As we move into an era of AI-assisted decision making, the adage “garbage in, garbage out” has never been more relevant. Yet, the traditional approach of exhaustively cleaning data before leveraging it is becoming obsolete.
Incomplete or inaccurate data fed into AI systems reveals valuable insights. When AI produces suboptimal responses, it can effectively highlight areas where our data needs improvement. Understanding these gaps in your data is often more crucial than cataloging what you already have. In decision-making processes, it’s the unknown or flawed data that poses the greatest risk, making the identification of these areas essential.
We can use AI to concurrently clean and analyze data, swiftly identifying quality issues, detecting gaps, and providing immediate insights. This eliminates the need for costly “clean first” approaches. By using AI to identify what data needs cleaning and how to clean it, we save significant time and resources while accelerating the path to actionable results.
Integrating AI into the data preparation process can create a feedback loop that constantly improves data quality while simultaneously deriving insights. This AI-driven approach handles massive datasets consistently, a critical factor for organizations dealing with siloed and diverse data sources. As our data changes and grows, continuously processing it with AI allows us to make continuous improvements.
Don’t worry about cleaning your data for AI to process – use AI to process your data. Embracing AI for data preparation allows you to simultaneously prepare and analyze data, optimize resources, and leverage imperfections to identify blind spots. As AI is both the means and the end for its implementation, what are you waiting for?
