Four Questions Every SAP Organisation Should Ask About Data Integrity

Summary

Data integrity has quietly become one of the most consequential gaps in SAP quality engineering. As Business Data Cloud, SAP Joule, and increasingly connected enterprise environments place greater reliance on trusted data, validating software functionality is no longer sufficient. Four questions can help organisations identify where that gap is most likely to cost them.

Why data integrity is becoming a Quality Engineering priority in SAP

For years, SAP quality engineering has focused on validating software. Do business processes work as expected? Are integrations functioning correctly? Will users be able to complete their daily tasks after go-live? Those questions remain important, but today's SAP landscape demands more. Business Data Cloud, AI capabilities such as Joule, process intelligence with Signavio, and increasingly connected enterprise environments all depend on trusted data. As a result, quality engineering is expanding beyond software testing to validate the data and business outcomes that organisations rely on to operate with confidence. Yes, data validation always had its place as part of an overall quality assurance process, however these new uses and re-uses of data through multiple derivations and transformations pose new challenges.

Data Has Become a Strategic Asset in SAP Environments, and Quality Engineering Must Reflect That

Every SAP implementation generates enormous volumes of business data. This data no longer supports business processes alone. Today, data has become a strategic asset in its own right. Financial reporting, supply chain planning, workforce management and executive dashboards increasingly draw information from multiple SAP and non-SAP sources and process it into increasingly complex derived assets. SAP Business Data Cloud reflects this shift. Rather than keeping operational and analytical data in separate silos, it brings information together from SAP and non-SAP environments to create a consistent foundation for reporting, enterprise intelligence and AI in ways that open new possibilities for enterprise analytics and AI. At the same time, it raises the importance of ensuring that the data feeding these capabilities and the business rules it follows remains accurate, complete and trustworthy.

For quality engineering teams, this shifts and reprioritises what needs to be validated. A process can execute exactly as designed while still producing unreliable business outcomes if the underlying data is incomplete, inconsistent or inaccurate. As organisations continue to modernise their SAP environments, confidence in business data becomes just as important as confidence in the applications themselves. This confidence necessitates a nuanced approach to data validation and the specialized expertise to carry it out in a meaningful business context.

Traditional SAP Testing Validates Functionality but Does Not Confirm Whether Data Can Be Trusted

Most SAP testing strategies confirm that processes execute correctly but do not verify whether the data those processes produce and consume can be trusted. They validate funcionality: Can a purchase order be created? Does payroll complete successfully? Does an interface transfer information between two systems?

Modern SAP environments raise a different set of questions. Whether the data feeding AI assistants can be trusted. Whether financial figures stay consistent across connected systems. Whether recent changes have quietly affected critical master data. Whether business rules are robustly applied to data transformation processes. At TTC Global, we increasingly see organisations encountering these questions during S/4HANA transformations, often after functional testing has already passed.

Four Questions Every SAP Organisation Should Ask

Can you trust the data that feeds AI?

Validating data quality is part of validating AI itself. SAP Joule and other AI capabilities are only as reliable as the data behind them. An AI assistant cannot tell the difference between accurate and outdated customer records, inconsistent financial data or incomplete supplier information. It works with whatever it is given.

Can you trust data across end-to-end business processes?

Business processes rarely stay within a single application. An order may originate in Salesforce, continue through SAP S/4HANA, trigger warehouse activities and eventually feed financial reporting or analytics platforms. Business Data Cloud makes these connections even more valuable by bringing information together from multiple sources.

SAP Signavio helps organisations understand how business processes actually flow across the enterprise and where opportunities for improvement exist. 

Once processes have been redesigned, organisations need confidence that they continue to work correctly under real operational conditions. Understanding a business process is one thing. Demonstrating that it continues to work under real operating conditions requires quality engineering.

Can you trust your data after every SAP change?

Even organisations pursuing a Clean Core strategy continue to introduce quarterly updates, integrations, configuration changes and extensions. Technologies such as Tricentis LiveCompare help organisations understand the business impact of every SAP change before testing begins. Rather than executing large regression suites after every update, teams can focus on the processes, integrations and data that have genuinely been affected. That makes continuous testing more efficient while maintaining confidence in business-critical functionality.

The importance of this approach is reflected in SAP research. Horváth found that 65% of completed S/4HANA migrations experienced major quality defects after go-live. Many of those issues emerge only when business processes and data are validated together rather than as isolated technical components.

Can you prove that your critical business data remains correct?

Some business data carries far greater consequences than others. Payroll information, financial transactions, regulatory reporting and supply chain data all require a high degree of confidence. Small inconsistencies can lead to compliance issues, operational disruption or costly business errors.

