Trusted data will define enterprise success in 2026

AI is transforming industries at an unprecedented pace. Boards are demanding AI roadmaps, CTOs are accelerating digital transformation, and each of those initiative sits on the same foundation: data.

Here’s an uncomfortable truth: many enterprises can’t tell you whether that foundation is solid. They hope and maybe even assume it is, but they can’t be certain if they aren’t testing it.

Data errors hide inside complex pipelines and often surface weeks later, after they’ve warped forecasts, corrupted AI outputs, or influenced business decisions.

Modern validation must be continuous, scalable, and keep up with how the data actually moves. This is all possible with Tricentis Data Integrity, now available in the cloud.

Watch the webinar: Untested data could cost you everything in the AI era

The silent crisis hiding in plain sight

Data quality tools tell you what your data looks like, and data observability tools alert you when something changes. Neither of them tests whether your data is actually correct.

That gap is where businesses bleed. It manifests slowly: AI models produce unreliable outputs because they were trained on flawed data. SAP migrations go live with corrupt records because testing was manual and patchy. Analytics dashboards that executives trust implicitly are built on numbers nobody validated.

This is not a data engineering problem; it’s a business risk. Solving it is a task that belongs squarely in the boardroom.

Introducing Tricentis Data Integrity, now in the cloud

Tricentis Data Integrity is a low-code, cloud-native solution for automated data testing and reconciliation. It validates data accuracy across your entire enterprise IT landscape, continuously, end-to-end, and at scale, so that every decision your business makes is built on data you can trust.

Unlike a point solution or data quality monitor, Data Integrity brings the same rigorous, risk-based testing methodology that Tricentis pioneered for software quality to the data layer of your enterprise.

AI has raised the stakes to a level that manual testing cannot meet

For years, the argument for automated data testing was largely operational: save time, reduce manual effort, catch errors earlier. Those arguments were always valid, but AI has made them existential.
When AI models are trained on inaccurate, inconsistent, or unvalidated data, the outputs are unreliable at best and catastrophically wrong at worst. A hallucinating AI model in a mission-critical application can be more than just embarrassing; it can be dangerous. The difference between those two outcomes is often a function of data quality upstream.

AI is only as reliable as the data behind it.

More: Four hidden risks of bad data in the age of agentic AI

Organizations that validate their data continuously and treat data testing as a core part of their quality engineering practice will build AI systems that actually work.

Those that don’t will spend the next five years wondering why their AI investments are underdelivering.

How Data Integrity keeps bad data from becoming business damage

Tricentis Data Integrity connects to your entire data landscape, including SAP, Salesforce, Oracle, SQL Server, Snowflake, Databricks, and more.

It uses pre-built connectors, validates data accuracy against business rules, reconciles data across multiple systems, and continuously monitors for anomalies and drift before they compound into business damage.

These capabilities work together to ensure data remains accurate, trusted, and business ready at all times.

Low-code authoring

Business analysts and data managers can build comprehensive data test cases without deep coding expertise. This solution democratizes data testing across the organization.

Continuous, end-to-end testing and reconciliation

Data continuously flows through ETL pipelines and can be altered at any stage. Data Integrity continuously validates data accuracy across your entire IT landscape, ensuring business operations are always running on trusted data rather than a snapshot from last quarter.

Risk-based prioritization

Not all data carries equal risk. Data Integrity focuses testing efforts on high-risk areas first, maximizing business impact and resource efficiency. This is the same risk-based methodology that underpins Tricentis Tosca for application testing, now applied to data.

Cloud-native deployment

With Tricentis Data Integrity, there is no infrastructure to stand up, and there are no manual updates to manage. Cloud-native deployment means teams can connect their first data source and run initial tests within hours instead of months. Because this solution integrates natively into the Tricentis platform alongside Tosca and NeoLoad, organizations can test functionality, load, and data in a unified quality engineering workflow.

Enterprises that made the move

The results from early Data Integrity deployments are transformational:

• A global real estate leader achieved a 90% increase in testing efficiency, saving hundreds of hours of manual validation while achieving 100% test coverage across billions of records, including structured and semi-structured datasets across Snowflake and numerous applications.
• Flowers Foods achieved 50% automation of their regression test suite during an SAP S/4HANA migration, with full insight into SAP configuration change impacts and zero data errors reaching production.

These results show what happens when enterprises treat data testing as a strategic discipline rather than an afterthought.

What comes next: Agentic AI for data testing

Coming in late 2026, Tricentis Data Integrity will introduce agentic AI capabilities that transform natural language descriptions into precise SQL queries and comprehensive data test cases.

Imagine a data tester or business analyst describing a data validation requirement in plain English, and having the AI agent generate syntactically correct, context-aware SQL queries tailored to the specific data source and schema, whether that is Databricks, Snowflake, SAP, or Oracle. Tests generated in minutes, not hours.

Ready to unlock decision-grade data to support your AI initiatives?

Join us July 29 to learn how to prevent the data errors that slip through pipelines, corrupt AI outputs, and turn into business-level failures.

Claim your spot