Data Quality

The
Foundation

DQ is
Business-
Critical

Poor data quality is costly – according to Gartner, it can lead to revenue losses of up to 15% per year. The causes:

• Duplicates and flawed master data

• Inconsistent product information across channels

• Missing validations in operational processes

• Lack of transparency across data sources

The consequences: incorrect reports, inefficient processes, missed market opportunities – and, in many cases, compliance risks.

Defining DQ

According to the ISO standard for quality management, quality is the degree to which specified requirements are met. The same applies to data. Using defined data quality dimensions, we identify measurable data attributes that allow us to evaluate quality based on clear standards – and derive the appropriate actions.

More than
IT

Data quality is often seen as an IT responsibility. In our view, however, it is a company-wide commitment. Data quality initiatives must span the entire data lifecycle – from the moment data is created to the moment it is used – in order to enable powerful data stories.

Every business process should align with the data management strategy so that quality, security, and standardization can work together as a balanced ecosystem.

That’s why the data quality frameworks we develop with your stakeholders define not only IT-related roles, but also actively involve business units.

Choosing
the right technology

Modern data quality concepts are, of course, supported by sophisticated software solutions that continuously monitor and improve data quality through defined rules.

We support you throughout the entire lifecycle of your data quality initiative – from selecting the right tools and designing a robust framework, to developing a proof of concept and fully implementing the solution. The required data quality rules are defined together with your business experts based on your specific requirements – and then implemented by us.

Our approach is technology-agnostic: We adapt to your existing system landscape – not the other way around. Whether SAP, Stibo Systems, Informatica, Akeneo, or custom-built solutions – we bring the integration expertise needed to establish and automate your data quality processes efficiently and sustainably.

Our
approach

We take a holistic view of data quality – strategically, technologically, and operationally. Our methodology combines best practices with extensive project experience.

1. Analysis & Assessment

• Data quality audit: automated and rule-based

• Definition of relevant metrics (e.g., accuracy, consistency, completeness)

• Prioritization based on business impact

2. Governance & Processes

• Definition of roles and responsibilities (Data Stewardship)

• Establishment of clear workflows (e.g., product data onboarding)

• Integration into existing MDM or PIM structures

3. Automation & Monitoring

• Rule-based validation and correction proposals

• Continuous monitoring and KPI dashboards

• Seamless integration with existing IT systems and platforms