Enterprises are going all in on generative AI (GenAI), with the technology driving a massive 8% increase in worldwide IT spending this year, according to Gartner. But just because businesses are investing in GenAI doesn’t mean they’re broadly implementing it in actual production. Organizations are eager to wield the power of GenAI. However, deploying it safely – getting data governance, quality, cybersecurity, privacy, and compliance right – is proving to be a cumbersome and surprising challenge.
There are a number of factors that make the iraq whatsapp number data safe use of genAI complicated. The biggest is genAI’s considerable reliance on unstructured data.
Unstructured Data Drives GenAI
There are two types of data: structured and unstructured. Structured data is any data that is organized in a traditional row-column database format or has a predefined data model, while unstructured data is all the other data that doesn’t exist in spreadsheets and databases. The latter is typically text-heavy and lacks the structural organization and properties of structured data. It’s also huge: Up to 90% of all enterprise data is unstructured.
GenAI mostly leverages unstructured data, such as the examples cited above. GenAI technologies employ this data to train and fine-tune models as well as to build enterprise AI search capabilities. This causes a problem for organizations as the vast majority of their data management solutions were built for structured data.