Data Quality Training – Ensuring Data Reliability, Reducing Risks, and Enabling Better Decisions
Upon request
1 day
Online, in Munich, or in-house
Upon request
German or English
Digital Certificate of Participation
How companies can sustainably improve data quality and create a robust foundation for business, analytics, and AI. Erroneous, incomplete, or inconsistent data incur high costs for companies, skew analyses, and make it difficult to make informed decisions. At the same time, the demands placed on reporting, business intelligence, analytics, and AI applications are constantly increasing. This is precisely why high data quality is becoming a critical success factor for modern organizations. Our data quality training course provides practical guidance on how companies can systematically measure and improve data quality and embed it sustainably into their data management processes. You’ll learn to analyze quality issues in a structured manner, assess risks, and establish processes that ensure data remains reliable and usable over the long term—for better decisions, more efficient processes, and greater trust in data.
Content and Structure of the Data Quality Training Course
This training program combines the fundamentals of data quality management with specific methods and practical approaches that companies can apply immediately.
- Introduction to Data Quality, Data Quality Management, and Data Governance
- Data as the Foundation for Information, Decisions, and Competitiveness
- The Importance of Data Quality for Reporting, Processes, Analytics, and AI
- Requirements from Business, Regulation, and Organization
- Data Quality as a Business Risk and Success Factor
- Assessing the Company’s Current Status
- Identifying specific, current use cases that are affected by data quality
- Understanding Data Quality Dimensions and Quality Criteria
- Measurability Through Profiling, Monitoring, and Key Performance Indicators
- Analysis of Cause-and-Effect Relationships
- Assess Risks and Their Impact on Processes, KPIs, and Decisions
- Establishing a Structured Data Quality Management System
- Roles and Responsibilities
- (Domain) Data Owner
- Data Steward
- Data Customer
- Standard Processes for Profiling, Monitoring, and Error Tracking
- Collaboration between Business Units, IT, BI, and Analytics
- Governance Structures, Policies, and Decision-Making Processes
- Integration into existing organizational and process frameworks
- Development of Data Quality Goals and KPIs in Use Cases
- Establishment of a Data Quality Reporting System and Data Quality Index
- Define Rules for Master Data and Transaction Data
- Data Lineage and Traceable Data Processes
- Ensuring Data Quality in BI, Analytics, Data Warehouses, and AI Applications
- Prioritizing Improvement Measures—Based on Benefit and Risk
- Cost-Benefit Analysis of Data Quality Measures
- Developing Quick Wins, Pilot Areas, and Sustainable Roadmaps
- Strengthening Quality Awareness and Data Accountability
- Communication, Training, and Change Management Related to Data Quality
- Best Practices and Real-World Scenarios for the Successful Implementation of DQM
What You Can Put into Practice Right Away After the Data Quality Training
After the seminar, you will have specific methods and tools at your disposal to improve data quality in a sustainable way and manage it professionally.
- Reliably Identify and Analyze Data Quality Issues
- Assess Risks and Their Impact on Processes and Decisions
- Establishing a Structured Data Quality Management Framework Within the Company
- Define Roles, Responsibilities, and Governance
- Establish KPIs, Monitoring, and Reporting for Data Quality
- Ensuring Data Quality in Analytics, BI, and AI Applications
- Prioritize and implement improvement measures in a sustainable manner
Who is the data quality training program intended for?
This training is intended for:
- Specialists and Managers in Data and Information Management
- Managers from Data Management, BI, and Analytics
- Data Owners, Data Stewards, and Data Customers
- Project Managers and Organizational Developers
- Companies that want to systematically improve data quality
Suitable for beginners as well as organizations looking to professionalize their existing processes.
Prerequisites for the Data Quality Training Course
No special technical knowledge is required to participate.
Customized In-House Training
We design customized in-house training programs—tailored perfectly to your needs.
- Content tailored to your reality
- Individual case studies from your company
- Development of an organization-specific prompt library
- Optional: Follow-up coaching & transfer sessions
About Us as a Training Provider for the Data Quality Seminar
We are a management consulting firm with many years of experience in data management, business intelligence, and organizational transformation. Our data quality training courses combine in-depth knowledge with proven methods and concrete implementation strategies. We help companies establish data quality as a strategic success factor and sustainably improve data-driven collaboration.
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Expertise in Data Governance – Your Trainer
Tim Naumann helps companies implement modern data and governance structures. His focus is on sustainably improving data quality, data management, and analytics processes. In his seminars, he combines strategic perspectives with pragmatic methods and presents complex topics in a clear, structured, and practical way.
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- Strategic: Establishing Data Quality , Data Quality Management, and Governance for the Long Term
- Secure: Reliable Data Structures for Reporting, Analytics, and AI Applications
- Proven in Practice: Many Years of Experience in Data Management, BI, and Organizational Transformation
- Measurable: Focus on Data Quality, Transparency, KPIs, and Sound Decisions
TISAX and ISO certification apply only to the Munich location
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TISAX and ISO certification apply only to the Munich location
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FAQ – Data Quality Training
Data forms the foundation for decision-making, reporting, processes, analytics, and AI applications. If data is inaccurate, incomplete, or inconsistent, it leads to incorrect analyses, inefficient processes, and significant financial risks. High data quality, on the other hand, fosters transparency, trust, and better decision-making throughout the entire organization.
This training is suitable for organizations of all sizes that use data systematically or wish to adopt a more data-driven approach. It is particularly relevant for companies with BI, analytics, reporting, or AI initiatives, where data quality directly impacts results and processes. Companies with rapidly expanding system landscapes also stand to benefit significantly.
No. The training is intentionally designed so that specialists and managers without in-depth technical knowledge can also participate. The content is explained in an easy-to-understand way and taught with a practical focus, so that both beginners and experienced data managers can gain concrete benefits from the course.
Participants will learn how to identify and assess data quality issues and implement structured improvements. In addition, they will gain concrete approaches to governance, KPIs, monitoring, and embedding data quality management within the organization. This creates a solid foundation for sustainable improvements within the company.
A central role. Data quality can only be improved over the long term if responsibilities are clearly defined. Roles such as data owner, data steward, or data customer help establish standards, structure decision-making, and ensure the long-term stability of data processes.
BI, analytics, and AI applications are only as good as the data on which they are based. Poor data quality leads to flawed models, unreliable reports, and low confidence in results. This training course teaches methods for ensuring data quality in analytical and AI-related processes.