“Your ability to predict the future depends on how much you know about the past — a company’s ability to forecast depends only on the data it has. The need for new and better data sources has led technology sectors and technical consultancies to pioneer new ways of collecting data, such as IoT sensors and devices.
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Data has become one of the most valuable assets of a business. As data analysis techniques improve, analysts will soon be able to extract valuable insights from large amounts of data.
Your ability to predict the future depends on how much you know about the past — a company’s ability to forecast depends only on the data it has. The need for new and better data sources has led technology sectors and technical consultancies to pioneer new ways of collecting data, such as IoT sensors and devices.
Used properly, these IoT sensors can greatly improve the predictive analytics capabilities of any company.
Below, we describe how data is collected from IoT sensors and used for predictive analytics, and how companies can benefit from this data.
What is IoT Analytics?
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What differentiates IoT analytics from traditional analytics is the data it uses. IoT analytics is the extraction of data from an array of IoT sensors configured to provide multiple data types. In addition, these sensors can provide administrators with comprehensive and real-time data sets, for example, in some systems, IoT data pools can be updated every minute or every second.
Administrators can then analyze this data—often with the help of big data analytics or IoT platforms—to identify trends and make predictions.
Why and how to use sensor data in predictive analytics
There are a few industries that particularly benefit from the data provided by IoT sensors, and they are already heavily integrating these sensors into existing workflows. Here are some examples of how and why sensor data is used in predictive analytics.
Factories are the biggest beneficiaries of the rapid development of IoT sensors and data collection platforms.
In a factory setting, downtime can be costly, where machine failure can be costly or even dangerous to workers.
IoT sensors built into factory machines can track variables such as vibration, temperature, and machine timing, which are then fed into analytics platforms and analyzed to predict when specific machines need maintenance. (From IoT Home Network) That way, factory managers can identify equipment before they fail, reducing the risk of downtime or more expensive repairs.
Some of these platforms are even embedded into factory control systems to shut down machines in an emergency or imminent failure.
IoT sensors are also used for predictive analytics in smart cities. With sensors, cities can monitor everything from traffic flow to parking space usage. Cities can then use this data to drive policy decisions—such as how to redesign traffic light designs or enhance infrastructure investments.
Some major cities, such as Moscow and New York, are already using data from smart sensors to guide urban policy.
Improving predictive analytics with IoT data
When companies have access to large amounts of accurate data, they can use predictive analytics to make better decisions. IoT sensors can provide unprecedented high-quality data—enabling companies to dramatically improve their ability to predict the future.
These sensors are already used in a number of different fields – such as manufacturing and smart cities. As data becomes more valuable, organizations will start looking for ways to improve their ability to predict future events, and IoT sensors will become more commonplace.
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