What It Takes To Make IoT Implementation A Success – Robert Plant & Cherie Topham
Organizations around the globe understand the importance of IoT. In fact, in a recent Forbes Insights/Hitachi survey of more than 500 executives worldwide, over 90% said IoT will be important to the future of their business. What’s more, of all emerging technologies, executives said IoT would be the most critical, ranking it above others like artificial intelligence and robotics.
While executives acknowledge the importance of IoT, 49% remain in the early stages of planning or are only operating pilot programs. We spoke with John Magee, Hitachi Vantara’s vice president of product and solutions marketing, to get his perspective on this state of development and how organizations can make IoT a larger part of their strategy and operations going forward.
If an executive is looking to invest in IoT and understand the economics behind it, what does he or she need to know?
Most organizations are looking to IoT projects to either improve operational efficiency or drive new revenue streams. A lot of organizations are seeking to use the data they can get from IoT sensors and connectivity to provide better visibility and help them understand what’s going on in their operations. For product companies, they’re often looking to optimize how their products are being manufactured or used, and to offer new data-driven services with those products.
The goal for most of these companies is to transform the way they operate and the way they compete. For business leaders looking to take advantage of IoT, the most important thing is to begin with the business outcome goals first and then determine what data IoT can provide that can help deliver those outcomes. It’s the new data that delivers the business value. So that should be the starting point for any project. Then you can work back from there to the technology required to meet the objective.
For example, manufacturers might want to understand why quality issues are creeping into one of their manufacturing lines but not the other. Logistics companies may want to understand the location of parts and deliveries to optimize scheduling. Product companies may want to sell new value-added software services that help customers get more value from their products. Whatever the goal, by understanding what data you need to collect and who needs access to it, the technology requirements will fall into place more easily and you won’t over- or underspend for success.
When executives are thinking about what data is most important to achieving their desired outcomes, what do they need to know? How should they approach this?
IoT is essentially a rich source of new business data. Data that comes from machines and devices, and from the spaces and environments those machines operate in. In many situations, just having access to real-time data about what’s going on—in a manufacturing plant, on a remote oil rig or in a city train station—can be transformative. In most situations, though, some analysis of the data is going to be needed to gain the insights that lead to business value.
This is where technologies like big data analytics, machine learning and artificial intelligence come into play. Analytics is the key to not just understanding what is happening but also learning and getting smarter so that your IoT solutions can predict when a problem will occur or find the root cause of product quality issues that would have been unsolvable without analyzing the mountains of data that IoT can deliver.
The right way to think about IoT is as an extension of the business analytics that your organization is probably already doing in other areas. At the end of the day, IoT is a means to accessing and interpreting more data. And data management, data integration and data science are all key enabling technologies for IoT, just as they are for most other areas of business today.
One new twist on IoT data that differs from traditional business data is the idea of a “digital twin.” The digital twin is the software representation of a physical device, such as a pacemaker, an elevator or a dump truck. As data streams in from the physical device, it is collected and stored in the corresponding digital twin. The digital twin knows everything about that asset: where it was manufactured, how it has been operated, when it was last serviced.
By using software to analyze hundreds or even thousands of these digital twins, data scientists can build powerful analytic models that can optimize the corresponding physical assets. Organizations are using this approach to enhance asset uptime and performance, extend the useful life of critical assets and optimize maintenance and operations.
Once you’ve aggregated data into a single version of the truth and are drawing conclusions, how can companies best integrate that information into broader networks?
There’s a sort of stairway to value in many IoT scenarios. The first step of the stairway is the physical devices themselves. The second step is the operations around those devices. And the third step is the business processes and ecosystem around those operations.
Think of a manufacturing plant. If you use sensors on critical plant equipment, you can get data that can help you operate that equipment more effectively. If you collect enough data, you can even start to predict when it will fail so you can service it before that happens. So that’s the next step – using the data insights about the equipment into optimizing your maintenance and repair operations.
But that data can also be useful at the next step in the stairway, which is how your supply chain responds to requirements for parts or materials being delivered based on the performance of the equipment and operations in the factory. The more data you have, the more visibility you have, and the more opportunity to optimize every part of the operation. Sort of like air traffic control for the factory.
This stairway, or hierarchy, of value—from asset to operations to business process—is one we see play out in industry after industry.
When it comes to IoT, which is a complicated endeavor, research shows that it’s best not to go at it alone. What should executives be looking for in a partner when they’re considering making this transformation?
Working with a partner who understands your industry and has a methodology to help you think through your data strategy are the real enablers for success. IoT is a hot technology right now, and it is easy to get caught up in the hype and invest in the wrong areas. Working with an experienced partner who has a pragmatic approach that starts with understanding how IoT data and analytics will drive the desired business outcome is the key to success.
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