From Chaos to Clarity: A Pragmatic Approach to Scale Data Quality Mgmt

In today's world, data appears to be the magic ingredient adding spice to every business strategy. Organizations are capitalizing on their (vast and varied) data sources, stirring up concrete opportunities like:

Automating Environment, Social & Governance (ESG) reporting; Leveraging LLMs with internal data; Or creating any other kind of data products.

However, one recurrent issue continues to hinder progress and comprise confidence - Data Quality. While the data landscape has grown exponentially, the struggle with maintaining data quality is an old, yet persistent, problem faced by all organizations. Inaccurate, incomplete, or outdated data lead to: massive manual efforts, delayed deliveries of new use cases, doubts over the reliability of data-driven decisions and insights.

Addressing this requires a holistic, streamlined, and proactive approach to Data Quality Management (DQM) - an approach that goes beyond mere tools and technologies.

Join our next webinar, in collaboration with SteepConsult, to discover a practical and modular approach to building a highly effective DQM system. Together with business and tech experts in the field, you will learn the 5 dimensions of a successful DQM system, how Publicis Media automated its DQM system, and how to supercharge DQM thanks to an ML-supported approach.

Key takeaways: Data Quality pitfalls

The 5 dimensions of a DQM system: From roles and processes to mitigation strategies Leveraging an ML-supported approach to DQM

Real-world case studies: Automated DQM system at Publicis Media

Webinar designed for:

• Chief Data Officer
• Head of Data Management
• Data (Quality) Manager
• Data Platform Manager
• Data Governance Experts
• Data Product Owner

Or anyone with a stake in Data Quality and Data Governance.