The overloading data paradox or the era of measuring everything, but understanding nothing
We live in a world obsessed with data. But instead of making us smarter, itβs making us slower, more reactive, and increasingly disconnected from meaning.
This is not just a technical problem. Itβs a strategic one. And itβs shaping the way we build, manage and understand brands.
Letβs talk about it.
Too much input, too little processing
Iβm part of 22 WhatsApp groups. Between clients, projects, marketing teams, university work, family and friends. Only a few are personal. Every day, dozens of notifications compete for my attention.
I manage four active email accounts. All of them filled with trend reports, newsletters, whitepapers, industry alerts, academic references, requests, presentations, invitations. Most of them are valuable. Almost none get read in real time. So I save them. I tag them. I open tabs. I send myself links, set alarm clocks to read them later, I try to keep up, but thereβs no such thing.
I use Otter to transcribe conferences and talks. I take notes obsessively. I collect content for later, because I know I wonβt process everything now. I store PDFs. I hear podcasts while exercising at the gym. I screenshot interesting charts. I highlight paragraphs like Iβm building my own textbook.
On top of that, I have hundreds of pages of academic readings for my Masterβs in Data Driven Marketing. Some mandatory, some recommended by professors, others that I chose because I genuinely want to understand more. Iβm not even talking about fiction or leisure, I mean technical, heavy literature that requires time and mental space.
And still, I keep collecting. More insights. More resources. More data.
Some days, it feels like Iβm building a library Iβll never read.
Being data driven doesnβt mean being data reactive
Thereβs a fine line between being data informed and being data obsessed. Right now, weβre on the wrong side of it. In data science, this is known as the curse of dimensionality, and its most dangerous symptom is overfitting. As the number of dimensions increases, data tends to become sparse, and the computational complexity of algorithms grows exponentially. This makes it harder to find patterns, train models, and interpret results.
When a model is exposed to too many variables, it stops learning general patterns and starts memorising irrelevant noise. It gets distracted by details that donβt matter. It picks up signals that wonβt repeat.
The result is a model that performs beautifully on paper, but fails in reality.
Thatβs overfitting: the illusion of intelligence with no real predictive value.
And in marketing, weβre seeing the same. Too many indicators. Too many filters. Too many conflicting metrics telling us what we want to hear, not what we need to know.
Because what weβre doing is building campaigns with endless performance layers, tracking KPIs for everything that moves, and filling dashboards with graphs, funnels, and segmentation trees. We measure brand awareness, consideration, sentiment, purchase intent, dwell time, bounce rates, conversions, referrals.
We track everything.
But when someone asks the most basic question β βis the brand actually growing?β β we hesitate.
We donβt know. Not really. And thatβs the problem.
Because there are too many angles. Too many KPIs. Too many dashboards telling different stories depending on what you want to believe.
Data doesnβt speak on its own. It responds to what you ask. Predictions are choices. Interpretation is judgement. And even before that, we already made decisions, about what data to collect, how to clean it, how to handle outliers, misisng values, what to ignore, and what to highlight.
Every dataset is a filtered version of reality. Every insight is the result of a framing. We think weβre looking at truth, b ut what weβre really seeingβ¦ is what weβve chosen to see.
When more data becomes less clarity
Weβre not lacking information. Weβre drowning in it.
What weβre missing is the ability to choose. To synthesise. To say no. To stop measuring everything just because we can.
In data science, we have methods to solve this. Feature selection cuts out irrelevant variables. PCA (Principal Components Analysis) condenses multiple metrics into one meaningful component. Clustering groups behaviours to reduce noise.
We need the same in marketing. Simpler models. Fewer KPIs. Clearer indicators. Less obsession with dashboards. More focus on context and decisions.
The smartest strategy is rarely the most complex. Itβs the most intentional.
Creativity is also a process of subtraction
I work in branding. I value insight. I love ideas. But the older I get in this field, the more I realise that cutting is just as powerful as creating. Less is really more.
Creativity doesnβt thrive in clutter. Strategy doesnβt grow in noise. Too many inputs block clarity. Too many metrics kill momentum.
The more dashboards we build, the harder it is to focus. The more we track, the less we act. The more we measure, the less we see.
Sometimes the most strategic thing you can do⦠is to choose and put apart. See what is best for your strategy and goals and eliminate the rest.
Data should support strategy, not replace it
Being data driven should never mean becoming data dependent. Itβs not about reacting to the latest number.
Itβs about using data as support, not as a crutch.
Weβve confused motion with progress. Reporting with clarity.
Information with insight.
And that confusion is costing us time, creativity, and impact.
Choose your stack, then go deep
Itβs exactly like whatβs happening now with AI. There are thousands of tools. Every day a new one launches. Platforms to generate images, videos, ideas, headlines, ads, code, workflows. Itβs overwhelming. But you donβt need them all. Youβre not supposed to.
What matters is choosing the ones that make sense for how you think, how you work, how you create, and sticking with them.
Because itβs not about testing everything. Itβs about going deep into what supports your process and discarding the rest without guilt.
Efficiency isnβt about quantity. Itβs about clarity. The same goes for data, and the same goes for strategy. And definitely for branding.
The strongest brands are not the ones who measure the most
But are the ones who measure what matters, because they filter noise, they act with intention, they choose wisely and then they stay sharp and focused.
Because in the end, the difference between information and insight is not how much you collect.
Itβs what you do with it.
