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Data Quality Services

Reliable data quality right from the start

With professional data quality management, you create trust in your data, reduce risks and lay the foundation for successful digitalization projects and AI applications.

Data quality is crucial for generating added value

In many companies, incomplete, outdated or inconsistent data leads to incorrect decisions, inefficient processes and additional costs. Incorrect customer data, duplicates, incorrect master data or unclear responsibilities often require manual corrections – which costs time and money. With professional data quality management you can avoid these problems – and create a reliable, trustworthy database.

How to benefit from high-quality data

  • Detection of data quality problems in real time
  • Reduction of manual error costs and reworking
  • Faster and more informed decisions thanks to reliable data
  • Improved customer satisfaction & target group approach
  • More efficient processes, fewer redundant loads
  • Adherence to regulatory requirements and compliance

Our solution: Data Quality Services

We offer you a comprehensive portfolio of data quality services and solutions – from strategy to operational implementation.

Strategy & Governance

Development of a data quality strategy, definition of standards, roles & responsibilities, data governance structures.

Monitoring & Reporting

Creation of dashboards, KPIs for data quality, automated monitoring, alerts, regular audits.

Analysis & Assessment

Data profiling, data source inventory, evaluation of the current status, identification of weak points and risks.

Data cleansing & enrichment

Duplicate cleansing, standardization, address validation, integration of supplementary data sources, data enrichment.

Technical integration & modules

Tools & interfaces, interfaces to existing systems (CRM, ERP etc.), modular expansion of functions.

Support

Regular reviews, feedback, training.

Five phases, one goal: excellent data quality

Analysis &
Quick Wins

Survey of the current status, immediately identifiable improvements - e.g. data cleansing, duplicate reduction.
Direction Arrows
1

Establish strategy & governance

Define roles, guidelines, data quality standards, data owner/steward.
Direction Arrows
2

Operational implementation & tooling

Introduction of the necessary tools, interfaces, automation, data flow controls.
Direction Arrows
3

Monitoring &
Controlling

KPIs, dashboards, error messages, ongoing quality assurance, escalation mechanisms.
Direction Arrows
4

Continuous improvement & culture

Regular reviews, feedback, training, adaptation of standards to new requirements.
5

Quickstart Data Quality

Receive a practical introduction to modern data quality solutions – and immediately apply what you have learned to your own data. Together with our Data Quality Professional, you will create the basis for sustainable data quality management.

These are the advantages:

  • Introduction to central concepts and typical error types
  • Direct application on your own data
  • First automated quality checks to visualize specific problems
To the workshop

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