think about test data at WATS. Not as reports, but as structured information that should remain useful long after a test has completed.

The technology has evolved significantly since the early days. The underlying challenge has not. And that’s exactly why standardization still matters so much as we move further into 2026.

The Business Value of Test Data

We’ve worked with electronics manufacturers for decades and seen the value of structured test data firsthand.

Organizations that succeed in making test data accessible, trusted, and actionable report significant improvements. With real-time data, reliable metrics, and visual insights, they move from reactive firefighting to proactive quality management.

The results are tangible:

• FPY increased with up to 30%points within a year
• Reduced weekly cost of failed FPY by up to 70%
• Savings of up to $1 million per year

Beyond the numbers, our customers see improved quality, faster scaling, better collaboration and decisions - and far less time spent preparing, cleaning, and maintaining data and databases.

Looking Ahead

As we move further into 2026, manufacturing will continue to become more connected, more data-driven, and more automated.

But none of that will deliver its full value unless the foundation is right.

Test data already contains the answers manufacturers are looking for.

Twenty years ago, the problem was obvious, but hard to solve. Today, the tools exist.

As we continue to implement AI capabilities in WATS, we expect to see a dramatic increase in new features, trend analytics and insight, and a change in the way users work with data analytics in WATS.

The question is whether 2026 will be the year your organization finally treats test data as a strategic business asset.