Tag: Operational Blindness

Why I Started Calling It Operational Blindness

“There has to be a better way to describe what I’ve been seeing.”

For years, that thought kept returning whenever I met customers. It followed me into boardrooms, factories, utility plants, government agencies, and Smart City command centres. Every meeting seemed different on the surface, yet they all ended with a similar feeling that something important was missing.

At first, I believed the problem was technology. I thought organisations simply needed more sensors, better connectivity, or a more capable IoT platform. Later, as artificial intelligence became the latest trend, I wondered if AI would finally solve the problem that had frustrated me for years.

The more I observed, the more I realised I had been asking the wrong question. The problem was never about having more technology. It was about why organisations continued making poor operational decisions despite having so much technology around them.

The Promise That Didn’t Match Reality

When I co founded Favoriot, I genuinely believed that if organisations could connect their devices and continuously collect operational data, better decisions would naturally follow. It sounded logical because connected systems should produce better visibility, and better visibility should improve operations.

That belief shaped much of our early thinking. We focused on connecting sensors, collecting data, building dashboards, and making information available in real time. Technically, everything worked exactly as we intended.

Then I started spending more time with customers.

Everything Looked Digital

I visited factories with sophisticated production monitoring systems. I walked into utility control rooms filled with SCADA screens and large video walls. I met building operators who proudly demonstrated their Building Management Systems, while Smart City command centres showcased dashboards that displayed hundreds of live data feeds.

Everything looked impressive. From the outside, these organisations appeared highly digital, highly connected, and fully in control. If someone judged only by the technology they saw, they would probably conclude that these organisations had already solved their operational challenges.

Yet the conversations told a completely different story.

The Same Answers Everywhere

Whenever I asked simple operational questions, I kept hearing remarkably similar responses.

“We’re still investigating the root cause.”

“Nobody realised the equipment had been deteriorating.”

“The maintenance team wasn’t informed in time.”

“Operations thought Engineering was handling it.”

“We have the data, but we’ll need time to retrieve it.”

These weren’t isolated incidents. They appeared in manufacturing, water utilities, agriculture, energy, healthcare, environmental monitoring, and Smart City projects. Different industries were using different technologies, but they were all struggling with surprisingly similar operational problems.

Technology Wasn’t the Missing Piece

As an engineer, I naturally tried to solve the problem by looking at the technology. Perhaps organisations needed more sensors. Perhaps they needed more dashboards or more sophisticated analytics. When AI became widely available, I even wondered whether intelligent algorithms would finally close the gap.

But the gap remained.

The more technology organisations deployed, the more puzzled I became. Multi million ringgit projects still depended on phone calls to verify incidents. Teams still relied on WhatsApp groups during operational emergencies. Reports still arrived after problems had already caused financial losses, and managers continued making decisions using fragmented information collected from disconnected systems.

The Frustration Started Growing

There were many evenings when I drove home replaying customer conversations in my head. Why does this keep happening? Why are organisations still surprised by problems they should have detected much earlier? Why do sophisticated dashboards still fail to prevent operational failures?

Those questions stayed with me because I couldn’t find a satisfying answer. Every new customer seemed to reinforce the same pattern instead of challenging it. It became increasingly difficult to believe that another dashboard or another AI model would somehow solve the underlying issue.

Slowly, I realised that I wasn’t looking at a technology problem. I was looking at something much deeper.

It Was Never About Data

One of the biggest turning points came when I realised organisations were not suffering because they lacked data. In fact, many had more operational data than they knew how to use. Sensors, PLCs, SCADA systems, ERP platforms, and IoT solutions were already generating enormous amounts of information every second.

The problem was that data does not automatically create understanding. Thousands of sensor readings cannot explain which issue deserves immediate attention. Beautiful dashboards cannot tell people which decision will prevent tomorrow’s failure. Artificial intelligence cannot produce reliable recommendations when operational information is incomplete, delayed, or disconnected from business context.

What organisations lacked was not data.

They lacked operational visibility.

Finding the Right Words

The phrase didn’t appear during a conference or while preparing a presentation. It arrived during one of those quiet moments when years of observations finally began connecting together.

This isn’t a data problem.

This isn’t an IoT problem.

This isn’t even an AI problem.

People are making decisions without seeing the complete operational reality.

Almost immediately another thought came to mind.

They’re operationally blind.

Operational Blindness.

The moment I spoke those words, everything suddenly made sense.

