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Only 32 percent of companies achieve a positive ROI from their AI projects, according to the Wasabi Global Cloud Storage Index 2026. The biggest obstacles often lie not in the algorithms themselves, but in the data.*
That number strikes a nerve because it exposes a pattern: companies invest millions in new SAP technologies, in S/4HANA migrations, in AI-driven processes. But they invest too little in the foundation all of that stands on. Their data.
At paricon, we have worked with SAP data since 1997. More than 120 product customers, from banks to mid-sized manufacturers, have taught us: data quality alone is not enough. Governance alone is not enough either. What companies need is a discipline that connects both and ties them to compliance requirements. We call that Data Excellence.
Data Excellence: More Than a Buzzword
Data Excellence is not a new tool and not another framework document. It describes the state in which three things work at the same time: data quality, data governance, and compliance.
Data quality means that master and transactional data are correct, complete, and current. Not estimated, but measured. With rules that automatically check whether a material master contains all mandatory fields, whether business partner data is free of duplicates, whether classifications stay consistent across company codes.
Data governance defines who is responsible for which data. Who maintains the customer master? Who decides on data standards? Who reviews compliance with them? Without these structures, even the best data quality decays within a few months.
Compliance ensures that data, processes, and evidence meet regulatory requirements. That includes, for example, GDPR-compliant deletion and anonymization processes, traceable data flows, audit-proof logging, and auditable processes – for instance in the context of DORA or BCBS 239 in the financial industry.
Most SAP organizations handle each of these three areas separately. Different teams, different budgets, different timelines. That is exactly the problem.
Three Pillars, One State
Correct, complete, current. Measured, not estimated – through automated rules.
Who is accountable for which data, decides on standards, and reviews compliance.
GDPR, DORA, BCBS 239 – traceable, audit-proof, auditable.
Why SAP Data Quality Becomes a Strategic Priority in 2026
2026 brings a combination of regulatory pressure and technological upheaval that puts SAP data at the center. Three developments interlock:
Any one of these developments alone would already be a good reason to focus on data quality. Together, they make Data Excellence the prerequisite for almost every strategic SAP decision in the years ahead.
What Happens When Companies Ignore Data Excellence
The consequences are concrete, not abstract.
AI projects deliver no results because the input data is incomplete or inconsistent.
Migration projects run months longer than planned because data quality problems only surface during the test cycle.
Compliance audits reveal gaps because no one knows where personal data is actually stored in the SAP system.
In our experience across numerous customer projects, one pattern keeps repeating: the technology works and the tools are in place. What is missing is the organizational anchoring of data responsibility, and the willingness to honestly measure the current state of one’s own data instead of estimating it.
Three Pillars, One Goal
Data Excellence is not a state you reach once and check off. It is an operating discipline. Comparable to quality management in manufacturing: no production plant runs a single quality check and then declares the topic closed.
The three pillars interlock: without measurement (data quality), the factual foundation is missing. Without rules and responsibility (governance), improvements decay. Without regulatory embedding (compliance), the whole effort stays an internal exercise with no external accountability.
Companies that treat Data Excellence as an integrated discipline, instead of handling quality, governance, and compliance as separate projects, reduce effort and gain the ability to act. Not through another strategy paper, but through practice.
One example: a financial institution has its master data quality measured and finds that the duplicate rate in business partner data stands at 9 percent. That has three consequences at once:
One problem. Numerous consequences. One measurement as the starting point.
The First Step: Know Where Your Data Stands
Before you commit to a migration strategy, before you invest in AI, before you define governance roles, you need an answer to one simple question: How good is your SAP data, really?
The paricon smartscan delivers a defensible assessment for exactly that. Directly in the SAP system, with no data export, with automated checks across the relevant data and system dimensions. The result is a transparent inventory that lets you set priorities and derive next steps.
Whoever knows the state of their data makes better decisions. Whoever does not, works on assumptions.
System transparency. Free. 5 dimensions.Directly in the SAP system – no data export.
The smartscan combines five dimensions in a single Fiori view, directly in your system. Guesswork becomes a defensible basis for prioritization and decisions – as a Data Excellence Index.
*Source: Wasabi Global Cloud Storage Index 2026 (based on 1,700 IT decision-makers worldwide)
