paricon Solutions
Cross-System
Data Quality in SAP
Whether you use the Data Quality Framework for enterprise-wide validation of your SAP data or for targeted cleansing of data errors, you get maximum value at minimal implementation effort. The solution is optimized for large data volumes and delivers immediately measurable results – durably improving data consistency, preventing poor decisions caused by flawed data, and accelerating your business processes.
Companies that trust SAP Data Quality with the Data Quality Framework
The Challenge
Data Quality Risks in SAP Systems
Complex SAP landscapes process enormous volumes of data over many years. Different modules, interfaces, and manual data entries create inconsistencies and duplicates that often go unnoticed. Yet cost-efficient processes require high Data Quality: even small errors in Master Data or transactional data can propagate through downstream systems and lead to poor decisions in business departments. Without integrated Data Quality management, quality issues are usually detected late and resolved through costly manual effort. The risks include high personnel overhead, process delays, and in the worst case compliance violations, for example when incorrect data flows into reports or regulatory submissions. Given steadily growing data volumes and rising compliance requirements, an automated, audit-ready approach is essential.
Poor Decisions & Follow-on Costs
Poor Decisions & Follow-on Costs
Poor SAP Data Quality carries a concrete price tag: according to Gartner (2021), organizations lose an average of $12.9 million per year due to inadequate data quality. In SAP systems, this means: faulty material Master Data leads to incorrect orders, inconsistent customer master records block accurate analysis, and duplicate vendors generate uncontrolled payment flows.
According to the MIT Sloan Management Review, poor data can cost organizations 15 to 25% of their revenue, depending on the situation. At the same time, nearly a third of analysts spend more than 40% of their working time solely on validating data (Forrester, 2018). Systematic Master Data quality in SAP turns this cost driver into a competitive advantage.
Manual Effort & Silos
Manual Effort & Silos
When every department maintains its own approach to data validation – whether through Excel lists, manual spot checks, or individually developed ABAP reports – data silos and redundancies emerge that no one can fully oversee. In practice, data owners spend a large share of their time searching for, preparing, and cleansing data.
89% of organizations report that data silos prevent real-time decision-making (Computer Weekly, 2024). Without a central platform that systematically validates SAP Data Quality, data cleansing in SAP remains a reactive, costly ongoing operation.
Compliance and Migration Risk
Compliance and Migration Risk
The numbers are alarming: A large share of SAP S/4HANA migrations encounter significant problems, and 77 percent of organizations cite Data Management as their biggest challenge (Computer Weekly). Inconsistent Master Data and transactional data lead to failed integration tests and significant budget overruns.
Organizations that do not systematically validate SAP Data Quality before migration risk having to remediate 35 to 65 percent of all records. Structured Data Quality in SAP S/4HANA projects is the critical success factor for budget, timeline, and compliance.
up to 0 %
Process Cost Reduction
0 +
Implemented DQ Rules
up to 0 %
Data Quality Improvement
The Solution
Data Quality Framework – SAP Data Quality
What is the Data Quality Framework?
The paricon Data Quality Framework is a fully ABAP-based SAP add-on for efficient Data Quality management in SAP S/4HANA and SAP BW/4HANA. The solution is installed directly in the SAP system and requires no additional hardware or middleware. It covers all functions for securing data quality, from the analysis and validation of incoming data through cleansing of erroneous records to ongoing DQ reporting. All read and write accesses use SAP standard functions, so existing processes are not disrupted but transparently optimized. Through deep SAP integration, including authorizations based on SAP roles, the DQF operates audit-ready and maintenance-free within your system landscape.
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Fully Integrated Add-On
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Source-agnostic – SAP & Non-SAP
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Modular: Validation, Cleansing, Cockpit & Reporting
How Does the Data Quality Framework Work?
Define rules: Versionable rule set in a graphical editor, with templates, quickly extensible without coding.
Check where it counts: in real time at data entry, during data load, or as a batch run, covering completeness, consistency, and plausibility.
Fix errors: Automatic correction and enrichment for standard cases, everything else handled in the central Fiori DQ Cockpit.
Control & demonstrate: Workflows, authorizations (optional Dual Control Principle), and audit-ready logs including KPI dashboards.
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Central, versioned rule set with graphical editor
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Real-Time, Load, and Batch Checks
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Automatic or manual correction in the Fiori Cockpit
Why the Data Quality Framework?
Even the most modern SAP systems hit their limits when Data Quality depends solely on manual checks or rigid standard validations. The paricon DQF delivers the demonstrable value that isolated point tools cannot achieve – fast, scalable, and audit-ready. Because it is fully integrated as an SAP Add-On without modifications, your SAP Clean Core is preserved and updates to new releases are straightforward.
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Ready to Deploy Immediately
Implementation is carried out via SAP add-on (SAINT) in a matter of hours. Preconfigured rule packages cover common quality checks, enabling first productive runs within days. No additional hardware or middleware required.
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Scalable & Proven
In one project, over 500 DQ rules were implemented in parallel in SAP ERP. High-volume capability is assured: the DQF is designed for large data volumes and parallel processing (e.g. hundreds of thousands of postings in a single run).
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Audit-Ready Documentation of All Validation and Correction Runs
Every change is logged without gaps and is traceable for internal or external audits. The rule set itself is versioned and transportable, so changes can be applied in a controlled, audit-compliant manner.
FAQ
Frequently Asked Questions
Here you will find answers to the most common questions about the Data Quality Framework.
What Does SAP Data Quality Mean and How Do I Measure It?
SAP Data Quality describes how complete, correct, consistent, and current your Master Data and transactional data in SAP are. The DQF measures data quality using configurable KPIs – such as completeness rate, cross-module consistency, duplicate rate, and plausibility. Results are visualized in KPI dashboards.
How Does Automated Data Cleansing Work in the DQF?
Do I need SAP Master Data Governance (MDG) if I use the DQF?
How quickly is the DQF ready to use?
Is the Data Quality Framework Clean Core-compliant?
How Does the DQF Support an S/4HANA Migration?
Which data is checked – Master Data only, or transactional data as well?
Still have questions?
Our experts are glad to help you directly!
RELATED CONSULTING
The following consulting services support the use of this solution:
"With the Data Quality Framework, we monitor SAP Data Quality across the entire organization, without disrupting our ongoing processes."
– Michael Eder, Data Quality Expert
Book a no-obligation call now, and we will show you the potential of the DQF for your organization.
