The Internet of Things (IoT) spans multiple technologies; collectively the acronym refers to a network of connected physical objects or “things” that gather information through intelligent sensors and chips and then transfers it across the internet. This allows organizations to collect and eventually use that data to drive business decisions.
The IoT generates a vast amount of information and has significantly contributed to the growth of the world’s data over the years. Because the IoT works by gathering real-time data from the connected “things” in its network, the amount of IoT data is exponentially growing over time. In addition to the magnitude and quickly changing nature of the data, it is highly complex, coming in a variety of formats.
Because of these attributes, historically it has been a time-consuming and costly challenge for organizations to analyze the information being collected—even with the benefit of IoT platforms. Unless you were willing to pay for the recourses and experts needed to make sense of the chaos, the sheer magnitude and complexity of IoT data put any data-driven insights out of reach.
As the market and technologies matured, advanced and affordable Internet of Things analytics solutions have turned previously inaccessible data into insights that are literally changing how companies do business. As the number of organizations that can effectively use IoT analytics has increased, the number of innovative applications and advanced analytics use cases has similarly proliferated.
Read on to understand how organizations across varying industries use Internet of Things analytics to drive business decisions.
IoT analytics is used to make sense of the vast amount of data produced by the Internet of Things. This data comes in through myriad sensors and devices; a single piece of machinery may have dozens of different sensors, each producing data constantly. IoT analytics is critical to being able to ingest the data from IoT connected devices, and produce insights, detect patterns, or make predictions that businesses can use when making business decisions.
There are different kinds of advanced analytics solutions that organizations can utilize to make sense of the IoT data. The right solution depends on the volume of data being generated, the complexity of insights provided, and the type of actions driven by those insights.
The types of Internet of Things analytics are broken down by the types of challenges they address and insights they produce. The main four are descriptive analytics, diagnostic analytics, prescriptive analytics, and predictive analytics.
Descriptive analytics make sense of the real-time data coming from IoT connected devices. It monitors the performance of devices and determines whether it is running how it should be. This type of analytics can be used to detect anomalies, understand how a device is being used internally or by consumers, locate an organization’s assets, understand the outputs of a given machine and more.
Diagnostic analytics provides insights into why things are happening. It can be used to understand anomalies, areas of inefficiency or other trends. In the case that a device is not performing optimally, diagnostic analytics look at IoT data to identify what the problem is.
Predictive analytics incorporates machine learning capabilities to assess the likelihood of a future event happening. Machine learning models are trained with vast amounts of historical data that allow it to identify trends and the probabilities of certain things leading to certain outcomes. It applies this knowledge to the real-time data coming in from IoT devices to effectively predict the future. These types of insights give organizations the time to act proactively to change the predicted outcome if it’s not what is desired.
One of the more advanced analytics capabilities is prescriptive. Prescriptive analytics gives additional insights into what actions you can take to impact the results of descriptive, diagnostic, or predictive analytics. It helps organizations better understand how they can prevent failures, improve effectiveness, avoid or increase outputs and more.
By applying IoT predictive analytics to a predictive maintenance model, companies can better understand the current condition of devices—as well as their future needs. Predictive maintenance can inform the best time to service equipment, and even predict and prevent possible failures before they occur. Predictive maintenance is transforming service outcomes—including up to a 30% drop in unplanned downtime, up to 83% faster service, and upwards of 75% less time spent on site.
For industrials, IoT analytics applications lead to improved product quality, production efficiency, and customer service. While in production, industrials can test and monitor goods in a virtual environment that allows them to proactively identify any issues before putting them to market. By using smart manufacturing equipment, organizations can better understand the manufacturing process and potential areas for increased efficiency. Once products go to market, organizations can leverage predictive maintenance to decrease service costs and increase customer satisfaction.
The use of Internet of Things analytics in the healthcare field is leading to a more patient-centric and holistic approach to healthcare due to the insights it can provide.
Using healthcare apps on smartphones and connected medical devices like wearables and smart hospital beds, medical providers are now equipped with a deeper understanding of their patients’ health. The additional insights into patients reveal potential risks and allow providers to proactively treat patients.
Wearables and apps used outside of the hospital allow healthcare providers to remotely monitor patient metrics and vital signs and automatically be alerted of certain outcomes even when patients are not physically near their healthcare provider.
Because speed and efficiency is so important to revenue in Supply Chain, many of the IoT applications in this industry have to do with its ability to optimize processes. The IoT can be used to identify the exact location of both raw materials and products, meaning organizations can track and predict how a product moves and develops through the supply chain. This analysis helps to identify opportune areas to increase efficiency as well as actionable insights into how to fix those inefficiencies.
Both providers and end users benefit from the ways that IoT analytics are applied to the energy industry. For energy providers, energy meters equipped with sensors allow them to monitor and control the electrical network between production plants and different distribution points. For end users, they can gain insights into how they are consuming energy and how they can adjust it if they want to.
Advanced analytics associated with the Internet of Things is a relatively new and challenging field. It takes in huge volumes of heterogeneous data from IoT devices and because of this there are a few innate challenges typically associated with big data analytics. One of these is visualization. Due to the volume at which IoT data is created (and will continue to be created) data storage and management is a primary challenge. Current big data storage capabilities are limited so the challenge of analyzing this data continues to develop.
Secondly, because IoT data can come in different formats; structured, unstructured, or semi structured, visualizing the data to drive business decisions can be difficult. For IoT data to provide clear, actionable insights, it first needs to be optimized for visualization.
The availability of innovative solutions in the market have been game changing for organizations who previously couldn’t make use of their huge amounts of data. Products like ThingWorx remove the barriers to data analytics so companies can focus on the value that their data brings—without needing to staff up a team of data scientists. These solutions help organizations to transform their high-volume IoT data directly into actionable insights that can enhance decision making across enterprise functions.
From healthcare to manufacturing, organizations across varying industries use IoT analytics to drive business decisions. By utilizing the different types of analytics available they can understand how products are used, why certain outcomes occur and can even predict the future and gain insights on how to change future outcomes—redefining how businesses function.
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