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What Your SCADA System Isn't Telling You

What Your SCADA System Isn't Telling You

Your SCADA system gathers overwhelming quantities of data from your equipment. However, are you able to make meaningful decisions from this data?

Your SCADA system is growing in complexity and capabilities, producing more data than ever before. However, we have reached a point where the issue is no longer an absence of data. On the contrary, there might be too much for the operators to interpret. What can you do about that?


Human history has always been punctuated by incremental advancements in technology. The pre-history of our species is even named by the fact that we learned to make tools of stone, bronze, and iron to ease our lives. Today, the landscape is a bit different but we continue to rely on constant technological improvements to face challenges. We’re no longer fighting mammoths and glaciers. Instead, we’re at a stage where no stone can be left unturned in our search for ways to reduce pollution, better utilize scarce resources, and ensure safe and reliable operation of our equipment.

Even though our tools are no more than a means to an end, we still use the development of science and technology to put labels on epochs in human history. In the last two hundred years, we’ve concluded not only one but three industrial revolutions. The product of this are the modern SCADA systems, an impressive symbiosis of computers, programmable logic controllers (PLCs), sensors and actuators. They’re used to control everything from semi-submersible drilling rigs to the production of pharmaceuticals and microelectronics. Modern automation systems enable us to produce faster, safer and more efficiently than ever before. We’re at a stage where such systems with flawless manners turn measurements and inputs into controlled outputs. They monitor and control thousands of states and, in doing so, produce huge quantities of data.


What your SCADA can’t do, though, is apply intuition to these enormous data sets. The expanding scope of industrial process control might actually have made it harder for the operators to single out information that matters. We’ve reached a stage where the problem is not a lack of data. On the contrary, there might be too much data for the operator to efficiently interpret. This means you risk missing both the subtle and explicit hints your system is desperately trying to give about sub-optimal configurations and possible failures.

More data isn’t necessarily good; it's not inherently valuable. Until we refine it into useful information it's actually quite useless. There are simply a lot of barriers to efficient communication between automation systems and human operators. The data exists, but it’ll only cause confusion until we can translate it into comprehensible insight. Luckily, we’re now able to do that.


We’re at the dawn of the fourth industrial revolution, where industrial progress is defined by a new set of tools. Advancements in data science and edge computing technology allow us to dig further down into the vast amounts of data generated and reveal patterns invisible to the naked eye. Plants and systems all over the world can be connected. Data that was previously hidden in remote locations can be democratized to promote operational transparency.

Regardless of any advancements in digital tools, you’ll still need to rely on deep insight into your process and industry. Successful industrial digitalization efforts require large amounts of time and commitment. However, if you do this right, this grand blend of domain knowledge and digital tools will redefine the way you’re working. By mixing industry experience with skilled data scientists and solid infrastructure for connectivity and data gathering, you have a recipe that will differentiate the winning players in the traditional industries from those who’ll lag behind.