Data Quality Management Implementation

The duration of an implementation depends heavily on the starting point, scope, and specific requirements of your organisation. Based on our experience, an initial implementation phase with first measurable results typically takes three to six months, while a fully embedded data quality management system generally requires twelve to eighteen months. ADVISORI applies an iterative approach that delivers quick wins in early phases, making the value of the project visible at an early stage. Our proven methodology allows us to significantly reduce implementation timelines compared to conventional approaches.

A successful implementation requires above all management commitment and a willingness to embrace organisational and cultural change. Technical infrastructure and existing data systems are important boundary conditions, but not an obstacle — ADVISORI guides you from the initial inventory through to the target architecture. Equally important is the availability of internal contacts from business units and IT who, together with our consulting team, drive the implementation forward. At the start of each project, we conduct a structured readiness analysis to identify areas for action at an early stage and adapt the implementation strategy accordingly.

Sustainability is a core principle of our implementation methodology and is addressed from the very beginning of the design phase. ADVISORI places particular emphasis on building internal competencies, establishing clear governance structures, and fostering a lived data quality awareness throughout the entire organisation. Through targeted change management measures, training programmes, and the establishment of data quality roles such as Data Stewards and Data Owners, we ensure that data quality management is understood not as a one-off project, but as a continuous process. Regular reviews and a defined maturity model enable progress to be measured and the organisation to be kept on a path of continuous improvement.

Particularly in the financial sector, compliance with regulatory requirements is a key driver for professional data quality management. ADVISORI takes into account relevant regulations during implementation, including BCBS 239, MaRisk, DORA, the EU Data Strategy, and data protection requirements under the GDPR. Our consultants possess in-depth expertise in regulatory compliance and combine this with methodological data quality knowledge, ensuring that your implementation rests on a sound regulatory foundation from the outset. Through our own ISO 27001 certification and many years of experience in regulated environments, we bring a strong understanding of the specific requirements of financial institutions.

Implementation success is measured using an individually defined KPI framework that reflects both technical and business dimensions of data quality. Typical metrics include data quality scores across dimensions such as completeness, accuracy, consistency, and timeliness, as well as business metrics such as reduced error rates in processes, shorter reporting cycles, or avoided regulatory findings. At the start of the project, ADVISORI works with you to define a baseline and clear target values, ensuring that progress is documented in a transparent and traceable manner. Our AI-supported platform assists with continuous monitoring and automated reporting on the quality status of your data.

Yes, ADVISORI supports organisations both in building new data quality management capabilities from scratch and in further developing and consolidating existing data quality initiatives. At the outset, we conduct a comprehensive inventory in which existing processes, tools, governance structures, and maturity levels are analysed and assessed. On this basis, we develop a tailored strategy that builds on existing strengths, closes gaps in a targeted manner, and eliminates redundant structures. This pragmatic approach protects investments already made and ensures that your organisation is efficiently advanced to the next quality level.

A structured DQM implementation follows six core phases: First, a comprehensive assessment of existing data quality and management practices is conducted. Building on this, an individual implementation strategy with roadmap and prioritisation is developed. In the third phase, governance structures, roles such as Data Stewards and Data Owners, and responsibilities are defined. This is followed by the selection and integration of suitable data quality tools and the establishment of measurement and monitoring processes. In parallel, we support the organisational transformation through targeted change management. Finally, a continuous improvement model is embedded that ensures long-term success through maturity level measurements.