Consistent master data, reliable decisions

Many MDM initiatives fail not because of technology, but because of a lack of a clear target vision. We translate your business goals into an MDM target vision that describes which master data (customer, supplier, product, location, organisation) is managed as a ‘system of record’, how the golden record is created, and how data is used via ERP, CRM, PIM and data platforms. To do this, we define domain scope, critical use cases (ERP rollout, M&A, e-commerce, reporting), and standards for identifiers, mandatory attributes, hierarchies, and reference data.
In the next step, we design the target architecture and decision logic: central MDM hub, hybrid or registry approach, including integration principles (API, ETL, events), publishing to downstream systems and role models

for maintenance and release. The roadmap prioritises according to value contribution and feasibility, and includes milestones, dependencies, resource requirements and a set of KPIs for data quality and process throughput times. The result is an actionable plan in waves per domain that brings together tooling, governance and migration and provides a solid justification for investment. We use maturity models to quantify gaps between the current state and the target state and to divide the implementation plan into realistic waves. Optionally, we supplement this with business cases and enablement plans to ensure that the roadmap is financially viable and effective in everyday use. Workshops included. Priorities become clear. Includes decision template for the steering committee.

MDM only works if responsibilities are clearly defined. We establish a data governance and operating model for master data that specifies who defines data, who maintains it and who makes decisions. Data owners are responsible for governance results, while data stewards are responsible for operational implementation and quality in day-to-day business. Together, we define domain and attribute responsibilities, decision-making rights (e.g. creation, modification, merging), approvals, escalation paths and a lean committee setup.

We design end-to-end master data processes from creation to decommissioning: structured acceptance and recording of a master data request via service catalogue/forms, validation rules, approval workflows, mandatory fields,

documentation requirements, and audit and compliance requirements (e.g. GDPR-relevant attributes). Governance policies are implemented as standards and templates, including naming conventions, reference data maintenance, key management, and DQ rules. This makes master data maintenance plannable: clear RACI, measurable quality targets, defined processing times and a backlog for continuous improvement. You receive a governance manual with role profiles, process descriptions and templates for data issues, requests and DQ exceptions. In addition, we define KPIs (e.g. duplicate rate, rule violations, throughput time) so that business departments and IT have a common view of data quality and priorities. This significantly reduces the coordination effort. Includes data council setup, meeting frequency and escalation path.

Clean master data starts with a clear data model. We define a uniform and neutral target data model for your MDM domains (customer, supplier, product), including attribute catalogue, mandatory fields, definitions, hierarchies and reference data. This clarifies what a customer or item ‘is’, how keys are structured and which validation rules apply in ERP, CRM and PIM. We then translate these standards into a set of data quality rules with KPIs and monitoring so that completeness, consistency and timeliness can be measured.
One focus is on avoiding duplicates and cleaning up data:
we configure matching logic (including fuzzy matching), define merge and survivorship rules, and build the golden record in such a way that conflicts between sources

are resolved in a traceable manner. In addition, we set up workflows for data issues, exceptions and corrections so that data quality is not just a one-off cleansing exercise, but remains permanently anchored in the master data processes. We start with data profiling, define DQ thresholds for each attribute and prioritise rules according to their impact on the process.

For migrations, we provide cleansing and cutover criteria as well as random checks to ensure that the database is stable before go-live. The Golden Record is operated as a continuous process: recording, matching, validation, publication and maintenance. DQ remains measurable during operation. Includes a rule repository for thresholds, test data and internal root cause analysis for rule violations.

We implement MDM in such a way that it works within your system landscape, not as an isolated tool. Based on the target vision and governance, we select the appropriate implementation style: consolidation, coexistence or transaction, including clear rules on where master data is created, modified and published. We configure data models, matching/survivorship, validations, workflows and role rights, and connect source and target systems (SAP, Microsoft Dynamics, CRM, PIM, Commerce, Data Platform) via APIs, ETL or event-driven patterns.

One focus is integration into operational processes: service catalogue, structured acceptance and recording of master data requests, approvals, exception handling and stewardship queues, so that changes remain traceable

and auditable. We deliver interface design, mapping, test cases, cutover plan and monitoring to ensure that golden records are distributed stably and downstream reports see consistent entities.

After go-live, we support enablement, operational handover and a release and DQ backlog so that your master data management grows as new domains, markets or systems are added. Optionally, we assist with tool selection and licence/TCO evaluation and embed data protection, authorisations and metadata/lineage in the solution so that governance, data quality and integration work together.

This keeps changes traceable and your core clean. Includes operational documentation, alerting support model SLAs and handover to IT and business departments.