Suddenly the Pattern Was Obvious

Operational Blindness explained why organisations reacted instead of anticipated. It explained why departments worked in isolation despite sharing the same objectives. It explained why dashboards could display everything in green while operational performance quietly deteriorated underneath.

I began seeing Operational Blindness everywhere. A leaking pipeline that remained unnoticed until millions of litres of treated water had been lost. A machine whose vibration gradually increased until production stopped unexpectedly. A hospital where maintenance records were scattered across disconnected systems. A commercial building where energy costs quietly increased month after month because nobody recognised abnormal patterns early enough.

Different industries. Different technologies.

Exactly the same operational condition.

It Changed How I Saw Favoriot

That realisation also changed how I described our own company.

For years, I introduced Favoriot as an IoT platform because that was technically correct. Over time, I realised customers were rarely looking for another platform. They were looking for a way to stop recurring operational surprises, reduce uncertainty, and gain confidence in their daily decisions.

Customers don’t buy dashboards simply because they look attractive. They invest because they want fewer failures, lower operating costs, better compliance, improved reliability, and stronger business outcomes. The platform is only one part of that journey.

A Bigger Mission

Today, I no longer see Favoriot simply as a company that connects devices. I see it as helping organisations reduce Operational Blindness by connecting fragmented operational data, providing meaningful context, supporting faster decisions, and enabling coordinated actions across different teams.

That mission extends beyond IoT. It applies equally to manufacturing, utilities, healthcare, agriculture, logistics, smart buildings, critical infrastructure, ESG reporting, and even cybersecurity. Wherever operational decisions depend on trusted information, Operational Blindness can exist.

As artificial intelligence becomes increasingly capable, this challenge becomes even more significant. AI cannot compensate for missing operational context, disconnected information, or delayed data. Before organisations can become AI driven, they must first become operationally aware.

More Than Just a New Phrase

Looking back, I sometimes smile at how long it took me to recognise what had been sitting in front of me all along. I thought I was building an IoT platform, but what I was really trying to solve was a much larger operational problem that existed across almost every industry.

The phrase Operational Blindness represents years of customer conversations, personal frustrations, lessons learned, and moments of clarity. It gave me a way to describe a problem that many organisations experience every day but struggle to explain.

Perhaps the most meaningful breakthroughs don’t always come from inventing new technologies. Sometimes they come from finding the right words to describe a challenge that everyone has experienced but nobody has been able to name.

Have you ever experienced Operational Blindness in your own organisation? I would love to hear your story because every experience helps us understand this challenge better, and every lesson brings us one step closer to building organisations that can truly connect, see, and act.

The Moment I Realised Something Was Broken Why IoT Projects Fail to Scale, and What Nobody Wants to Admit

A few years ago, I walked into a facility that had spent close to a million ringgit on an IoT deployment. Sensors were installed. A dashboard was running. The operations manager proudly pulled up a screen showing hundreds of data points flowing in, live, in real time.

I asked him one question: “What decision did you make yesterday based on this data?”

He paused. He looked at the screen. He called a colleague over. A few minutes passed.

“Let me check the system,” he finally said.

That pause told me everything. The IoT project was not broken in the way most people imagine broken things to be. The sensors worked. The connectivity was stable. The platform was live. But something deeper had failed, and it had failed quietly, in a way that nobody in the room had named yet.

That was the moment I realised the industry had a serious problem. Not a technology problem. A thinking problem.

We Built the Infrastructure. We Forgot the Purpose.

When I look back at two decades of IoT work, across MIMOS, through my time at REDtone IoT, and now building FAVORIOT, the pattern I keep seeing is the same one. Organisations invest in sensors, platforms, and dashboards. They point to these things as proof of digital transformation. And then they wait for the results to come.

The results rarely come. Not because the technology failed, but because nobody asked the hard question before deployment: what decision are we trying to make faster?

The sensor was never the goal. The data was never the goal. The goal was always a better decision, made quicker, based on something real. But somehow, between the vendor pitch and the project sign-off and the go-live celebration, that goal got buried under the excitement of the technology itself.

I have seen this across manufacturing plants, utility companies, logistics operations, and smart city deployments. The pattern is consistent. The language around it is always optimistic. “We now have full visibility.” “We have a live dashboard.” “We are data-driven now.”

But ask the operations team what decision they made differently last Tuesday because of that dashboard, and the room goes quiet.

