For as long as I can remember the question organisations ask about their data is the same one.
How long do we have to keep it?
Not, what could it tell us? Not, what decisions could it improve? Not, what patterns are hiding inside it that we have never had the tools to surface?
Just, how long do we have to keep it before we can get rid of it?
I have been in this industry for nearly thirty years. I have watched how organisations think about their data change three times in that period, and each time the thinking moved forward without the assumption underneath it ever being examined.
In the early 2000s organisations printed everything and stored it physically. The archive was a filing system. The question was always about compliance, what the law required how long retention needed to be when destruction was permitted. Data existed to satisfy a legal obligation. Nothing more.
In the 2010s the language changed. Scan to process not to archive. Electronic document management. Invoice capture. The focus shifted to operational efficiency, digitise what you need to use now move paper out of the process. Better. But the underlying assumption did not change. Data was still a liability. The goal was still to manage it as cheaply as possible until it could be disposed of.
Now AI has arrived. And suddenly that assumption, held quietly unchallenged for thirty years, has become the most expensive mistake in the building.
Here is what I have learned from watching organisations try to deploy AI.
They understand that AI needs data. What they do not understand is how much data in what state and from what depth of history AI needs to make decisions that can actually be trusted.
They think AI has the answers already. They believe they are buying intelligence. What they are actually buying is a very sophisticated engine that will perform in direct proportion to the quality and volume of the knowledge base it draws from.
AI does not run on megabytes. It runs on terabytes. It does not learn from this year’s data. It learns from every year’s data, the patterns that only emerge across decades of decisions outcomes and accumulated organisational experience.
The archive that has been sitting in a warehouse or in a document management system or in a pile of scanned image files that nobody can search, that archive is not a cost centre. It is the knowledge base the AI needs. And the organisations treating it as a liability are building their AI on a foundation that does not exist.
I was recently in a room with a contracts manager. We showed him what we are building, a data intelligence solution that reads documents extracts the information inside them structures it and makes it available to the systems and the people who need it.
He went quiet.
I have been in enough of these conversations to know what that silence means. It means someone has just seen a solution to a problem they have been carrying for a long time. But I did not know which problem. I just waited.
When he spoke he talked about contracts. Contracts from five years ago ten years ago twenty years ago. Contracts across multiple territories. The time he spends finding them reading them comparing them looking for vulnerabilities identifying overlapping obligations evaluating exposure across different jurisdictions.
Not because he is inefficient. Because that is what managing a large complex historically accumulated contract portfolio currently requires, when the contracts are stored in formats that cannot be searched compared or analysed at scale.
He said it would pay for itself just on the time saving alone.
Then he said the additional work he could do if that time were freed up was mind blowing.
I believed him. Because I had not thought of his problem when we built the solution. He found it himself.
That is what happens when you build something that genuinely unlocks intelligence. The people who see it find problems in it that the people who built it had not imagined.
The question organisations have been asking about their data for thirty years is the wrong question.
Not because keeping data for legal reasons is wrong. That is necessary and it always will be.
But because it is the minimum. The floor. The baseline obligation that stops organisations from doing the one thing that has become with the arrival of AI more commercially important than almost anything else they could do with their time and their budget.
Build the knowledge base.
Not buy the AI system. Build the knowledge base that the AI system needs to make decisions you can trust rather than confident guesses dressed up in the language of intelligence.
Organisations are forward thinkers. They want the next idea the next capability the next competitive advantage. That instinct is right. But forward thinking without connected thinking produces AI investments in one department data quality programmes in another department and archive management sitting in a facilities budget that nobody senior has looked at since it was set up.
The knowledge base lives in all three places simultaneously. And until someone connects them, until the archive becomes the foundation for the AI rather than the cost centre nobody wants to own, the AI will keep making expensive confident boardroom, approved guesses.
We have spent nearly thirty years helping organisations manage their data. We have watched the question change from how long do we keep it to how do we process it faster to how do we make it useful.
We are now building something that answers the third question properly.
Not because we planned this journey from the beginning. Because we were in the room when each version of the question was being asked, and we saw each time what the next question was going to be before most people had finished answering the current one.
The question organisations have been asking about their data for thirty years has just become the most expensive question they have never properly answered. We think we know what the right question is. And we are building the answer.
Dajon’s Data Intelligence Solution is in development. We are looking for the right conversations with organisations who are asking the right questions. Get in touch below.