This is where data integrity becomes a quality engineering discipline rather than a migration activity. Automated validation helps organisations confirm that critical business data remains complete, accurate and consistent throughout the SAP landscape, even as applications, integrations and business processes continue to evolve.

Executive Confidence in SAP Environments Now Depends on Trusted Data

Business leaders rarely ask whether a particular SAP transaction executed successfully. They want to know whether the financial reports are reliable, whether AI recommendations can be trusted, whether regulatory requirements are met and whether the organisation can introduce change without disrupting operations.

As SAP programmes become more interconnected, confidence in business data directly influences executive decision-making. Quality engineering provides that confidence by validating applications, business processes and the data that connects them. This is the same gap we explored in The Hidden Risk in SAP Transformation: as Clean Core and BTP shift risk toward integration boundaries, independent validation becomes structural, not optional.

Connecting Signavio, LiveCompare and Data Integrity Together Creates a Complete Picture of SAP Quality

Business processes, enterprise data, SAP changes and application functionality all influence the success of a transformation programme. Each calls for a different quality capability, yet they ultimately contribute to the same objective: confidence in business outcomes.

SAP Signavio helps organisations understand and optimise business processes. SAP Business Data Cloud creates a trusted foundation for analytics, AI and enterprise reporting. Tricentis LiveCompare identifies the business impact of SAP changes so testing can focus on the processes, integrations and data that have genuinely been affected. Automated testing validates functional behaviour, while data validation confirms that critical business information remains accurate and reliable.

Viewed together, these capabilities create a much richer picture of quality than any individual technology can provide.

Quality engineering provides the thread that connects these capabilities. Rather than treating business processes, applications and data as separate domains, it validates how they work together to support reliable business outcomes, while applying the relevant expertise to each of them. That integrated approach enables organisations to introduce change more frequently while maintaining confidence across increasingly complex SAP landscapes.

TTC Global Brings Together Business Processes, Applications, and Data Validation 

Building confidence in enterprise data requires more than implementing individual tools. That is where TTC Global's approach differs. We help organisations build an integrated quality engineering capability that connects business processes, applications, testing and data validation into a single operating model. This gives programme leaders greater confidence when introducing change across evolving SAP environments. With experience across more than 250 SAP programmes and recognition as Tricentis Global Implementation Partner of the Year in 2025 and 2026, TTC Global combines independent quality engineering with deep expertise across Tricentis Tosca, LiveCompare and Data Integrity.

Rather than approaching testing as a standalone activity, the focus is on helping organisations establish sustainable quality capabilities that continue long after a migration or implementation has finished. 

As SAP landscapes become more connected and AI plays a greater role in day-to-day operations, organisations need confidence in far more than the software itself. They need confidence that every release, every business process and every business decision is supported by trusted data.

That is why data integrity is rapidly becoming one of the defining disciplines in SAP quality engineering. Organisations that invest in trusted data today will be better equipped to adopt AI, accelerate SAP innovation and make business decisions with confidence for years to come.

If your organisation is exploring SAP transformation, AI or Business Data Cloud initiatives, now is a good time to consider how you build confidence in the data that underpins them. We'd be happy to discuss how other organisations are approaching that challenge.

Frequently Asked Questions

Why is data integrity now a quality engineering concern, not just a migration activity?

A process can execute exactly as designed while still producing unreliable business outcomes if the underlying data is incomplete or inaccurate. As SAP environments increasingly power AI capabilities, enterprise reporting and executive decision-making, data integrity has become a continuous quality engineering discipline rather than a one-time migration activity.

Can AI tools like SAP Joule be trusted if data quality has not been validated?

AI capabilities work with whatever data they are given and have no mechanism for distinguishing reliable information from incomplete or inconsistent records. Organisations that introduce SAP Joule into business processes without first validating their data are effectively delegating decisions to an AI operating on an unverified foundation.

How does SAP Business Data Cloud change what needs to be tested?

SAP Business Data Cloud connects operational and analytical data across SAP and non-SAP environments, which increases the surface area that quality engineering needs to cover. When data from multiple sources feeds reporting, AI and enterprise intelligence, a quality issue in any one source can propagate across the entire landscape without being immediately visible.

How does Tricentis LiveCompare help with data integrity after SAP changes?

Tricentis LiveCompare identifies the business impact of every SAP change before testing begins, focusing effort on the processes, integrations and data genuinely affected rather than executing broad regression suites after every update. Research shows that the majority of post-go-live defects in SAP programmes emerge at the point where business processes and data intersect.

What is the difference between functional SAP testing and data integrity validation?

Functional testing confirms that business processes execute correctly. Data integrity validation confirms that the data those processes produce and consume remains accurate, complete and consistent across the SAP landscape.