Photo by Vlad Vasnetsov on Pexels

Despite the hype surrounding Artificial Intelligence, many organizations are finding their AI initiatives stalled by familiar data challenges. Echoing the struggles encountered with “Big Data,” issues like data quality, consistency, and accessibility are proving to be significant hurdles in AI implementation.

The lack of a solid data foundation is a primary culprit. Organizations often grapple with inconsistent standards, biased datasets, and the complexities of handling sensitive information. Transforming raw data into an “AI-ready” format requires significant effort and resources. As a result, many companies are exploring data treatment and management platforms, hoping to leverage emerging technologies to streamline the data preparation process.

Successfully feeding AI models requires careful data curation and real-time processing. The choices made during this preparation phase profoundly impact the opportunity, risk, and overall cost associated with each AI platform.