By Michel Spruijt, president, Brain Corp International
Here’s an uncomfortable truth: we’re drowning in data – and it’s making us dumber, not smarter.
Global data production is accelerating at a breathtaking pace, from 64 zettabytes (or 64 trillion gigabytes) in 2020 to an estimated 170 ZB by 2025.
To put that in perspective, that’s enough data to stream every movie ever made simultaneously on 50 billion devices.
Yet despite this information tsunami, most organizations find themselves in the paradoxical position of being data rich but insight poor.
This challenge becomes particularly visible in robotics deployments. Brain Corp powers the world’s largest fleet of autonomous robots operating in public spaces – over 40,000 units deployed across retail, healthcare, and logistics sectors.
These machines can capture millions of images daily, along with collecting terabytes of operational data from sensors, navigation systems, and environmental monitoring. The sheer volume is staggering, but volume without insight is just expensive noise.
The real question isn’t how much data we’re collecting, it’s what we’re actually doing with it that creates genuine impact.
More information, less intelligence
The robotics industry offers a unique lens into this problem because robotic systems generate data continuously.
A single autonomous mobile robot can produce detailed logs of its navigation patterns, task completion rates, environmental conditions, obstacle encounters, and system performance metrics, all in real-time, 24/7.
But large-scale deployments reveal a critical insight: having access to comprehensive data doesn’t automatically translate to better decision-making.
In fact, without the right infrastructure and strategy, it often has the opposite effect. Teams become paralyzed by choice, chasing metrics that don’t matter while missing the signals that do.
Take a typical retail deployment. Robots are scanning shelves, tracking inventory levels, identifying out-of-stock conditions, and monitoring floor cleanliness. Each robot generates thousands of data points per shift.
But without clear frameworks for converting data into decisions, and the right platform functionalities to process that data, leaders can end up overwhelmed with data, or stuck in what industry experts call “dashboard theatre” – impressive-looking displays that make everyone feel data-driven while actual performance remains unchanged.
Lessons from ‘the world’s largest robotic fleet’
Managing 40,000+ autonomous robots (which Brain Corp believes to be the world’s largest robotic fleet) across diverse environments reveals that the gap between data collection and meaningful action can only be resolved by having the right outlook.
In large-scale robotics deployments, the most valuable data insights are those that can be acted upon autonomously or semi-autonomously.
When robots detect unusual patterns, unexpected obstacles, changes in foot traffic, inventory discrepancies, advanced systems don’t wait for human analysis. They immediately adjust behaviour, alert relevant stakeholders, and incorporate the learning into future operations.
Advanced cloud-based platforms allow leaders to act on robotic data immediately. When a robot identifies an out-of-stock condition on a retail shelf, it doesn’t just log the observation – it can trigger an integrated workflow that alerts store associates, updates inventory systems, and adjusts future scanning priorities based on historical patterns. The data becomes operationally valuable within a short period of time.
This immediate action loop is what separates effective data utilisation from mere data accumulation. But creating these loops requires rethinking how organisations conceive their information architecture.
Traditional approaches often separate data collection, analysis, and action into distinct phases managed by different teams with different tools, lacking a unifying platform of any kind. By the time insights reach decision-makers, the operational context has often changed.
The real-world impact of operational intelligence
The difference between data collection and operational intelligence becomes clear when examining specific outcomes. In logistics operations, the intelligence extends beyond individual robot performance to fleet-wide optimisation.
When one robot discovers a more efficient route or identifies a recurring obstacle, that knowledge can propagate across an entire system. The compound effect of shared learning means that operational efficiency improves exponentially rather than linearly as fleet size grows.
Perhaps the most striking example comes from retail environments where robots have fundamentally changed how stores understand customer behaviour.
By analysing millions of daily inventory images, retailers can better predict out-of-stock conditions before they occur, optimize product placement based on actual shopping patterns, and allocate staff resources more effectively.
This intelligence only emerges when data flows freely between systems, made possible by the right architecture; from this point it can bring about timely actions.
Building systems that think, not just collect
The organisations with systems that can successfully sift through masses of data all share common characteristics. They’ve moved beyond thinking about data as something to be stored and analyzed toward treating it as fuel for immediate operational improvements.
This requires what robotics experts call “closed-loop intelligence” – systems where data collection, analysis, and action happen in integrated cycles rather than separate phases.
In practical terms, this means designing infrastructure where robots can make autonomous decisions, update their own behaviour based on new information, and contribute to collective fleet intelligence without human intervention for routine optimisations. Hardware and software, working in unison.
The technical architecture matters, but the organisational approach matters more. Successful deployments establish metrics that tie directly to business outcomes, and build feedback loops that continuously improve decision-making quality.
From data deluge to competitive advantage
The explosion in data volume is accelerating as more systems become connected and autonomous. Organisations that treat this as something to be passively managed will inevitably find themselves increasingly overwhelmed.
Those that treat it as a capability to be harnessed will almost certainly discover competitive advantages.
When managing tens of thousands of autonomous systems generating continuous data streams, organisations either figure out how to convert information into immediate operational value or they drown in a sea of data.
The lessons are transferable across industries: prioritise actionable insights, design systems that can act on data automatically, and create feedback loops that turn individual learnings into collective intelligence.
The data deluge isn’t making us dumber by default, but it will if organisations approach it with analogue-age thinking and don’t begin to consciously move away from building systems of information, to creating systems of intelligence.
About the author: Michel Spruijt is an experienced business leader with over 20 years in competitive environments, holding roles in sales, business development, operations, and customer care. He is known for driving growth, market share, and strategy, while building strong relationships with colleagues, clients, and partners. A proactive, structured thinker, Michel balances independent decision-making with teamwork and leadership.
