Compounding this, unstructured data is highly siloed and defined by ambiguous ownership. It is often created and managed by different departments or individuals within an organization, leading to data silos and ambiguity around data ownership and accountability. While structured data is more likely to have known ownership within an organization due to understood security or cost implications, a company’s unstructured data is often either sequestered for legitimate reasons (e.g., upcoming iraq whatsapp number data commentary for an acquisition) or for less desired causes (e.g., political boundaries between divisions).
Finally, the formats of unstructured data are varied. Whereas structured data has collapsed into a small set of universal standards, SQL being a principal one, unstructured content systems have a multitude of formats and legacy patterns. The tools needed to manage these formats in a unified way are unique and require a commitment from the organization to deploy and use them.
To properly manage unstructured data and effectively use it for GenAI projects, enterprises should focus on these seven essential areas:
Discover, catalog, and classify unstructured data: Automatically discover, catalog, and classify files and objects on the fly, which are essential for GenAI projects.
Preserve access entitlements of unstructured data: Maintain existing enterprise entitlements at source systems to ensure that only authorized users access relevant data via GenAI prompts.