I Started Calling It Operational Blindness

Recently, I wrote a more technical definition of this condition for IoT World. I called it Operational Blindness. The full definition is this: it is the condition where an organisation invests in IoT infrastructure, collects operational data at scale, yet still cannot make confident, timely decisions because the data never closes the loop to action.

The organisation sees numbers. It does not see clearly.

This is not a criticism of the people involved. The engineers built what they were asked to build. The analysts produced their reports. The executives attended the strategy sessions. Everyone did their job. But the system, as a whole, was never designed to produce decisions. It was designed to produce data. And data without a decision is just noise dressed up in a dashboard.

When I started naming this condition clearly, something interesting happened. People began recognising it in their own organisations. System integrators told me they had seen this in nearly every project they had ever delivered. Operations managers said they had felt it for years but had no language for it. That recognition mattered to me, because you cannot fix what you refuse to name.

Why IoT Projects Stop Scaling

The question I get asked most often is: why do IoT pilots succeed but never scale?

The answer, in my experience, comes back to the same broken loop every time.

A pilot project works because it is small, focused, and closely watched. Someone with authority and curiosity is paying attention. Decisions get made. Results are visible. Everyone celebrates and writes a press release.

Then comes the scale-up. More sensors, more sites, more data, more dashboards. And suddenly the thing that made the pilot work, which was a human being with a clear question and the authority to act on the answer, gets diluted across a larger organisation with competing priorities, legacy processes, and departments that were never part of the original pilot conversation.

The data multiplies. The decision-making does not.

By the time the system is running at scale, there are alerts nobody reads, dashboards nobody opens, and reports that go straight from the inbox to the archive folder. The operations team, who were not involved in designing the IoT system, have learned to work around it rather than with it. They trust their own experience more than a dashboard they did not ask for.

This is not stubbornness. This is rational behaviour when you design a system without designing it around the people who are supposed to use it.

The Three Things That Actually Need to Change

After watching this pattern repeat itself across too many projects, I have settled on three things that genuinely move the needle.

The first is contextualisation. Raw data is not insight. A temperature reading means nothing unless it is translated into operational language. Not “82 degrees Celsius.” But “Motor 4B has been running above its safe operating threshold for 40 minutes and failure is likely within six hours.” Context converts measurement into meaning. Without it, the dashboard is just wallpaper.

The second is integration into workflow. The insight has to reach the right person through the channel they already use, at the moment they can act on it. Not a report that lands at 9am about something that happened at 2am. Not a dashboard on a screen in a control room that the relevant decision-maker never visits. The signal has to travel to where the decision actually gets made.

The third is closing the loop. The system needs to know whether action was taken, what action, and what the outcome was. Without that feedback loop, the organisation cannot learn from its data. It cannot improve. It cannot measure whether the IoT investment is working. The loop has to close: from sensor to decision to outcome and back.

These are not exotic ideas. But they are consistently absent in organisations that are spending heavily on IoT and wondering why the ROI never materialises.

What I Tell Every Organisation That Comes to Me

When organisations approach FAVORIOT, one of the first things I ask is: what decision do you want to make better, and who in your organisation makes that decision today?

If they cannot answer that question, the deployment is not ready. Not because the technology is not ready. The technology is almost always ready. But the organisation is not yet clear on what it is trying to do with what the technology reveals.

That clarity is everything. It is the difference between a sensor deployment that scales and one that stays a pilot forever. It is the difference between a dashboard that changes operations and one that gets opened once a month during the management review.

We built FAVORIOT precisely because we kept seeing this gap in the market. Not a platform gap. A gap between what the data says and what the organisation decides to do about it. That is the gap we are working to close.

The Real Failure Mode

The IoT industry has spent years convincing organisations that more sensors, more data, and more dashboards are the path to operational excellence. They are not. They are the beginning of the path. What lies between that beginning and actual operational excellence is the hard work of turning data into decisions, and decisions into habits, and habits into results.

Most IoT projects fail to scale not because the technology lets them down, but because nobody built a bridge between the technology and the way the organisation actually makes decisions. The bridge is not a software feature. It is an organisational design question. And it is one that the IoT industry has, for too long, left for somebody else to answer.

If you are running an IoT deployment right now and it is not producing the results you expected, I would encourage you to ask one question before you spend another ringgit on more sensors or a better platform. Ask: what decision am I trying to make, and is my current system designed to help me make it?

The answer to that question will tell you more than any dashboard ever will.

I wrote a deeper technical take on this over at IoT World