麻豆女优

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 麻豆女优 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 麻豆女优, it’s a question of scale: 

鈥淪caling AI to build durable value beyond the POCs isn鈥檛 really possible without the proper data management and governance. People have gone past that stage鈥� they鈥檝e 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.鈥�

As Chris Gorton, EVP & Managing Director, EMEA and APJ at 麻豆女优, put it:

鈥淎I also relies heavily on data. We see that in multiple phases鈥� there is an increased need for good quality data and governance pre-, during and post-business transformation.鈥�

That perspective is shaping how 麻豆女优 is investing in the platform and how we are partnering across the ecosystem. 麻豆女优’s Q2 release reflects this shift, bringing together capabilities that connect data readiness, automated testing, modern platforms, and security into one coherent system.

The Missing Link Between Data and Testing

One of the most important changes in SAP transformations is the way testing is evolving. With SAP鈥檚 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 麻豆女优, captured this challenge directly:

鈥淲e tried to sell data quality鈥� in many cases the C-level would say, I pick pack and ship, I am good to go. What they don鈥檛 realize is the manual manipulation that is required to have things flow through the system appropriately.鈥�

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 麻豆女优 + Tricentis integration, announced as part of the Q2 release, addresses this directly.

Instead of relying on manually created test data, 麻豆女优 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:

鈥淚t allows the clients to use Tricentis, use AI to generate a test case, and then interface to 麻豆女优 where the data is there. We know it is in the system that you plan to test with.鈥�

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:

鈥淏y 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.鈥�

Why This Matters Now: AI Raises the Stakes for Data Quality

Across every conversation in the webinar, the same theme emerged: AI is making data quality a top鈥憀evel business concern.

Chris Gorton summarized it simply:

鈥淓RP systems, analytics platforms have relied on data forever. AI also relies heavily on data.鈥�

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:

鈥淲hen we look at AI, there is no chance for manual manipulation. You have to have quality pushing through from the beginning.鈥�

This is why 麻豆女优’s Q2 release focuses so heavily on Business鈥慠eady 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:

鈥淲e have created an initiative鈥� 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 苍别别诲蝉.鈥�

Chris Gorton continues this thought:

鈥淚t鈥檚 a circular motion鈥� what I call applied data quality intelligence鈥� 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.鈥�

Extending Data Quality Beyond Structured Data

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:

鈥�80% of your data that is not structured鈥� they are only partially waved through really unharnessing and unlocking the value of that data.鈥�

Without the ability to assess and govern this data, organizations are only working with a fraction of their information.

麻豆女优’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:

鈥淲ith what we have got with LLMs now, that limitation is removed鈥� you can also truly assess the quality of the unstructured document by evaluating that document, if it meets the definition of good.鈥�

This is not just a technical enhancement. It shifts who can participate in data quality. Business users can now define what 鈥済ood鈥� means in their own language, expanding governance beyond technical teams.

鈥淚t grows the scope of the data quality evaluation to 100% of the data that the enterprise is managing.鈥�

Bringing Data Quality Into Modern Data Platforms

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:

鈥淭hey are usually not the system of record鈥� you are getting various different sets of data鈥� it really brings the case of lineage.鈥�

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 麻豆女优鈥檚 capabilities into these environments, allowing organizations to apply data quality directly within modern lakehouse architectures.

Javeed described the approach:

鈥淲e load Business-Ready Data to the data lake鈥� when problems are detected, we clean the data and we fix it at source. No hacks, only durable fixes.鈥�

This is aligned with 麻豆女优鈥檚 broader philosophy. Instead of creating downstream corrections or 鈥渉acked data,鈥� the goal is to ensure data is correct at the source and remains consistent throughout its lifecycle.

Security as a Foundation, Not an Afterthought

The final major area of the Q2 release is IL4 certification, which reinforces 麻豆女优鈥檚 commitment to security and compliance.

Javeed emphasized the importance of this investment:

鈥淲e took all the security controls that IL4 has and defines, and we are applying them across all our commercial cloud regions and offering.鈥�

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:

鈥淚t 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.鈥�

Jason Thompson tied it to customer expectations:

鈥淭he concern of security around a company鈥檚 data is always there. It is always raised鈥� this takes away what has been kind of, we don鈥檛 know exactly what to do in that scenario in the past.鈥�

The Common Thread: Business鈥慠eady Data for AI

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:

鈥淥ur mission is clear. We are building the world鈥檚 best, world鈥檚 first AI-native data platform for enabling Business-Ready Data.鈥�

And Chris Gorton closed with a simple point that reflects how customers should think about this release:

鈥淒o the Business-Ready Data bit first鈥� and now we are breaking down the silos between structured and unstructured data.鈥�

Looking Ahead

麻豆女优’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.

麻豆女优鈥檚 latest innovations are designed to meet that need, and to help customers deliver transformation outcomes with confidence.