Less Is More? Not for Enterprise AI
The idea that ‘less is more’ has been passed down through generations as a useful piece of wisdom. You can apply it to almost everything in life and, for the most part, it makes perfect sense. Reducing what is unnecessary can bring greater clarity and focus.
Where this idea falls down completely, however, is in the context of enterprise AI. Here, organizations are looking for ways to leverage more of the information they hold, not less. The modern version should perhaps be changed to ‘more is more’.
The data estates organizations typically tap into for AI projects can contain billions of files and petabytes of information, much of it unstructured and distributed across file systems, cloud platforms, archives and other repositories.
Successful enterprise AI depends on fulfilling three fundamental responsibilities across the unstructured data estate: Understand, Align, and Execute. These represent a continuous decision cycle because execution changes the environment, new data appears, existing data changes, and business requirements evolve.
Know What You Have and What It Means
In many cases, this is not really a problem; there is plenty of data to work with. But organizations cannot make informed decisions about data they do not understand. This is especially difficult when enormous volumes of unstructured data are spread across different repositories. Knowing that a file exists is very different from understanding it.
Proper visibility establishes that data exists, while understanding provides the context needed to decide how to treat it. This can include its location, ownership, age, sensitivity and relevance to an AI initiative. The goal is a sufficiently complete and reliable view of enterprise data to support informed decision-making.
Aligning Data with AI Intent
But understanding alone does not create business value. Organizations must determine what should happen to their data. This is the role of Align, and it is where business intent enters the operating model.
The appropriate treatment of a dataset depends on what the organization is trying to achieve. For enterprise data preparation, the question may be whether the data is appropriate for a downstream AI initiative. The underlying decision process is the same: aligning data treatment with a business objective. The desired outcome is Business Alignment, in which data is intentionally managed in line with business priorities.
Turning Decisions into Action
A decision that cannot be implemented creates no operational value. Once an organization understands its data and determines what should happen to it, those decisions must be translated into action across the entire data estate.
For an AI initiative, execution may involve preparing and mobilizing selected data for downstream systems. It must be repeatable and capable of operating continuously as the data estate changes. The desired outcome is Continuous Execution: business decisions about data are reliably translated into operational action across the enterprise.
Generative AI has dramatically increased the urgency around enterprise unstructured data, but it has not changed the fundamental operating requirement. Organizations preparing data for AI still need to understand what data they possess, determinewhich data is appropriate for the intended use, and execute the actions required to prepare and mobilize it.
Datadobi supports this continuous process by helping organizations gain the insight needed to make informed decisions about their unstructured data and act on those decisions across the enterprise.