Enter the Industry 4.0 Era Today by Using “Dark Data” You Already Have

by Gregory Gimpel and Allan Alter

The Industrial Internet-of-Things (IoT), also known as Industry 4.0, provides new opportunities to harness data-driven insights to improve business performance. In reality, operational technology, production equipment, and other “things” already provide data that can improve – even transform – company operations. By answering four key questions, business leaders can benefit from this “dark data” today.

 

Enter the Industry 4.0 Era Today by Using “Dark Data” You Already Have

The Industrial Internet of Things (IoT), also known as Industry 4.0, provides a new opportunity to improve business performance through data-driven insights. A quick Internet search provides many details about the transformative potential of the IoT. Wireless sensors on patients will help hospitals to predict heart attacks before they happen, enabling preventative emergency measures.1 Companies will equip employees with uniforms that detect hazards as a way to cut down on industrial accidents.2 Sensors in roadways, sidewalks, and mass transit systems will manage traffic flow in real time, minimising traffic and the lost productivity and environmental cost associated with it.3 IoT-enabled supply chains will become adaptive, self-organising, and super-efficient networks.4

Building the new IoT infrastructure will take years and many companies that can benefit from the IoT are not prepared to use the technology.

Building the new IoT infrastructure will take years and many companies that can benefit from the IoT are not prepared to use the technology.5 Fortunately, companies can enter the IoT era today by using the “dark data” created by supply chain operations, production equipment, operational technology, and other existing “things”. Presently, over 90% of these data are dark, providing a rich opportunity for companies to begin exploiting these information resources today. 

Our interviews with senior executives suggest that companies can begin to take advantage of the opportunities enabled by dark data by asking four questions. What data are being generated? What problems can I solve with the data? Should we use the data? How do I motivate the necessary investments? The answers will provide a guide for companies to use existing information resources to enter the IoT era now. 

 

1 What data are being generated? 

Although Big Data and the Internet of Things have become hot topics of discussion, those who want to analyse IoT data must first identify what data are being generated within their processes. In many cases, managers are only aware of a small fraction of the total data generated by operational technology and other equipment. The CTO of an Industrial IoT application provider explains: “most of the dark data we’re probably not aware of [is dark] simply because historically we have not been looking for it. But the fact that it is dark data, it’s data we weren’t thinking as having value, we weren’t cataloging it or indexing it.” Different functional units within an organisation may be unaware of the data generated by their sister units. The managing director of an analytics service firm paints an all too common scenario: “I remember a customer being with a client and their IT department, and the client said, ‘Well, if I had X & Y, I could do that.’ He then explains the IT department’s answer: ‘Yes, we know we have this. We have the data. Why don’t you ask us?’ Well, the line of business is not going to ask what they don’t know.”

Sometimes, it is a classic situation of “not seeing the forest because of all the trees.”  Employees know about the data and use it every day.  But because they view it as common, they don’t recognise that it is a form of IoT data. 

Sometimes, it is a classic situation of “not seeing the forest because of all the trees.” Employees know about the data and use it every day. But because they view it as common, they don’t recognise that it is a form of IoT data.The managing director of a consortium of IoT companies shares how common this is in the oil refining business: “There is a significant amount of intelligence, information, or data from a petrochemical refinery that would be considered instrumentation of the physical world. Being in the middle of it could be blinding me to the possibilities of how it’s different.”

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About the Authors

Gregory Gimpel is a clinical assistant professor of computer information systems at Georgia State University. Prior to GSU, he conducted digital strategy research at the Massachusetts Institute of Technology and he designed Ball State University’s business analytics major. His research focusses on the intersection of emerging technologies, analytics, and digital business transformation. He worked in senior management positions for a decade before entering the academic world

Allan Alter is an independent consultant and researcher in the Boston area. He is a former senior principal at Accenture Research and Fellow in Artificial Intelligence and Machine Learning at the World Economic Forum’s Centre for the Fourth Industrial Revolution in San Francisco. His research focuses on emerging technology and technology management strategy. He was previously an editor at MIT Sloan Management Review, Computerworld, CIO and CIO Insight. He holds degrees in intellectual history from the University of Michigan and the University of Pennsylvania.

References
1. Chou T. Precision: Principles, Practices and Solutions for the Internet of Things. USA: Cloudbook, Inc.; 2016.
2. Solon O. Wearable Technology Creeps Into The Workplace. Bloomberg. Available at: https://www.bloomberg.com/news/articles/2015-08-07/wearable-technology-creeps-into-the-workplace, 2018.
3. Association G. Mobilizing Intelligent Transportation Systems: GSM Association; 2015.
4. Liongosari E, Mullan P, Muller M, Guittat P. Finding a Way Forward: How Manufacturers Can Make the Most of the Industrial Internet of Things: Accenture; 2015.
5. Markoff R, Seifert R. The Real Industry 4.0 Challenge. The European Business Review; March – April, 2018.

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