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Executive Insights on the State of IoT in 2018

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Executive Insights on the State of IoT in 2018

See what nearly two dozen executives have to say about the state and current trends of IoT projects as the field continues to mature.

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To gather insights on the current and future state of IoT, we talked to 23 executives involved with IoT. 

Key Findings

  1. The keys to a successful IoT strategy are to understand how you are going to get value from all of the data you collect and how this fulfills a business need that will improve the customer experience. Answer the following questions as a team: What is the product or service being created? What is the benefit being provided? What is the value you are adding? What customer needs are we fulfilling? How can we monetize it? Start small, then scale and iterate. Use business insights from the data to solve problems and improve the solution you have built.

    Learn what data will be useful to customers, and what they’re willing to pay for. Put the customer first and make it easy for the customer to see the benefits of your product or service.

  2. Companies can get more out of IoT by solving a specific business problem. Doing so will generate revenue, reduce costs, and have an obvious ROI. Solve problems simply and easily, but don’t mistakenly believe that IoT is easy, because it’s not. There is a lot of complexity to make IoT solutions a reality, and most companies do not have the skillsets in-house to do so. In addition, most employees do not have the strategic vision to think about consumer or business use cases that drive revenue, efficiency, and ROI.

  3. The biggest change in IoT in the past year is that more companies are pursuing IoT strategies as they see use cases that have driven ROI. Companies are learning the need to focus on their IoT business model as well as the ROI over three to four years. Organizations are looking more closely at business cases before jumping into technology and this is important given the complexity and specialization of the technology by vertical. We have a lot of vendor stories, but not a lot of customer stories except in energy and manufacturing. Some companies are pursuing IoT initiatives in conjunction with their digital transformation initiatives since they are both heavily data-driven.

    Additionally, some companies are leveraging artificial intelligence (AI) and machine learning (ML) for cross-sensor analytics as well as to generate real-time results and insights for better-informed decision making.

  4. The technical solutions used for IoT initiatives vary greatly by industry with specific sensors, data, and analytics tools are being used based on specific industries and use cases. The industry is becoming very complex, and more specific solutions are becoming necessary.

  5. The industries with the most IoT applications shared by our respondents are healthcare, manufacturing, oil and gas, and automotive. The most frequent applications are incident prevention, predictive maintenance, optimization, and autonomy. Several respondents are working with medical device manufacturers to perform preventive and predictive maintenance on MRI machines that are also aggregating images and analyzing them with ML to prevent and predict diseases, as well as recommend treatments.

    A lot of respondents are working with different automotive OEMs to help build autonomous vehicles that know the best routes to reduce traffic, risk, and ensure safer transportation. There are also a lot of manufacturing applications to optimize yield management, as well as using preventive and predictive maintenance so companies can bring machines down for maintenance and part replacement on a schedule rather than in an emergency situation, which can cost a production facility millions in lost productivity.

  6. The most common issues mentioned preventing companies from fully realizing the benefits of IoT is a lack of talent and vision, as well as organizations' and individuals' failure to identify the problem they are attempting to solve. Organizations do not have the people capable of performing the analysis necessary to take advantage of data to solve problems. There’s an unrealized complexity of deploying IoT solutions and a lack of skills to do so. There’s a general lack of awareness about the impact IoT can have and what problems it can solve, as well as a lack of understanding of what technologies to use.

    The second most frequently mentioned problem has been a recurring theme – failure to identify the specific business need or problem to solve. Focus on the business value being created for customers. Taking a regular product and connecting it increases costs – but you must also increase the value to the end user.

  7. The biggest opportunities in the continued evolution of IoT is around AI, ML, DL (deep learning), predictive analytics with edge computing, and cross-sensor AI/ML models. There is a clear intersection between AI and IoT, and the benefits for businesses will be transformational. Tasks that used to take humans weeks or months to complete will be done in minutes or seconds.

  8. The primary concerns with the state of IoT today are security, privacy, and the lack of standardization. There’s a concern over privacy and security with the potential of people or companies to have a backdoor into your home network, as well as an inability to update devices already in the field. There is also a lack of standards for device communication. No one seems to be interested in taking a leadership role and solving the issue.

    The IoT industry seems to be relearning the same lessons regarding security that have evolved over time in other computing industries. Best practices, like the ability to update a device and restricting access to unauthorized users are rampant problems in the industry today.

  9. A broad range of skills is needed for developers to be successful working on IoT projects. Those mentioned more than once include: understanding the business context, knowing the IoT stack, mobile development, toolsets, data ingestion, cloud, and security. It used to be OK to be a good developer, but IoT is complex. You need to understand the business context of the solutions you are building. Know mobile and cloud platforms, as well as the wide spectrum of firmware available. Use available tools to shortcut the process. Think about how the product will function and get updated when it’s deployed.

Here’s who we spoke to:

This article is featured in the new DZone Guide to IoT: Harnessing Device Data. Get your free copy for more insightful articles, industry statistics, and more!

iot ,iot data ,machine learning ,iot use case

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