What is IoT Analytics and Why Business Leaders should care?
Internet of Things (IoT) – an inevitable part of modern life, right from those in our homes to the ones over our wrists and in our pockets. Business operations and Industry sectors are also not new to this concept. In fact, many businesses have started integrating IoT technology into their processes and products, changing the way they work and serve their customers. In 2022, 26% of small enterprises used IoT while large enterprises used it almost twice as much (48%).
Predictions estimate that there will be 14.4 billion active connections in 2023 (an 18% growth) and reach 27 billion by 2025. This is expected to result in 73.1 ZB of data generated solely by IoT devices. In such a data-driven world, imagine how precisely businesses could generate real value for their customers just by processing and analyzing this data properly.
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To list a few benefits, business leaders will be able to make more informed decisions, reduce operational costs, utilize their assets, equipment, and resources smartly, and even drive their business toward sustainability – a keyword for businesses in 2023.
This article explores the possibilities that IoT brings when merged with business analytics. We’ll discuss what IoT Analytics is and what it means for today’s business.
What is IoT Analytics?
IoT Analytics is the practice of collecting, processing, and analyzing the data generated by Internet of Things (IoT) devices and sensors. The goal of IoT analysis is to turn raw data into actionable insights that can inform business decisions and drive growth. This is achieved through the use of big data analytics, machine learning, and artificial intelligence, which allow for the processing of vast amounts of data in real time.
As the number of connected devices and the amount of data generated continues to grow, businesses leveraging IoT Analytics can gain a deeper understanding of their operations, identify areas for improvement, and make informed decisions to achieve their objectives.
Benefits of implementing IoT Analytics
- Improved Operational Efficiency: By collecting and analyzing data from IoT devices, businesses can gain real-time insights into their operations, helping to identify areas for improvement and optimize processes.
- Predictive maintenance: IoT analytics can help businesses predict when equipment is likely to fail, allowing for proactive maintenance that reduces downtime and increases overall equipment reliability.
- Better decision-making: With access to real-time data and insights, businesses can make informed decisions based on actual performance, rather than relying on assumptions or guesswork.
- Increased revenue: By optimizing operations and reducing downtime, businesses can increase their overall efficiency and productivity, leading to increased revenue.
- Competitive advantage: By leveraging IoT analytics, businesses can gain a competitive edge by being able to quickly adapt to changing market conditions and customer demands.
- Cost savings: IoT analytics can help businesses identify inefficiencies and reduce waste, leading to cost savings. For example, by optimizing energy consumption, businesses can reduce their energy costs.
- Customer satisfaction: By providing better products and services, businesses can improve customer satisfaction, leading to increased customer loyalty and repeat business.
In summary, implementing IoT analytics in a business can help to improve efficiency, reduce costs, increase revenue, and provide a competitive advantage.
Types of IoT Analytics & How they impact Business? [+ Examples]
- Descriptive Analytics
Descriptive analytics focuses on summarizing the data already collected to provide a basic understanding, uncover patterns, and identify trends and relationships. It helps businesses answer questions such as: What happened? Where did it happen and How often a particular behavior is seen? Using this type of analytics businesses can detect anomalies and understand the usage of a device, locate assets, etc.
For example, a manufacturer could use descriptive analytics to understand the distribution of their products, and identify the average production time, the range of production times, and the percentage of products produced within a certain time frame. This information could then be used to optimize the production process and improve efficiency.
- Diagnostic Analytics
While Descriptive Analytics focuses on the ‘What?’ part, Diagnostic Analytics is a step deeper, focusing on the ‘Why?’ part. It drills down into the data to understand the root cause of particular issues and provides insights into why it’s happening. The goal of this type of analytics is to help businesses identify the problem and improve their operations.
For example, a manufacturer could use diagnostic analytics to identify the root cause of production bottlenecks. They could analyze data from their production line to determine the specific factors that are causing the slowdown, such as a specific machine that is frequently breaking down or a particular process that is taking longer than expected. With this information, the manufacturer could make targeted improvements to their production process and improve efficiency.
- Predictive Analytics
As the name suggests, predictive analytics uses statistical models and machine learning algorithms to make predictions about future events or outcomes based on historical data. The goal of predictive analytics is to provide businesses with valuable insights into future trends, patterns, and behaviors to make informed decisions, anticipate issues and make data-driven decisions to achieve goals.
For example, a retailer could use predictive analytics to make predictions about future sales trends. By analyzing historical sales data, they could identify patterns and relationships in the data, such as the impact of specific promotions or events on sales. With this information, the retailer could make informed decisions about future promotions and marketing campaigns, leading to improved sales and increased revenue.
- Prescriptive Analytics
This analytics is considered the most advanced type of IoT analytics as it not only predicts the future but also provides suggestions and recommended actions based on data generated by IoT devices. By analyzing large amounts of real-time data generated by IoT devices, prescriptive analytics provides insights and recommendations that can improve the efficiency and effectiveness of business processes.
For example, a retailer may use prescriptive analytics to analyze sales data and forecast future demand. Based on this analysis, the retailer can optimize its inventory levels to ensure that the right products are in the right place at the right time, reducing the risk of stockouts and excess inventory. By considering multiple constraints, such as budget constraints, lead times, and customer demand, prescriptive analytics can help the retailer make better decisions and improve its supply chain efficiency.
Embrace IoT Analytics Journey with iLink Digital
IoT Analytics help businesses understand their data better, draw actionable insight and produce value for their customers. With over 20 years of experience in data analytics and IoT technology, iLink has helped several companies implement a reliable IoT Analytic solution as a service.
If you’re ready to take that first step into the future of analytics, feel free to reach out!
