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The vocabulary generator has been working overtime. Just last week I talked about Data Contracts. Here’s another: Data Trust.
I predict that through 2028, 80% of S&P 1200 organizations will become curious about the meaning of Data Trust. Why? Because among The Gartner 100 Data, Analytics and AI Predictions Through 2031 was:
Through 2028, 80% of S&P 1200 organizations will relaunch a modern, data & analytics (D&A) governance program, based around a trust model.
That’s a pretty astonishing prediction. Let’s break it down.
Through 2028. Probably not coincidentally, that’s the same year by which Gartner expects both significant adoption of Generative AI and AI Agents as well as concomitant AI-related risks and mishaps. The relationship between Data Quality and AI Quality is already well known, but the support for Data Quality initiatives is still too often half-hearted. Within three years, companies will either be enjoying the benefits of their quality efforts, watching others benefit from their quality efforts, or recovering from some misfortune as a result of lacking quality efforts.
80% of S&P 1200 organizations. That’s a lot. The broadest of the predictions presented. I guess they can’t say “everyone,” but that’s pretty much everyone. This probably means YOU.
Will relaunch a modern, D&A governance program. Not launch. Relaunch. It’s an interesting choice of words. It’s not that there aren’t existing D&A governance programs. It’s not even that there haven’t been successes with existing D&A governance programs. To the contrary, there have been lots. It’s just that they’re not working as well as they could or should be working. I’ve talked about this before. What we’re doing is working just well enough to give management the illusion of sufficiency, but it will become clear that these efforts will not be sufficient to support AI. Hence, “relaunch.”
Based around a trust model. This part caught my attention. Since it seems that most of us are going to be doing it within the next couple of years, it would probably be useful to take a look at Trust Models in more detail.
A Trust Model is a framework for establishing confidence in a relationship.
It’s a very common approach used to diagnose and strengthen trust dynamics between leaders and their teams. It seems that every management consultant and consultancy has its own model. Examples include the Trust Equation, ABC Model, Nine Habits of Trust, 3 C’s of Trust, and many, many more. (Maybe it’s just the era when I grew up, but to me that list looks like the tracks on Side 1 of a Sesame Street album.) Generally speaking, they emphasize variations and combinations of competency, integrity, reliability, empathy, communication, and accountability.
Trust Models have also been applied to cryptography and computer security. Frameworks include Public Key Infrastructure and Zero Trust. Their purpose is to help protect sensitive data and systems from cyber threats. The idea is to never trust. Always verify. Deny access by default. Actually, these seem more like “lack of trust” models.
I talked about the Zero Trust concept several months ago, and applied it to data. A Zero Trust Data Content strategy assumes that in the absence of sufficient evidence to the contrary, data is always assumed to be incorrect.
Here, it is being applied more broadly to data and analytics, not just Data Content.
From a D&A perspective, Data Trust means having confidence in the accuracy, reliability, timeliness, and security of your data.
I’m not sure that qualifies as revolutionary. I’m not sure that it even requires new vocabulary. I guess it says something about the state of our art that it appears to be necessary. We’ve been kind of underwhelming when it comes to Data Governance and Data Quality. When it comes to Data Trust, maybe a reset is necessary.
At this point it can be considered common knowledge that the quality and success of AI initiatives strongly depend upon the quality of the data used to train the models. Most companies understand that. Understanding is a great first step, but what about the doing? That’s the hard part. That’s the part that requires that something different be done.
Just as there are many Trust Model frameworks for management coaching and for data protection, there are many Trust Model frameworks for data and analytics. It feels like this in so many areas right now. Every vendor (existing and new) is trying to get something out into the market as quickly as possible, hoping that it will become the standard (or at least be used enough to justify the investment in it).
The framework from decube.io is typical. The key to trust is integration and transparency, and it rests on four pillars:
- Metadata Management
- Data Governance
- Data Quality with Observability
- Data Mesh
Sound familiar? Looks a whole lot like Data Governance with a particular emphasis on Metadata Management and Data Quality (with Observability), plus Data Mesh. It also looks a whole lot like Data Products. After all, Data Mesh rests on a foundation of Data Products. But I’ll go one step farther:
Pursuing Data Products is the fastest, most reliable, and most practical way to increase Data Trust.
It doesn’t matter whether it’s part of a Data Mesh (or Data Fabric) architecture.
It shouldn’t be a surprise. What’s the “product” in Data Product? If you’ve been following along here the last eighteen months, say it together: “The ‘product’ in Data Product is reliability.” Reliability builds trust.
Data Trust is an outcome, not a program or initiative.
I suspect that Gartner was using the term in their prediction more as an umbrella that encompasses relaunched Data Governance efforts having components similar to the four listed above. Increased trust in the data is the measurable result.
Sometimes I think our vocabulary choices are more aspirational than functional.
We want to build Data Trust. We can take steps to build Data Trust. We can measure the level of trust in the data. We certainly don’t want to lose trust because when we do it’s really, really hard to get back. So, yes, Data Trust probably a useful metric. It’s sort of like Consumer Confidence.
So, if using a different wrapper helps to sell the concept to management, then fine. Activities under the heading of “Data Trust” are those things that you know you should be doing anyway. Don’t get distracted by the hype and the buzz, but instead use the hype and the buzz as drivers to do them.
