Date: Q3/Q4 2025
Language: German/ English
The success of Data & Analytics (D&A) initiatives and AI projects relies heavily on the quality of the underlying data. Poor data quality leads to unreliable insights, inaccurate predictions, and ineffective decision-making processes—a classic case of “garbage in, garbage out.” However, many organizations struggle with ensuring data quality, as they deal with fragmented data sources, inconsistent data governance, and ever-growing data volumes.
This webinar will explore the current challenges organizations face in maintaining high data quality for their D&A and AI efforts. We will examine common issues such as incomplete or inaccurate data, integrating data silos, and cultural and organizational challenges. Attendees will gain insights into the importance of data governance, the role of automated data cleansing, and strategies to ensure that their AI models are built on clean, accurate data.