Around the third quarter of last year, I started hearing the word “context” pop up in many of my conversations. It stood out because previously the only thing anyone wanted to talk about was data.
We all know how the vocabulary of the business world works. When everyone starts talking about something, we come up with another word to make it sound like we’re talking about something new (which is usually not the case).
So when I started hearing “context” in the second half of 2025, it seemed poised to take its place alongside synergy, pivot, and alignment in the Corporate Speak Hall of Fame.
But “synergy” and “alignment” are actually two different ways of saying the same thing, while “data” and “context” are not. In fact, data was a legitimate problem through 2025, and that was more important than overuse.
More data no longer meant more value.
This applies whether you are discussing AI or GTM strategy in a B2B organization.
What were the limits of the Big Data strategy?
The idea behind the era of Big Data was simple: record everything so we can learn even more about our customers. We came; we have captured; we built on-premise and later cloud-based storage environments to store everything; we have implemented nice analysis software; and we… discovered that we did indeed have a lot of data.
We also realized that data is just a record. It doesn’t tell you anything Why someone did something or How they did it. More importantly, it doesn’t provide much advice on what to do next.
If you were to take your data and train an LLM on it, you would simply be designing a parrot well versed in telling you things you already knew. Only by adding context about your brand, your customers or your GTM strategy will you create an AI-powered assistant.
How does context apply to GTM strategy?
Most B2B GTM strategies are inward-looking. They tell the team they need to reach $25 million in revenue. They discuss the five most important product features. They tell you that the CTO is your ICP.
What is missing from such a strategy is the context, specifically the external pressures that any organization faces. The result is that you end up with a mismatch. You’re trying to sell growth to a company that needs help reducing costs. Your sales pitch sounds deaf. You don’t gain trust.
(If you want to learn more about the context and external factors in GTM, I highly recommend spending time with the work of MarTech contributor Mark Stouse.)
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As context makes its way into discussion after discussion, you’re probably wondering how we’ll ever run without it.
The truth is we didn’t.
Context lived in the minds of valuable and experienced employees for years, and they liked it. But as organizations demanded speed and scale – and especially as they struggled to achieve them – it was no longer feasible for context to exist only in the minds of employees.
In a business world built on speed and scale, knowledge silos cause as much damage as data silos. (I want to acknowledge here my theory that data silos are simply a technical manifestation of organizational silos.)
Complexity also mixes poorly with the human-based context. Many companies strive to be part of what they see as a fast-moving global economy, too dispersed and too fast to rely on the gut feelings and experience of ordinary people.
Companies have addressed many of their speed, scale, and complexity challenges with automation. And why not? When you automate business processes, you move faster and achieve results faster. That’s right, but you also remove the context because automation leaves no room for human nuance.
That brings us to where we are now. We turn to technology in the hope that it can bring back the context that has sunk to the bottom of Big Data.
We can be cynical about this and say that we have wasted a lot of time and resources on Big Data and gotten little out of it. But I consider this a course correction. Yes, we’ve collected exabytes of information to find answers. In many cases it worked well.
But answers alone are not enough, and now, like transcendentalists, we are desperate for meaning.
#era #data #dominance #didnt #long #MarTech