Individuality Versus Broad Trends

Business intelligence (BI) dashboards are a great tool for organizations to access and analyze large amounts of data in a meaningful and intuitive way. These dashboards often use aggregated data that is segmented into territories or distribution channels, making it easy for decision-makers to understand high-level trends and performance.

However, while aggregated metrics are robust and a few data errors here or there don’t matter in the big picture, AI systems make decisions at a much more detailed level. These systems often operate at the level of individual customers, and even small errors in data can result in incorrect decisions with potentially serious consequences.

In many countries, failing to correct incorrect customer data is illegal, highlighting the need for much more careful data pipelines when it comes to AI use cases. While a time-proven and reliable BI data pipeline may be sufficient for dashboarding, it may not be up to the task for AI.

To leverage the full potential of AI and avoid potentially serious consequences, organizations need to implement data pipelines that are designed specifically for AI. These pipelines need to be much more careful and nuanced than traditional BI data pipelines, allowing for the detection and correction of data errors at a granular level.

By recognizing the unique challenges posed by AI and taking steps to address them, organizations can harness the full potential of hyperindividualization, while minimizing the risk of costly and embarrassing errors. It’s time to build data pipelines using tools that are specifically designed for AI use cases.