IoT analytics provides businesses with the tools and procedures needed to realize and derive value from the sheer volumes of data generated by connected IoT devices. Business owners and decision-makers must analyze IoT data to identify trends and make forecasts — in other words, make better decisions.
Other business values derived from IoT analytics include optimized operations, improved product quality, improved customer experience, a better understanding of customer demand, and new sources of revenues. Businesses use many different data types so there must also be different IoT analytics.
This blog will define and explore descriptive, diagnostic, predictive, and prescriptive analytics and how it is used in the IoT context.
Descriptive IoT Analytics
Descriptive analytics is deemed the simplest form of data analysis. It focuses on using current and historical data to better understand patterns and changes within a business. In the IoT context, descriptive analytics provides surface-level insights into how a connected device is performing in real-time.
While IoT data at this stage offers little insight into future performance, it does enable decision-makers to be alerted of irregular patterns or anomalies so that they can take immediate action. For businesses, these alerts are critical in helping to avoid IoT device failure, potentially saving thousands or millions of dollars in damage and lost productivity. For example, if one of the sensors of a smart streetlight — which turns on and off when a vehicle passes — fails, teams running descriptive IoT analytics will get a notification that the sensor is not working properly. Then, they can take action to fix it before it starts affecting traffic patterns or energy consumption levels.
Diagnostic IoT Analytics
Diagnostic analytics builds on descriptive analytics to help businesses understand why something happened in the past. Analyzing IoT data at this stage helps to identify core problems and improve a service, product, or process. It can also help businesses understand why a particular IoT device is running in a certain way or why it is producing certain outputs.
While descriptive analytics identifies anomalies for the purpose of taking action in the moment, diagnostic analytics is used to understand anomalies and also areas of inefficiency so that businesses can find the root cause of a problem and fix it. For example, in the same smart streetlight scenario mentioned in the last section, if the sensor is working but not performing optimally to save energy, diagnostic IoT analytics looks at the data to understand why it is consuming so much energy. Diagnostic analytics such as these are especially important for IoT due to needing to conserve power for transportability and using low-powered communication protocols. These insights are valuable in optimizing the performance of the sensors.
Predictive IoT Analytics
Predictive analytics uses historical data to identify actionable circumstances or trends and model future data and behaviors. In the manufacturing field, for example, predictive analytics can assist in anticipating specific needs throughout a factory by automating much of the analysis processes.
Predictive maintenance is a growing subset within the field of IoT and is the process of utilizing data analysis to predict future outcomes. Combined with artificial intelligence (AI) and machine learning (ML) techniques, predictive IoT analytics can be used to predict future events more accurately, plan for unknown events, and discover opportunities in future actions. The main goal of predictive analytics is to inform the users what events are most likely to happen next. Continuing with our streetlight example, predictive analytics allow us to keep the right amount of replacement bulbs in stock by knowing how often we need to change the bulb.
The applications for IoT predictive maintenance in manufacturing are huge and can help improve machine lifespan, increase production, minimize maintenance costs, reduce downtime, and more.
Through the collection of machine data via sensors and wireless transmission of the collected data to a cloud-based analytics platform, manufacturers use predictive analytics programs to extract actionable insights. Manufacturers can monitor machines in real time and accurately identify and schedule maintenance on the parts that need replacement, thereby extending the lifetime of machines in the long run.
Prescriptive IoT Analytics
Prescriptive analytics is considered the most advanced type of analytics and typically uses advanced modeling based on AI, ML, or deep learning (DL) to examine the potential “consequences” of different courses of action, and then recommends the best path to take. The main difference between prescriptive analytics from predictive analytics is its focus on what could happen and consider various possible options and probabilities.
Many modernized industries make extensive use of prescriptive analytics in their day-to-day business, impacting consumers on a very basic level. Banks use prescriptive analytics for fraud detection, to protect their customers from suspicious transactions, and nearly all social media uses prescriptive analytics for content curation. Companies use lead scoring to predict which sales targets to pursue. Algorithms that predict the most likely outcomes if an action is taken is at the heart of this process.
The manufacturing industry has also started to take an interest in harnessing the recommendation power of prescriptive analytics. On the factory floor, prescriptive analytics can answer questions like, “This machine is 50% likely to fail in the next 24 hours. What should I do to prevent it?” It can also help make better decisions in improving various logistics handling models, distribution strategies, deciding what factories should create which products, and more. In summary, prescriptive IoT analytics is used to optimize decisions such as suggesting the best operating conditions to maximize efficiency or uptime of machines.
Conclusion
The role of data analytics in IoT allows businesses to extract meaningful information from their IoT data. Predictive analytics in IoT in particular can be seen as actionable intelligence so that businesses can improve operations across the board. Every business needs accurate, relevant data to drive the right decisions.
SDT’s solution helps businesses implement their own AI analytics and IoT hardware for automation and insights.
Head over to sdt.inc or connect with us on LinkedIn to get started!
Read more about IoT on our Naver Korean blog.
About the Author: Karen is a passionate B2B technology blogger. While studying at Georgia Tech, Karen first grew interested in cybersecurity and has since worked for several security and cloud companies as a global marketer. When she’s not freelance writing, Karen loves to explore new food trends.
