Artificial intelligence promises to transform customer acquisition, personalization, and e-commerce performance optimization.
But you still have to be able to trust the data that feeds it!
📊 Incorrect data in a dashboard can skew an analysis.
🤖 When this same data is processed by AI, it can lead to inappropriate recommendations, incorrect decisions, and even large-scale automated actions.
With AI, the cost of poor data quality takes on a whole new dimension.
For 60 minutes, Nicolas Malo and Evelien Vieveen from Optimal Ways, accompanied by Thomas PINTE, CEO of Netvigie (& Seenaptic), shared concrete examples and operational approaches for:
🔹 Understanding the 1–10–100–1000 rule and how AI amplifies errors.
🔹 Discover Spec-Driven Measurement to ensure reliable data collection in the age of AI.
🔹 Identify the main sources of errors in e-commerce data.
🔹 Measure the impact on customer acquisition, personalization, and conversion.
🔹 Detect anomalies using our Weekly Data Quality Check (WDQC).
🔹 Automate quality and compliance monitoring with Netvigie.
🔹 Build data foundations that are truly AI-ready.
💡 Our belief: AI does not turn bad data into good decisions. Above all, it risks amplifying the consequences of that data.
If these issues are relevant to you, don’t miss the “replay” of our exclusive webinar.
