It’s hard to believe now, but there was a time when people only collected data when absolutely necessary. The stereotypical images of 1970s offices, with rows of files and filing cabinets, testified to a very different attitude towards data. You kept what you absolutely and positively knew you should be referring to – and nothing else.
At the time, anything beyond a company’s core data was considered corporate waste. Data was a byproduct, not a resource. This has largely been driven by technology. Even when we moved from paper to online, digital storage was slow, expensive, and difficult to extract and analyze. Even if data was saved, it was often seen as write-only, saved but never accessed. Data was a liability: expensive to store and even potentially dangerous.
However, as technology has evolved and analytical techniques have developed, things have changed. Over the past two decades there has been a continuous shift in how we view the data we generate and collect. From a corporate waste product, it has rapidly evolved into a key marketing and business resource: the new oil, as we are often told.
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How data became the center of marketing
This shift has forced companies to reconsider what data they collect and why. Even if you didn’t know how to use it, the imperative became to archive all data, even the smallest transactional data. Data management technologies and techniques evolved such that lakes, pools and oceans of data arose and all data was now clean and available for analysis. In theory, at least.
As we have developed our data science and analytics capabilities, we have moved from being descriptive (“What did the customer buy?”) to predictive (“What is he or she likely to buy next?”). This type of information is extremely valuable to a business, allowing us to evolve our offerings and operations to respond to consumer demands and optimize performance.
But there was still another step to take: moving from predictive to prescriptive. This step goes beyond saying what the customer is likely to do next and instead says what we should do next. Systems began to arise that gave us the next best action: what we should actually do. In most cases, this was relatively limited in scope (e.g. what offer to offer next or what discount to apply), but nevertheless it provided us with an effective way to adapt to ever-changing customer and market demands. All based on the data we are collecting.
All of the above is based on the fact that we treat data as a resource to be returned to. The purpose of more advanced analytics, whether descriptive, predictive or prescriptive, is to give us a better view of the data we have and what that means for our business.
Why AI models change the role of data
We now see ourselves facing another major technological shift, as LLMs and other AI-related technologies fundamentally change the way we work. It may be tempting to view these new approaches and technologies as simply better ways to work with the data we have – and in some ways they are. However, if you take a step back and ask what role data plays in these technologies, you’ll see that it’s about something much more radical than just cool new tools.
To understand this we need to look a little under the hood. Most modern LLMs are built on an architecture called transformers. They take your text input and process it using billions of parameters (mathematical rules) learned from a massive initial diet of data. The way they store this knowledge can be simplistically compared to file compression.
The text “What is the capital of France?” successfully generates “Paris” not because the model has a search engine within it, but because its parameters effectively act as a compressed, lossy recall of the entire original training set. While imperfect, this analogy is useful. As science fiction author Ted Chiang said, an LLM is like a “Blurred JPEG of the web.”
The implication is that once a model has been trained, it contains all the knowledge it will retain (at varying levels of fidelity). When we use a model, we don’t go to the source, but to an imperfect snapshot of it. If you think about the fuzzy JPEG analogy, our challenge is to integrate the model with a crystal-clear, high-definition image of our business, which comes from our proprietary data.
Because the breadth of current underlying models is now so deep, they are excellent at the prescriptive part of the workflow, not just analyzing but telling what we will do next. Together with your data asset, you now have the ability we’ve been working towards: moving directly from data to action.
What this change means for your data strategy
One technology that helps drive this shift in how we use data is the Model Context Protocol (MCP) – a standardized way to expose our proprietary data to models – effectively becoming the universal adapter that allows models to read your live database without swallowing it permanently in their fuzzy memory. MCP is still in its infancy and probably won’t represent the definitive form of interaction between data and models, but it shows how necessary it is becoming to rethink the role of our data assets.
This means we now need to rethink the role of our data. If the primary purpose of our data is to train or integrate a model, does that change what we collect and when? Does it change its value and its role in our marketing and business landscape?
The challenge today for anyone who collects business data, and surely it’s all of us, is how to change our thinking to recognize that data is no longer the central asset? Companies that fundamentally rethink the role of their data assets will thrive in this new ecosystem.
Key points
- Data has transformed from a stored asset to something that powers and shapes AI-driven decisions.
- The evolution from descriptive to predictive to prescriptive analytics laid the foundation for today’s AI workflows.
- Large language models do not retrieve data in real time, they rely on compressed knowledge that must be integrated with proprietary data.
- The real advantage now comes from combining basic models with high-quality business-specific data.
- Marketers need to rethink data strategy, from collecting everything to making data actionable for real-time models and decisions
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