Years of enterprise experience have taught me that models are only as good as their data. Data science teams spend on average over half their time cleaning and preparing data for processing, using fallible processes that impact project delivery and team morale.

Having worked on several pioneering AI models, I am often reminded of the complexity involved in working with different types of data. The world ahead is multi-modal and technology which supports images, text and audio in several different data formats.

Quality data is crucial for AI models and applications. It’s essential that this data is ethically sourced and responsibly managed. The importance of data ethics in AI for our future cannot be understated, and it’s likely to be increasingly regulated and subjected to third-party audits. Understanding your data’s origin, its access history, and its management is fundamental. Developing AI data pipelines that not only meet ethical standards but also align with upcoming legal requirements is vital for sustainable progress.

When it comes to developing AI, one of the challenges that frequently crosses my radar is data. At first glance, pipelines seem simple. However, going even one layer in has shown us the dozens of different things that must go right in an AI data pipeline in order to deliver high quality AI.