Explore Syniti’s Q2 2026 platform innovations, including real‑time test data provisioning with Tricentis, AI‑powered unstructured data quality, Databricks and Snowflake integration, and IL4 security certification—enabling Business‑Ready Data for SAP transformations and AI initiatives.
Enterprise transformation is changing. It is no longer just about moving to SAP S/4HANA or modernizing systems. It is about how organizations connect data, testing, and automation into a continuous cycle that improves quality and reduces risk.
In a recent conversation with Syniti leaders, the message was clear: data is now the foundation for everything, including AI, and it needs to be addressed earlier and more consistently than ever before.
For Javeed Nizami, Chief Technology Officer at Syniti, it's a question of scale:
“Scaling AI to build durable value beyond the POCs isn’t really possible without the proper data management and governance. People have gone past that stage… they’ve spent a year or two now trying to do POCs, and then they quickly realized that at the end of that, no business outcome and no real value, durable value is being driven.”
As Chris Gorton, EVP & Managing Director, EMEA and APJ at Syniti, put it:
“AI also relies heavily on data. We see that in multiple phases… there is an increased need for good quality data and governance pre-, during and post-business transformation.”
That perspective is shaping how Syniti is investing in the platform and how we are partnering across the ecosystem. Syniti's Q2 release reflects this shift, bringing together capabilities that connect data readiness, automated testing, modern platforms, and security into one coherent system.
Watch the Fireside chat with Syniti leadership on the latest release of innovations.
One of the most important changes in SAP transformations is the way testing is evolving. With SAP’s Agent-led Toolchain and the growing use of Tricentis, testing is becoming more automated, more continuous, and more tightly integrated into the transformation lifecycle.
But testing still depends on one thing that has historically been difficult to get right: data that is actually ready to use.
Jason Thompson, SVP, Global Solution Architect at Syniti, captured this challenge directly:
“We tried to sell data quality… in many cases the C-level would say, I pick pack and ship, I am good to go. What they don’t realize is the manual manipulation that is required to have things flow through the system appropriately.”
That manual manipulation is exactly what slows down testing. Teams spend time extracting data, formatting it, and trying to make it usable. Over time, that data becomes stale and disconnected from the real system.
The new Syniti + Tricentis integration, announced as part of the Q2 release, addresses this directly.
Instead of relying on manually created test data, Syniti delivers cleansed, production-grade, business-ready data directly into Tricentis Tosca. That means test scenarios reflect real-world conditions, not approximations.
Jason explained the impact:
“It allows the clients to use Tricentis, use AI to generate a test case, and then interface to Syniti where the data is there. We know it is in the system that you plan to test with.”
This closes a critical gap between migration and testing. It also supports ongoing validation, not just one-time cutover testing, which is increasingly important as organizations adopt more continuous delivery models.
Chris Gorton framed the broader outcome:
“By being part of the SAP Agent-led Toolchain and being integrated, we are helping customers move at scales they never would have thought was possible, at much less to no risk with quality built in.”
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Across every conversation in the webinar, the same theme emerged: AI is making data quality a top‑level business concern.
Chris Gorton summarized it simply:
“ERP systems, analytics platforms have relied on data forever. AI also relies heavily on data.”
The difference now is that AI does not tolerate the same level of inconsistency or manual correction that older systems could absorb. As Jason noted:
“When we look at AI, there is no chance for manual manipulation. You have to have quality pushing through from the beginning.”
This is why Syniti's Q2 release focuses so heavily on Business‑Ready Data. It is not just about cleaning data after the fact. It is about ensuring data is trustworthy, governed, and usable from the moment it enters the system.
Javeed Nizami described this as a core strategic shift:
“We have created an initiative… AI for Data and Data for AI. On the one side, we are accelerating innovation using AI. On the other side, that innovation helps our customers produce the Business-Ready Data that AI needs.”
Chris Gorton continues this thought:
“It’s a circular motion… what I call applied data quality intelligence… allows you to build a very, very rich, holistic insight into your data that you can then use to drive the business case and the remediation back to the various systems of record.”
One of the most significant innovations in the Q2 release is the expansion of data quality into unstructured data.
As enterprises adopt AI, they are increasingly dependent on content that is not traditionally governed: contracts, PDFs, images, and other documents. Chris Gorton highlighted the scale of the problem:
“80% of your data that is not structured… they are only partially waved through really unharnessing and unlocking the value of that data.”
Without the ability to assess and govern this data, organizations are only working with a fraction of their information.
Syniti's new unstructured data quality capability changes that by applying AI to extract and evaluate data in its original form, as well as converting it into structured formats when needed.
Javeed explained the significance:
“With what we have got with LLMs now, that limitation is removed… you can also truly assess the quality of the unstructured document by evaluating that document, if it meets the definition of good.”
This is not just a technical enhancement. It shifts who can participate in data quality. Business users can now define what “good” means in their own language, expanding governance beyond technical teams.
“It grows the scope of the data quality evaluation to 100% of the data that the enterprise is managing.”
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Enterprise data is not just in SAP anymore. Increasingly, it lives in platforms like Databricks and Snowflake, where organizations are running analytics, AI, and data products at scale.
Jason Thompson highlighted the complexity:
“They are usually not the system of record… you are getting various different sets of data… it really brings the case of lineage.”
The challenge is that data often arrives from multiple sources, with varying levels of trust and consistency. In some cases, it is even incomplete due to failed pipelines or missing transfers.
The Q2 release extends Syniti’s capabilities into these environments, allowing organizations to apply data quality directly within modern lakehouse architectures.
Javeed described the approach:
“We load Business-Ready Data to the data lake… when problems are detected, we clean the data and we fix it at source. No hacks, only durable fixes.”
This is aligned with Syniti’s broader philosophy. Instead of creating downstream corrections or “hacked data,” the goal is to ensure data is correct at the source and remains consistent throughout its lifecycle.
The final major area of the Q2 release is IL4 certification, which reinforces Syniti’s commitment to security and compliance.
Javeed emphasized the importance of this investment:
“We took all the security controls that IL4 has and defines, and we are applying them across all our commercial cloud regions and offering.”
This is not limited to government customers. The same standards are applied globally, reflecting a broader expectation in the market around data sovereignty and trust.
Chris Gorton added:
“It says that we are on a roadmap to what is a very complicated but critical landscape. And we are taking the first big step in that direction.”
Jason Thompson tied it to customer expectations:
“The concern of security around a company’s data is always there. It is always raised… this takes away what has been kind of, we don’t know exactly what to do in that scenario in the past.”
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Across all of these innovations, the message is consistent. Whether it is testing, unstructured data, analytics platforms, or security, the foundation remains the same.
Javeed summarized it best:
“Our mission is clear. We are building the world’s best, world’s first AI-native data platform for enabling Business-Ready Data.”
And Chris Gorton closed with a simple point that reflects how customers should think about this release:
“Do the Business-Ready Data bit first… and now we are breaking down the silos between structured and unstructured data.”
Syniti's Q2 release is not just a set of features. It reflects a broader evolution in how enterprises will operate in the AI era: with data, testing, and governance working together as a single system.
As organizations move further into SAP transformations, AI adoption, and modern data architectures, the need for trusted, governed, and usable data will only increase.
Syniti’s latest innovations are designed to meet that need, and to help customers deliver transformation outcomes with confidence.
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