Data Analytics and IoT are no longer fancy terms to use. They are an integral part of IoT solutions that help you cut past the noise. According to Mordor Intelligence 2019 estimates, by 2024, the IoT industry is expected to reach a value of $6.1 billion.
There has been tremendous hype around IoT in the last few years. Consumers and businesses have strived hard to keep pace with the technological development in the area of IoT. But is the hype justified? Does IoT merit that kind of attention? Let’s find out.
The global market favors IoT development because of its potential to bring substantial benefits to the economy. One element of growth in the IoT market is the blend of IoT and big data – IoT data analytics.
With a specific understanding of IoT-connected devices, you would already know that their existence and relevance rely heavily upon the data they obtain. But when it comes to the end-user, it is not solely about the raw data they find value in.
Rather the comprehensive interpretation of the information gathered is known as Data Analytics. The way raw data is transformed into meaningful information helps a user draw critical insights and bring core information to the forefront to provide understandable benchmarks on the user’s end.
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Presenting data in a meaningful and comprehensive way makes an IoT solution desirable to consumers of all kinds. Anyone can print data on the sheet and hand them off to you. But it takes time and effort of the user to sift through the data, derive meaning out of it, and shape it into something that can be worked upon.
Data analytics enables users to pick up trends within the information collected by any IoT-connected device. The data analysis insights ensure that a user is armed with the knowledge needed to make effective business decisions. Businesses and individuals hold significant power to be picky about the data they can produce.
Most consumers are willing to invest in IoT because the solution it offers ends up paying for itself down the line by automating tasks that require the manual expenditure of time and effort. Intelligent IoT Data Analytics provides essential metrics that are important to making these realizations a reality.
As a result of the technological advancements in AI and a plethora of open-source software, the availability of IoT is not an obstacle anymore. Competition amongst data analytics providers is at an all-time high, and legacy companies that dominated the industry earlier are now struggling to prove their relevance.
These emerging technologies have led data analytics to become more innovative, extracting critical information super quickly, learning what is essential to the consumer, and catering to their requirements. As IoT becomes an integral part of daily life, data analytics is imperative to help users draw critical insights.
Enterprises and businesses are utilizing IoT data to improve business operations in different ways. Insights from analyzing this data can make more informed business decisions referred to as IoT analytics.
The data that automates consumers’ lives can also be utilized by businesses and enterprises to gain insights into customer behavior and experiences to drive tangible product improvements. It can also be used to make business decisions based on real-time data and information. The challenge, however, to IoT data is that it is high volume and constantly streaming. Capturing, storing, and then analyzing IoT data is challenging.
Data and IoT are fundamentally linked to each other. When businesses receive vital information to analyze, they make use of data collected by IoT devices. This is how IoT gets connected to Data analytics. It helps businesses check significant volumes of data with segregated characteristics to generate meaningful inferences and conclusions in the form of trends, statistics, and patterns. These actionable insights enable businesses to use the predictive maintenance model to interact with different types of data to make informed decisions. IoT data analytics drives decision-making using predictive analytics for business owners and improves their ROI.
The primary challenge engulfing businesses seeking to incorporate IoT data analytics as an integral part of their business strategy is supporting the computing heavyweight lifting associated with it. Businesses must have an IT solution in place that is robust enough to handle constant and increased data load. It has a significant impact on the IT infrastructure, analytics tools, data management strategy, and security approaches of a business.
The IT infrastructure must be powerful enough to allow the influx of IoT data. Businesses need to identify data storage solutions and Business Intelligence tools to get the work done collaboratively and streamlined.
Data Management Strategy
Businesses must define a data management strategy that comprises IoT data, a mechanism to identify which data should be captured, how it should be sifted, and where it is stored. It is also critical to have consistent data that is compatible with analytics tools. In some instances, it is beneficial to integrate IoT data with other enterprise data to improve data visualization and predictive analysis, forming a core data management strategy.
IoT Data Analytics & Predictions
Constant streaming of large quantities of IoT data necessitates automation to be considered valuable. The system may also require configuration to enable the analysis of real-time and historical data. ML algorithms are utilized to perform predictive analysis to enhance business forecasting abilities.
Security is critical for integrating IoT data streaming from an array of connected devices across the globe. To guarantee the security of your IoT data, set up checkpoints to confirm valid security certificates before enabling IoT data into an IT solution.
Expanding enterprise IT systems to grasp the enormous computing capacity of IoT data analytics requires meticulous planning and implementation. But the rewards exceed the challenges. There are unlimited opportunities where IoT data analytics can be leveraged to amplify insights and improve business decisions. This includes –
IoT devices are being used in smart farming ranging from assessment of crop and soil to monitoring livestock. The data is captured and analyzed to provide real-time data. Problems can now be predicted, diagnosed, and corrected, leading to better livestock health and efficient harvest.
From thermometers and blood pressure monitored to wearable and chipped heart monitors, health can be observed in real-time with the help of IoT devices. This leads to better diagnostics and efficient preventive care.
Personalized Customer Experience
Customers expect personalized service when interacting with digital products and services. IoT data analysis can create the best experience for customers and enhance business insights. It reveals customer behaviors and preferences which can be used to predict customer needs over time.
Product Improvement & Innovation
IoT data tracks product performance metrics for IoT devices, such as smart home gadgets and automobiles. IoT analytics can make use of these metrics to improve the next generation of products and identify future consumer needs.
Energy Sector & Smart Meters
The energy sector has embraced IoT technology using smart meters that eliminate the need for physical meter readers. IoT data analytics improves real-time monitoring, diagnostics into system irregularities to allow energy companies to forecast potential energy spikes and future outages.
Over the past few years, IoT devices have brought abundant data streaming from smart home devices, smartphones, connected automobiles, and more. Businesses must capitalize on IoT data to build a robust IT solution for integrating high-velocity data and analyzing it.
Advanced data analytics is an integral part of IoT solutions, providing users with the necessary knowledge to make businesses smarter and point out potential problem areas without requiring much effort on the user’s end. However, there is more power in classifying data and what insights a user can draw from it. IoT data analysis enables profitable decision-making by consumers, and as IoT technology expands in popularity, it will continue to grow the demand for advanced IoT data analytics tools.