Data Quality Analyst (m/f/d)

Course overview

The Data Quality Analyst identifies, analyzes, and evaluates data quality issues in complex datasets. They develop standards and rules to ensure consistent data collection and management. Core tasks include conducting statistical analyses, implementing quality assurance measures, and monitoring data management processes. They create documentation on data structures, interpret results, and communicate these to relevant stakeholders. Additionally, they develop solution strategies for identified problems and work on the continuous optimization of data flow processes. Expertise in statistical methods, database systems, and programming languages is required. Data Science & AI - Data Analysis and Decision Support through Intelligent Systems • Strategic use of AI and Data Science - Future trends and application potentials • Managing and preparing data sources - Structuring and automating data flows • Practically applying Data Science tools - Technologies and frameworks in use • Developing and analyzing data models - From EDA to predictive forecasts • Targeted use of AI models - Recognizing patterns, finding anomalies, utilizing deep learning • Strategically using data-driven decisions - Real-time analyses and business impact • Effectively presenting analysis results - Data storytelling for decision-makers • Working fairly, transparently, and in compliance with data regulations - GDPR and explainability in practice • Mastering pitfalls of data practice - Promoting data culture and acceptance • Successfully measuring and managing data projects - Optimization through feedback and KPIs Quality Management and Predictive Quality - Data-based Forecasts for Process and Quality Optimization • Quality 4.0 and strategic framework for Predictive Quality • Smart Data Management and data preparation in quality management • Predictive Quality in application: Statistical analyses, machine learning, and software tools • Machine learning for Predictive Quality • Implementation and organizational anchoring of Predictive Quality • Performance measurement and continuous improvement duration • Risk management, data protection regulations, and ethical aspects • Industry-specific application and executive decision architecture Data-driven Digitalization - Sustainable and Efficient Handling of Information • Strategic data value creation and architecture in the company • Process optimization and cost reduction through digital data management • Ecologically responsible data management • Data quality and governance - Standards, roles, responsibilities • Technology platforms and tools for modern data management • Cybersecurity and data protection in the data ecosystem • KPI-based data controlling and performance monitoring • Scaling strategies and barriers in digital data management • Future technologies and data-driven innovation models ------------------------------------------------------------------------------------------------------------------ Work with our career advisors…

Upcoming dates

• 2026-10-18T22:00:00.000Z · 2027-01-24T23:00:00.000Z · Bautzen, Sachsen · Combined Learning
• 2026-10-18T22:00:00.000Z · 2027-01-24T23:00:00.000Z · Ludwigsburg, Württemberg · Combined Learning
• 2026-10-18T22:00:00.000Z · 2027-01-24T23:00:00.000Z · Dortmund · Combined Learning
• 2026-10-18T22:00:00.000Z · 2027-01-24T23:00:00.000Z · Heidelberg, Neckar · Combined Learning
• 2026-10-18T22:00:00.000Z · 2027-01-24T23:00:00.000Z · Dortmund · Combined Learning