Comprehensive Analysis and Optimization of Your Master Data Quality

MDM Health Check: Assess Master Data Management Maturity

Get a well-founded overview of the maturity level of your master data management and identify concrete optimization potential.

  • 01Comprehensive assessment of the quality and usability of your master data
  • 02Identification of data quality problems and their economic impacts
  • 03Prioritized action recommendations for quick wins and strategic improvements
  • 04Clear roadmap for the further development of your master data management
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11+Years of experience
120+Employees
540+Projects
ISO 27001certified

Systematic Diagnosis of Your Master Data Management

High-quality master data is crucial for business success and digital transformation of an organization. However, there is often a lack of overview of the actual state of master data management. Our Health Check provides you with a well-founded assessment of the strengths and weaknesses of your MDM and delivers concrete starting points for improvements. The results create transparency, ensure confidence in action, and form the basis for fact-based investment decisions.

Our MDM Health Check analyzes all relevant aspects of your master data management and provides you with a comprehensive report with detailed results, improvement potential, and prioritized action recommendations. The results are transparently prepared and linked to concrete optimization measures, so you can immediately proceed with implementation.

4 service modules

Service scope

01

Data Quality Assessment

Comprehensive analysis of the quality of your master data based on objective metrics. We examine completeness, correctness, consistency, timeliness, and other quality dimensions and quantify problem areas and their business impacts.

  • Development of domain-specific quality metrics and evaluation criteria
  • Conducting automated data profiling and quality analyses
  • Identification of data quality problems and their causes
  • Quantification of business impacts of quality problems
02

MDM Process and Governance Analysis

Assessment of your MDM processes, governance structures, and organizational aspects. We analyze data maintenance processes, roles and responsibilities, as well as decision structures and identify optimization potential.

  • Analysis of existing data maintenance processes and workflow efficiency
  • Assessment of governance model, roles, and responsibilities
  • Review of policies, standards, and control mechanisms
  • Identification of process inefficiencies and organizational weaknesses
03

MDM Technology and System Integration

Technical analysis of your MDM system landscape and its integration. We assess the technologies, architectures, and interfaces used and identify technical optimization potential for a more effective MDM solution.

  • Assessment of MDM technologies and architectures used
  • Analysis of system integration and data synchronization
  • Review of data models, matching rules, and validations
  • Identification of technical weaknesses and improvement potential
04

MDM Maturity Model and Benchmarking

Assessment of the maturity level of your master data management based on established models and comparison with industry benchmarks. We show where your company stands compared to best practices and competitors.

  • Assessment along a comprehensive MDM maturity model
  • Comparison with industry benchmarks and best practices
  • Identification of strengths, weaknesses, and development potential
  • Definition of a realistic target vision and development path

5 phases

Our Approach

Our MDM Health Check follows a structured, proven approach that considers all relevant dimensions of master data management and places them in a comprehensive context. The analysis is conducted both quantitatively with objective metrics and qualitatively through expert interviews and best practice comparisons.

  1. Step 1

    Preparation and Scoping - Definition of the scope of investigation, determination of master data domains and systems to be analyzed, identification of relevant stakeholders

  2. Step 2

    Data Analysis - Conducting quantitative data quality analyses, technical system reviews, and process observations to capture the current state

  3. Step 3

    Stakeholder Interviews - Surveying key persons from business units, IT, and management on challenges, requirements, and improvement potential

  4. Step 4

    Evaluation and Benchmarking - Consolidation of results, assessment based on established maturity models, and comparison with industry benchmarks

  5. Step 5

    Reporting and Roadmap - Creation of a detailed assessment report with prioritized action recommendations and concrete implementation roadmap

Asan Stefanski

Your contact

Asan Stefanski

Head of Digital Transformation

11+ years of experience, Applied Computer Science degree, Strategic planning and management of AI projects, Cyber Security, Secure Software Development, AI

