Stranger (Internet of) Things: A couple of factors to consider before making the investment

Stranger (Internet of) Things: A couple of factors to consider before making the investment

We recently attended the IoT Summit Chicago hosted by the Illinois Technology Association. This conference gathered over 500 professionals from around the globe to collaborate on best practices and discuss future trends in the often discussed, but rarely understood “Internet of Things (IoT).” We took a moment to reflect on time the conference and identify some key takeaways to share with you:

IoT is undoubtedly a buzzword that translates differently across industries. Despite the varied interpretations, understandings and explanations, common themes exist among successful IoT solutions. The combination of devices, sensors, data science, software, hardware, analytics and everything else required to extract insights from the IoT world is a challenge. It takes significant investments to transform traditional network solutions in multiple areas:

  • Hardware – the “things” that collect data (sensors, meters, cameras, etc.)
  • Collection and Communications – the protocols and services that collect and manage data from all the various places the hardware might be located (field, shop floor, telematics, etc.)
  • Software – the applications that analyzes and uses the hardware data to solve business problems

Before embarking down the costly path of time and capital, a few thoughts from the summit:

  • Successful partnerships – the sheer number of players in this space is endless. From embedded sensor technology manufacturers to ‘platform’ and data management vendors to prescriptive analytics and machine learning software developers; and everything in between. There are lots of pieces to this puzzle, and if you are going to be successful in putting the puzzle together, you will need to pick some partners to help. Finding a hardware or software partner is difficult choice in itself. But identifying a partner for the collection and communication of data is substantially more difficult. There is more risk in this selection, as data collection and communication is the lynchpin of any IoT solution. Platforms that provide this type of functionality are often heavily focused on specific industry functionality or less feature rich capabilities to cater to multiple industries. Choosing the right partner from the start is critical to any IoT success.
  • Data overload – those familiar with Murphy’s law can better understand the concern around data in IoT solutions. If a device can send data; and that data can be collected, managed, and analyzed; and that analysis can be used to solve a problem, then we are on the way to an IoT solution. But that IoT solution may not need every piece of data that can be generated or collected or analyzed. This is where the allude to Murphy’s Law come into play. On the scale of an IoT solution, the collection and management of excess amounts of data can become so large that it is prohibitive to the timeliness and overall function of an IoT solution. This scenario often manifests itself when infrastructure, platforms and software are implemented without focus on specific, value driven IoT solutions. A common mistake in IoT is to build a platform and start to collect massive amounts of data. All this with the idea that an IoT solution can then be developed on the back of that effort. This type of data collection and platform development is not a recommended approach for IoT solutions. Successful IoT solutions define the data needed before spending excess time, effort and money to collect and manage data that is not needed.
  • Use cases – solve business problems. Successful IoT solutions solve business problems and add value to organizations.

To illustrate the different translation of IoT between industries, we’ve described the top use case in both the Manufacturing & Distribution and Energy & Utilities industries:

In Manufacturing & Distribution, predictive maintenance is widely considered a common use case for an IoT solution. Like any asset, the time that asset is productive is critical to overall profitability. Any downtime caused by reactive measures to machine failures can be costly. Especially if machine parts or specialized services are required to repair the machinery, as these often come with delays in procurement and transit. An IoT system can combine sensor data and advanced analytics to predict failure points. Proactive measures can be taken to coordinate materials and schedule services to minimize equipment downtime.

In the Energy & Utilities industry, utilities are investing in Advanced Metering Infrastructure (AMI) to automate collection of customer consumption data and other distribution grid conditions. Crossing the IoT chasm, a utility can leverage its AMI network to provide additional services and insights to its customers. An IoT service offering could be price-responsive load control program that connects to the customer’s appliances via their Home Area Network. Alternatively, a utility could provide nearly real-time consumption data and meter intelligence that feed into a customer’s smart home platform. Utilities that are proactive in architecting IoT solutions will remain ahead of the regulatory curve and demonstrate leadership in digital customer experience.

The growth of IoT solutions can bring additional value to a manufacturer through the prevention of downtime and loss of operations; or it can unlock additional services and insights for a utilities’ customers. This is just a glimpse at a few possibilities and there are more industries that can benefit from IoT; and many more IoT solutions to be had.

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