The term „Data Centric AI“ is primarily found in the fields of Artificial Intelligence, Big Data and Smart Data, and Digital Transformation. With Data Centric AI, the focus is no longer solely on the model or algorithm, but above all on the quality and selection of the data used to train an Artificial Intelligence (AI).
While previously the main focus was on making models ever smarter, Data Centric AI focuses on collecting and processing the right, clean, and relevant data. This often leads to better results because an AI cannot work meaningfully without good data, even with the best algorithm.
A simple example: Imagine you want to deploy an AI that automatically recognises and correctly assigns invoices from emails. With Data-Centric AI, you first check if your data, meaning the collected invoices, is complete, correct, and well-labelled. You remove faulty or duplicate entries and supplement missing information. Only then is the AI trained with it.
This is how Data Centric AI increases the reliability and expressiveness of AI systems – and makes them better because they work with high-quality data from the outset.
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