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The podcast discusses the importance of the four levels of data analytics: descriptive, diagnostic, predictive, and prescriptive. The hosts emphasize that descriptive analytics, often considered basic, is fundamental because it answers "What happened?" without which further analysis is impossible. Diagnostic analytics answers "Why did it happen?", predictive analytics answers "What will happen?", and prescriptive analytics addresses "How can we make a certain outcome occur?"

They stress that the foundational elements like data quality, data architecture, and data governance are crucial for successful analytics. Many organizations overlook these aspects, leading to masked issues and unreliable data. There's a need to correct data at the source and ensure ongoing governance to maintain data quality.

The discussion highlights that most companies think they are more advanced in their analytics capabilities than they are. It's important to recognize where they truly stand and understand that achieving higher levels of analytics requires a solid foundation in descriptive analytics. The hosts also discuss the cultural aspects of data governance and the need for a process-oriented approach to maintaining data quality. The episode concludes with a reminder that good governance and a solid data foundation are essential for reliable and advanced analytics.

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