Data & AI

Data Quality Management

With the OMMAX Data Quality Management, you continuously monitor and improve the quality of business-critical data. We help you detect errors early, define clear quality rules, and implement automated processes that ensure your data remains accurate, reliable, and ready for advanced use.

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Data Quality Management

High-quality data as the foundation of every data-driven organization

Data Quality Management (DQM) is an operational process for the continuous monitoring and improvement of business-critical data. In the digital world, Data Quality Management is an essential part of any modern data architecture, because all data-driven applications such as Machine Learning, Business Intelligence, Customer Relationship Management (CRM), Supply Chain Management (SCM), or Enterprise Resource Planning (ERP) rely on high-quality data to generate valuable results.
Our machine learning algorithms, combined with the proven OMMAX Data Quality Management cycle, accelerate your journey toward consistently high data quality. We introduce the standards, processes, and automation you need to reach lasting excellence without heavy operational effort.

Our offering

Comprehensive Data Quality Management services

Data profiling and quality analysis

We identify relevant data fields and sources, apply ML-based profiling, and create transparency around usage patterns and initial problem areas.

Definition of data quality rules and targets

With your domain experts, we define desired data quality states and quality rules. Machine learning accelerates this step significantly.

Automated data quality measurement

We implement rule-based measurement mechanisms, reports, and KPIs that reliably detect deviations and provide actionable insights.

Root cause analysis and remediation planning

We analyze process and system-level causes of poor data quality and define targeted, economically viable improvement actions.

Data correction and quality improvement

We support efficient correction using algorithms and reference data, enabling sustainable improvement of data accuracy and consistency.

Continuous monitoring

We establish standards, processes, and tools for ongoing monitoring, ensuring long-term stability of your data quality.

Our Data Quality Management approach

A structured 6-step process for sustainable data quality

Explore

Together with your team, we identify relevant systems, tables, and fields using our methodology and algorithms, because not all data is equally important. We automatically generate statistics and machine learning–based suggestions for data quality rules. This provides rapid insights into which data is used, how often it is used, and where first issues appear. This step is also known as Data Profiling.

Define

We use the rule suggestions and statistics generated in the exploration phase to define requirements for detecting faulty data together with your domain experts. Machine learning accelerates this process dramatically, enabling us to quickly determine the target state for high-quality data. This step forms a key building block of Data Quality Management.

Measure

Next, we compare defined quality rules with the current data to identify deviations. From this, we create reports containing potential data errors and data quality KPIs. These insights form the basis for sustainable correction of data issues.

Analyze

We evaluate the measurement results together with your experts. Often, rules must be fine-tuned before analysis. Once rules are precise enough, the root causes of data quality problems are identified. These causes may lie in technical systems or within business processes.

Improve

After identifying the root causes of poor data quality, we define and implement suitable remediation actions. We consider cost efficiency and business impact when selecting measures. We also support efficient correction using algorithms and reference data.

Monitor & control

The established quality rules are then used for continuous monitoring and evaluation of your data quality. This allows early detection of new issues and structured tracking of improvement measures. As a result, you achieve stable long-term data quality within your Data Ops environment.

Impact

Our data & AI services in numbers

3,000+ tech, data & AI projects

100+ tech experts in-house

90+ net promoter score

50+ countries covered

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High data quality is never accidental. It results from clear rules, continuous monitoring, and automated processes. We help organizations turn their data into a reliable and value-generating asset.

Dr. Christian Fürber
Partner Data & AI

Why OMMAX

Your partner for data quality excellence

Deep data and quality expertise

With IQI now part of OMMAX, we combine leading expertise in data quality, data excellence, and machine-learning-driven quality processes.

End-to-end quality lifecycle

We support the entire DQM lifecycle from profiling and rule definition to correction and monitoring.

Scalable processes and standards

We implement clear standards, ownership structures, roles, and rules that enable sustainable data quality across systems.

Measurable business value

Stable data quality improves processes, decision-making, reporting, and ML performance while reducing operational risks and costs.

Our Data & AI partners

Turning intelligence into impact with proven partnerships

Get in touch

Want to enhance your data quality? Start the conversation with our experts.

Dr. Christian Fürber

Partner Data & AI

Daniel Soujon

Partner & CTO

Eva Bammann

Director Data & AI

Frequently asked questions

Everything you need to know about the OMMAX Data Quality Management service

DQM is a continuous process for monitoring and improving the quality of business-critical data. It ensures that data is correct, complete, and usable.

All data-driven systems such as ML, BI, CRM, SCM, and ERP require reliable and high quality data to generate meaningful and value-creating insights.

Explore, Define, Measure, Analyze, Improve, Monitor — a structured six-step approach for sustainable data quality.

Higher data quality, fewer errors, more accurate reporting, better ML models, smoother processes, and lower operational costs.

Machine learning accelerates profiling, rule definition, identification of data errors, and correction planning, outperforming traditional manual approaches.

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