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SAP processes can only be as reliable as the data they build on. Incomplete, duplicate, or inconsistent data does not only affect individual records. It can impede reporting, automation, migrations, and business decisions.
Data quality initiatives therefore often start with a clear goal: reduce duplicates, fill in missing values, resolve inconsistencies, and improve the data foundation for processes, reporting, or transformations.
Results can become visible quickly. But what matters is not only how good the data is at the end of the project. What matters is whether the quality achieved is maintained in day-to-day SAP operations. After cleansing, new records continue to be created. Data is maintained manually, taken over through interfaces, and processed in different business departments. Without binding rules, clear responsibilities, and continuous checks, faulty or incomplete data can build up again.
The project ends, data maintenance does not
Data quality projects do not fail at the cleansing stage. Cleansing almost always works, because the tools exist and the methods are proven, so the technical implementation is rarely the central problem. The real problem begins where cleansing ends: with anchoring data quality permanently in operational processes.
A one-off cleansing removes existing errors, but it does not automatically change the processes that create new data. If creation rules are not applied consistently after the project ends, duplicates can appear again. If mandatory fields are secured neither technically nor organizationally, they may stay empty on new records as well. And where clear business responsibilities are missing, it is often not defined who assesses and corrects deviations.
Cleansing is therefore only one part of sustainable data quality management. The second part is operations: data quality has to be checked, assessed, and worked on regularly.
What manufacturing does better
In production, nobody would run a quality inspection once and then declare the topic closed. Six Sigma, SPC, and continuous improvement are standard disciplines. Quality metrics are collected regularly, deviations often trigger immediate action, and everyone knows who owns which metric.
The same principles apply to SAP data. The analogy is close at hand: data is the raw material from which reports, decisions, and automated processes are made. If that raw material is faulty, the quality of the results inevitably suffers. As in manufacturing, quality does not come from a single inspection but from continuous monitoring, clear responsibilities, and consistent improvement.
Three prerequisites for the operating mode
Rules and ownership are assigned per data object.
Rule-based checks run continuously instead of once.
Findings are assessed, assigned, processed, and tracked.
Assign business responsibility clearly
Data quality is not a purely IT task. IT provides systems and technical functions. Business departments, however, define which data has to be complete, correct, and consistent for their processes.
For the relevant data objects, it should therefore be defined:
- Who defines the business quality rules?
- Who assesses the deviations that are found?
- Who decides on corrections?
- Who tracks how data quality develops?
The concrete allocation of roles depends on the organization and on the data objects involved. What matters is that responsibility is not only documented but anchored in the way people work.
Measure data quality continuously
A one-off measurement shows the state at a given point in time. For operations, how quality develops over time matters as well.
Rule-based checks can make visible, for example:
- whether mandatory fields are fully maintained,
- whether values are plausible from a business perspective,
- whether data sets are consistent,
- whether duplicates or reconciliation differences occur,
- how error rates and processing status change over time.
This ongoing checking needs functions that cover different points in time: validations on entry, controls during data loads, or recurring checks of existing data. The paricon Data Quality Framework maps these requirements directly in the SAP system. Results can be evaluated centrally in the DQ Cockpit and through reporting functions, so that trends, anomalies, and open processing status remain traceable.
Organize correction processes traceably
Errors that have been found do not improve data quality yet. A defined process for assessment, assignment, correction, and approval is needed.
For that, quality issues have to be assignable to the SAP objects concerned and to the responsible business departments. It is equally important that check runs, processing steps, corrections, and approvals are documented traceably.
These workflows can be mapped through a central Fiori cockpit, for example. In the paricon Data Quality Framework, quality issues are processed in a structured way, assigned to the responsible units, and tracked from there.
This turns a quality metric into a concrete task: a finding is detected, assessed, assigned, processed, and then checked again.
Using data quality metrics well
Metrics create transparency. They show, for example, in which data objects error rates are rising, which rules are broken most often, and how open quality issues develop.
For these metrics to take effect, they have to match the level of responsibility. Operational teams need concrete findings and the records concerned. Data owners and business departments need an overview of rules, causes, and processing status. Management needs condensed statements on trends, risks, and required action.
Not every data quality metric belongs in every management meeting. What counts are metrics with a traceable link to important processes, projects, or regulatory requirements.
Why a permanent operating model can pay off
Cleansing treats symptoms. An operating model prevents causes.
The greater leverage therefore lies not in the next round of cleansing but in building a permanent operating model. Investing once in clear responsibilities, rule-based checks, and traceable correction processes creates the basis for quality issues not to grow unnoticed again.
In the long run this can be more economical than recurring cleansing projects, because errors become visible early and additional checking and correction effort can be managed better.
The real value therefore lies not only in better data but also in less recurring rework.
Where to start
Before rules, thresholds, and responsibilities for operations are defined, transparency about the current state of the data is needed.
The paricon smartscan can create a solid starting point for this as an upstream system analysis. It makes relevant findings in the SAP landscape visible and helps to prioritize areas for action.
On that basis, quality rules, responsibilities, and processing workflows can be built in a targeted way. The Data Quality Framework then supports ongoing checking and processing directly in the SAP system, with rule-based controls, traceable correction processes, cockpit functions, and reporting.
The paricon smartscan makes the current state of the data visible before rules and thresholds are set. The Data Quality Framework then takes over ongoing checking and processing inside the SAP system.