+49 69 913 113-01 info@advisori.de LinkedIn
A systematic health check forms the foundation for every successful MDM initiative. It creates transparency about the status quo, quantifies improvement potential, and provides a fact-based decision basis for targeted investments. Our experience shows that most companies can identify both short-term quick wins and strategic improvement potential after an MDM Health Check that they did not have on their radar before.
ADVISORI expert opinion

Our Strengths

  • 01Many years of experience in analyzing and optimizing master data management solutions
  • 02Comprehensive approach that equally considers technical, organizational, and process aspects
  • 03Proven assessment framework with over 100 evaluation criteria in all relevant dimensions
  • 04Field-tested methods for quantifying quality problems and their business impacts

Expert Tip

A systematic health check should be at the beginning of every MDM initiative, but can also provide valuable input for established master data management programs. Our experience shows that even seemingly well-functioning MDM solutions reveal significant optimization potential upon closer examination. Identifying and addressing these not only leads to better data quality but also reduces ongoing data maintenance costs by an average of 25-30% and significantly reduces error costs in downstream processes.

7 QUESTIONS, BRIEFLY ANSWERED

Frequently asked questions about Master Data Management Health Check

What is a master data health check and when is it useful?

A master data health check is a systematic analysis of the maturity level of your master data management program. It evaluates data quality, processes, governance structures and technical implementation using objective metrics and delivers a prioritized roadmap with concrete improvement measures.

A health check is useful before launching an MDM program, when data quality remains unsatisfactory despite existing MDM initiatives, after organizational or technical changes, and as a regular check-up every 12 to 18 months.

How does an MDM assessment typically proceed?

An MDM assessment follows five phases: First, scope and objectives are defined during preparation. In the quantitative analysis, master data is examined using objective metrics for completeness, accuracy, consistency and timeliness. The qualitative analysis includes stakeholder interviews and process evaluations. During evaluation, results are compared against best practices and industry benchmarks. Finally, a report is produced with prioritized recommendations and a concrete implementation roadmap.

Which dimensions are evaluated in a master data health check?

A comprehensive master data health check typically evaluates five dimensions: data quality (completeness, accuracy, consistency, timeliness, duplicates), data maintenance processes and workflows, governance structures and responsibilities, technical architecture and system integration, and organizational maturity level. The evaluation uses a maturity model with defined levels that shows precisely where your organization stands and which steps lead to the next level.

What distinguishes an MDM health check from an IT audit?

An IT audit examines compliance with policies, standards and regulatory requirements. An MDM health check, by contrast, holistically analyzes your master data management with a focus on optimization potential. Beyond technology, it considers organization, processes, governance and data quality. The goal is not compliance verification but identification of concrete improvement measures that lead to better data quality and measurable cost savings averaging 25 to 30 percent in data maintenance.

How can organizations optimally prepare for a master data health check?

For optimal preparation, follow three steps: First, create an inventory of existing master data domains (customers, suppliers, materials, financial data) and associated systems. Second, identify relevant stakeholders from business departments and IT and schedule them for interviews. Third, document known pain points and current challenges. The better the preparation, the more targeted the health check can be conducted and the faster it delivers actionable results.

How is the ROI of an MDM health check measured?

The ROI of an MDM health check can be measured through several metrics: reduction of error costs in downstream processes (invoicing, logistics, reporting), savings in ongoing data maintenance (typically 25 to 30 percent), avoided costs through early detection of data quality problems, and accelerated decision-making through reliable master data. Experience shows that a health check pays for itself through implementation of identified quick wins within just a few weeks.

How does a master data health check support digital transformation?

Digital transformation requires reliable data. A master data health check identifies data quality issues that block automation, AI projects and data-driven decisions. It reveals which master data domains need to be cleansed and standardized for planned initiatives. The health check also creates the foundation for a sustainable data governance structure that ensures data quality remains secured long-term, even as complexity grows and new requirements emerge.