Data as a Tier 1 Asset

The one thing I have never settled in my mind is why data is so difficult to get right in companies -- it's certainly not for a lack of effort. Naturally, I have have progressed through the common theories, such as lack of a data leader, business ownership, capital investment, talent development, incentives, or supporting toolsets -- but there is something deeper.

I have always pondered this theory that the reason why it is so hard is because data is not a primary function at a company. Except for some rare exceptions, nobody goes to school saying that they are going to be a data leader when they graduate. For many, data is not something they think about, they assume it lacks the intellectual hook and career progression. They would rather invest time and energy in accounting, analytics, engineering, medicine, law, or finance. Anecdotally, I never met a database administrator or CIO who said that was their career choice, they just developed into those essential roles.

And because data is a by-product of a primary business activity, well intentioned advocates continually try to label it as oil, exhaust, water, or currency-- which just clouds and undermines its value further (Mohan et al., 2026). Naturally, this has changed some with the explosion of the transformer-based LLMs, as most businesses realize that even AI models suffer from the garbage in = garbage out axiom.

Yet, my intuition is that even with AI staring them right in the face, most organizations have not really thought deeply about how they can capitalize on AI leveraging their data. Unfortunately, many assume that AI will be able to clean or synthesize their data problems, so they kick the can down the road (Mohammed et al., 2026).

It is time to think about your data differently, as a first class asset, not as a byproduct of your operation. I would love to talk to you about your data -- please reach out!

JimSwallow@AccuDataNow.com

Sources:

Mohan, S. K., Bharathy, G., & Jalan, A. (2026). Enterprise data valuation—A targeted literature review. Journal of Economic Surveys, 40 (1), 73-92.

Mohammed, S., Budach, L., Feuerpfeil, M., Ihde, N., Nathansen, A., Noack, N., ... & Harmouch, H. (2025). The effects of data quality on machine learning performance on tabular data. Information Systems, 132, 102549.