Data is not a tech problem. It's a management problem

In 2024, the average enterprise spending on LLMs went from $7M to $18M.

Large and small companies are rushing to find ways to implement AI into their systems, with data as the common denominator.

What sets them apart is the quality of the data they possess.

Bad data comes from a lousy structure in which it is generated and circulated, leading to inaccuracies, poor definitions, under-utilization, redundancy, and accessibility problems.

A good manager addresses:

•⁠ ⁠Company Data Needs

•⁠ ⁠Communication Between Departments

•⁠ ⁠Eliminating root causes of errors and minimizing them

Companies can start using data for basic tasks by setting up a reliable information system. As they become more comfortable with simpler data techniques, they can gradually tackle more complex challenges.

Open-source LLMs are easily accessible to everyone, exhibiting only minor drawbacks compared to proprietary models.

With future models, they will likely be on par with proprietary models.

Could you let your company miss out on leveraging these innovations simply because data mastery wasn't achieved in time?

The quicker you address data quality challenges, the sooner your company can reap the benefits of AI's productivity.