There's something fundamentally different about discovering an insight yourself versus having someone hand it to you on a dashboard. When a quality manager digs into batch data and discovers a pattern themselves, they don't just accept the finding, they trust it. They own it. And more importantly, they act on it.
This isn't just about data access. It's about the psychology of discovery and how curiosity-driven exploration creates a culture of continuous improvement that no amount of top-down reporting can replicate.
From Data Consumers to Data Discoverers
The situation: An operator notices they're throwing away more defective product during the morning shift than usual. They check the standard quality dashboard, overall efficiency shows 96%, well within targets. But their gut says something's wrong.
In many factories, that's where the investigation stops. The operator logs the observation, maybe mentions it during shift handover, and hopes someone with data access can look into it later. What could have been caught early becomes thousands in lost material.
This happens because most factory data systems treat people as consumers rather than discoverers. Someone identifies a need, requests a report from IT, receives and analyses it, and acts on the findings. This works well for routine reporting: standardized dashboards serve crucial purposes and get used every day. But when it's the only option available, organizations miss opportunities hiding in plain sight.
Even the most thoughtfully designed dashboards have limitations. Most IT teams build excellent standardized dashboards that serve 80% of daily needs effectively monitoring KPIs, tracking compliance, displaying critical alerts. These get real use and deliver real value.
The challenge comes with the remaining 20%, those moments when someone notices a pattern or needs to investigate something specific. A quality dashboard might show reject rates by product line, but what if the pattern relates to supplier batches? Or ambient humidity? Or startup sequences?
The solution isn't choosing between standardized dashboards and custom exploration. It's enabling both.
Back to that operator with the quality concern. With exploration capabilities, they could quickly correlate reject rates with material batch numbers, temperature, and startup times. Maybe they discover the pattern only happens with Monday deliveries, or during the first hour after startup, or when humidity exceeds certain levels.
That custom investigation becomes actionable insight, and might even inform the next version of the standardized dashboard.
When people can explore data themselves, they stop asking "Is this report accurate?" and start asking "What's really happening here?" The difference between trusting someone else's analysis and verifying your own hypothesis is the difference between accepting information and gaining knowledge.
When People Explore, They Discover
The magic happens when someone can follow their own curiosity and build exactly what they need for their specific situation. A plant manager notices efficiency varies between shifts and can immediately drill down to see why. A maintenance tech has a hunch about temperature patterns and can create a custom view to verify it without waiting for an engineering request. An operator spots recurring waste at a specific time each day and can build a dashboard that highlights exactly that pattern.
These aren't major strategic initiatives. They're small moments of curiosity that, when enabled, lead to small improvements. And small improvements compound.
We've seen operators create simple dashboards to track patterns they notice during their shifts, monitoring specific temperature combinations that predict quality issues, or tracking material usage in ways that reveal waste patterns. These custom views would never make it onto an official dashboard request because they're too specific, too focused on individual workflows. But they turn out to reveal significant optimization opportunities precisely because they reflect real operational needs.
A quality manager might spend an hour building a custom analysis view for batch variations and discover a correlation that saves thousands in rework costs. The key isn't just the insight, it's that they could create exactly the view they needed, when they needed it, without depending on someone else's interpretation of what would be useful.
The key insight: these discoveries happen because people can test their own hypotheses immediately, while the idea is fresh and the motivation is high, using exactly the data combination that makes sense for their specific situation.
The Compounding Effect of Curiosity
Individual discoveries matter, but the real impact comes from creating an environment where curiosity is routine and where people can immediately act on their ideas by building the views they need. When multiple people across a facility can independently explore data and create custom dashboards, several things happen:
Different perspectives create different tools. The dashboard a maintenance technician builds focuses on equipment health patterns that matter for their maintenance rounds. The view a quality engineer creates highlights batch-to-batch variations that help them spot emerging issues. The production supervisor's dashboard tracks throughput and changeover times that affect daily scheduling decisions. Each serves a specific need that a generic, corporate-designed dashboard couldn't address.
Small findings accumulate into significant impact. A 2% reduction in energy waste discovered through a custom energy monitoring view, a 1% improvement in yield spotted through a personalized batch analysis dashboard, eliminating a recurring quality issue identified through an operator-built trend tracker these individually minor improvements can add up to substantial business impact over time. This was also the case for one of our customers, Jacques IJs (view the case here). They've managed to reduce overfill by 15-20% which has has allowed them to increase their production volume without buying new equipment. You can read-up on their case here:
Knowledge sharing accelerates. When someone creates a useful dashboard or discovers an interesting pattern, they naturally want to share it with colleagues. This creates informal knowledge networks that spread insights faster than formal reporting structures. Teams start copying and adapting each other's dashboard designs, creating organic improvement communities.
Most importantly, it builds a culture where data-driven decision-making feels natural rather than imposed. People start asking data questions because they can answer them immediately by building exactly the view they need, not because they're required to.
The Cost of Flying Blind
Companies that don't enable self-service data exploration and dashboard creation pay a hidden but substantial cost. Not in system failures or compliance issues, but in missed opportunities that never get identified and underutilized teams who stop engaging with data altogether. - Jeroen Coussement - CEO - Factry
How many process improvements go undiscovered because the people who would notice them can't easily access the data to verify their hunches or build the specific views they need? How many quality issues persist longer than necessary because investigating them requires formal IT requests for custom reports? How many efficiency gains remain hidden because the people closest to the process can't create the monitoring tools that would reveal optimization opportunities?
Perhaps most critically, how many production teams simply ignore their dashboards because those displays don't serve their actual daily needs? When dashboards become irrelevant, teams lose engagement with the data it represents. They stop noticing patterns, stop asking questions, and stop thinking about how information could improve their work.
These aren't catastrophic failures, production continues, products ship, compliance requirements get met. But the cumulative effect of missed small improvements and disengaged teams represents a significant competitive disadvantage over time.
Building Trust Through Access and User Control
In conclusion, in many cases the solution isn't just better dashboards or more detailed reports. It's giving people intuitive tools to satisfy their own curiosity and build exactly the views they need for their work. When people can follow their hunches immediately, when they can verify patterns they notice, when they can create exactly the monitoring they need for their daily tasks… that's when data stops being someone else's responsibility and becomes a natural part of how people work.
User-friendliness determines everything. If building a custom view requires technical skills or takes hours to configure, only specialists will bother. But when someone can create a useful dashboard in minutes, exploration becomes routine rather than a special project. The most sophisticated platform becomes useless if people can't quickly find what they need or if the interface fights against how they think about their processes.
The real transformation isn't technical, it's cultural. People who create their own analytical tools become advocates for data-driven decision-making in ways that no training program can achieve. They start sharing discoveries, building on each other's insights, and naturally driving continuous improvement from the ground up. That shift - from accepting pre-built information to actively creating personalized insights and sharing them with you peers - might be the most valuable outcome of all.
Every factory's data journey looks different. What's your experience with giving teams direct access to explore production data and build their own dashboards? Have you seen examples where self-service tools led to unexpected discoveries or better user adoption?
