10 Things companies need to know about Internet of Things

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The Internet of Things (IoT) is turning into a hot-catch issue for the C-suite. Many senior pioneers are concerned whether executing IoT-based innovation and processes is worth the time, money and effort involved. Others worry they risk being abandoned and losing their clients to rivals if they stay away from it by and large.

On the off chance that so many companies understand the value of the IoT and are contributing extensive time, money and effort to understand its potential, why are such a large number of as yet are failing to achieve it? Let’s take a look at why are they missing the mark by this list of 10 rundowns of what each company ought to think about effectively operationalising the IoT.

  1.       The value of the IoT is not technology. The real value lies in the creation of new value propositions and potential revenue streams. The key is taking this technology and using it to move toward new business models and services that will help realize them.
  2.       IoT data will be more democratic than Supervisory Control and Data Acquisition (SCADA) data. Historically, SCADA data has been locked away in somebody’s process control network. To access this information, update it, and revalidate it, people needed a miracle. With IoT, you can freely and quickly bring up this information when and where you need it.
  3.       Businesses outside of your industry may know something you don’t. For example, highly powerful tools developed for clickstream analysis, fraud detection, cyber security, and genome sequencing are now coming to process industries. Don’t snub other industries, thinking that you are different from them. They may have a few tricks in their pocket that you need.
  4.       Standardization leads to repeatability. The more comparable assets are in your organization, the better your forecasts will be. Machine learning is better with more, similar data. Anything less leads to misconstrued information and inefficiency.
  5.       Information Technology (IT) and Operational Technology (OT) are converging – deal with it. Data engineering can take significant time and resources. However, it shouldn’t stop you from moving forward with IoT initiatives. Instrumentation and controls engineers from the world of operational technology (OT) have to bridge the gap between the analytics and IT communities.
  6.       Sensors will not live forever. In other words, cheap sensors are not going to be 100% reliable, 100% of the time. Ultimately, all sensors fail either instantaneously or slowly degrade. Processes must be established to make sure sensors are fully operational and deliver correct data.
  7.       Your information is as good as your sensors. Reliability of predictions is only as good as the data feeding them. If you are going to run analytics based on sensor data, you better make sure that the sensor is in good working order.
  8.       Data needs context. To develop a model that forecasts behaviour, data scientists require context and time-series data. Otherwise it becomes very difficult to consume this information and truly see what happening now and in the future. People need real-time data to make the best possible decisions. With pervasive monitoring, this information is captured and delivered for business intelligence analysis
  9.       The IoT brings a tsunami of data. IoT rollouts bring a proliferation of cheap, distributed sensors – resulting in a huge volume of data in a short amount of time.
  10.   Don’t forget what powers the IoT. Data integration and actionable information are the heart of collection and analysis of IoT data. Invest in the technologies, expertise, and processes that support integration, reporting, decision making, and action – and maintain them well.



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