The Danger of Inconsistent Data Pipelines in AI
Your modern data stack isn’t designed for the two parallel, yet consistent, data pipelines that AI systems require.
Business intelligence (BI) dashboards are designed to simplify data and present it in well-defined and intuitive metrics. These metrics are used by humans to make informed business decisions. However, AI is different. To make decisions, AI first needs to learn how to link patterns in the data to desired outcomes.
Machine learning (ML) algorithms, which power modern AI systems, use a sample of historical data to learn and develop models. Only after the ML algorithm has been trained and tested is it put into production. Once in production, the AI can see all the current production data and make decisions.
However, frequently, the training data was created using a bespoke data pipeline. If the AI’s training data pipeline is different from the production data pipeline, it can cause serious problems.
Unlike BI data pipelines, which are not designed for training data and lack the tools to ensure consistency in the data pipelines, a modern data stack should guarantee consistent data pipelines for AI training data and production. By providing a consistent and reliable data pipeline, organizations can ensure that their AI models are accurate and effective, minimizing the risk of costly errors and delivering better business outcomes.
In today’s fast-paced business environment, organizations need the agility and flexibility to make informed decisions quickly and efficiently. By embracing a modern data stack designed specifically for AI, organizations can take advantage of the full potential of this powerful technology and achieve better business results. It’s time to switch to data pipeline tools that are designed specifically for training/production consistency.