Data Quality Management

Every company, every organization today - regardless of size - has to make decisions in the shortest possible time. To ensure this, high quality, reliable data is a must, but getting it is a challenge.

Data quality management refers to measures that ensure the quality of all data in the company. This makes the results, information and predictions derived from data analyses more valuable and valid.  

Data Quality Management: The Ultimate Tool for a Secure Database

Grafik: Integrierte Data Intelligence mit CONET

A secure database forms the basis on the way to a comprehensive digitization. Data Quality Management (DQM) is the tool, because the decisive factor is not just the collection of data or the technical infrastructure, but asking the right questions and finding the right answers.

You only realize how important quality is when it is lacking.  Unclear processing paths, exorbitant or inexplicable efforts in data preparation, process errors, violations of compliance requirements, unrecognized (and therefore surprising) risks, delayed or failed IT projects - these are just a few examples of the effects of poor data quality, which also represents a significant cost factor.

Good data, on the other hand, has a positive impact on the efficiency, effectiveness and flexibility of business, operational and technical processes and project workflows. It guarantees greater adaptability of the company.

  • Consistent data usage and a common understanding of data ensure accurate working and reporting and confidence in reporting solutions.
  • Coordinated data usage and a common understanding of data support informed decision-making.
  • Sophisticated governance enables a lean, pragmatic approach to data management with straightforward data integration across different applications.

Data Quality Management: Our Services at a Glance

We support you in setting up, controlling and monitoring sustainable data quality management. We advise you on determining and formulating your requirements for data and its quality. We also sift through relevant data sources, define and create reports and train your specialist staff in the use of data quality management processes for day-to-day operations.

We speak the language of IT and the specialist departments - which is why we can create trust and understanding and take on the role of communication hub if required.

With our tried-and-tested, pragmatic approach, we establish processes for effective data quality management within a manageable timeframe and without disrupting ongoing operations.


  • Data profiling
  • Requirements analysis
  • Vulnerability analysis



  • Data quality standards
  • Master data and metadata catalogs
  • DQM rules
  • Organization and roles
  • Maturity levels, measurements



  • Data cleansing
  • Connection of DQ systems to operative systems
  • Automated processing
  • Strategy
  • Competence center
  • Information map, data catalog
  • Requirements analysis: Structured data collection, Maturity level assessment, Report and KPI profiles

Processes, Methods and Technologies

  • Data of high quality, reliable and clear
  • Trusted results
  • Metadata management

Standardization and Systematization

  • Data control and management
  • Usage, storage, processing
  • Information and data flow

Data Values

  • Identifying of business value
  • Ignoring or deleting of unnecessary data
  • The right data in the right place at the right time

Contact us

CONET offers you individual consulting, from a free and non-binding first discussion as well as one-day workshops for demonstration and initial situation analysis to the complete data intelligence project.

Feel free to contact our experts for questions about data intelligence. The initial consultation is not binding and free of charge.

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