IoT analytics – What it is, Its Importance and Use Cases

The internet of things (IoT) is set to play a huge role in the near future, especially in industrial sectors. Sensors, manufacturing equipment, pipelines, and smart meters all have the potential to transform how organizations work. IoT generates a lot of buzz because it expands the functionality of organizations and shores up any weakness in their existing operations.

According to Gartner, “internet-connected things will outnumber humans 4-to-1, creating new dynamics for marketing, sales and customer service.”

In most ways, Internet of Things (IoT) analytics is like any other analytics. IoT analytics use most of the same algorithms and tools as other kinds of business intelligence (BI) and advanced analytics. Even so, the IoT is creating unparalleled information management and analytics challenges.

Let’s dig out more;

What is IoT analytics?

IoT analytics is the analytics platform that can assess the data collected from IoT devices. This variant of analytics is particularly well-suited to analyze IoT data because the devices typically generate a lot of information, in a relatively short time. Statistics show that IoT devices produce 2.5 quintillion bytes of data on a daily basis.

IoT data is similar to big data but there are differences not just in terms of size, but also because of the diversity of sources. The heterogeneous data sources make data integration an incredibly complex process – in fact, data integration is one of the biggest challenges to overcome. This is where IoT analytics comes into play.

Why analytics will play a huge role?

According to a study by 2020, IoT will include over 30 billion connected devices by the year 2020 – all these devices are going to collect a large volume of data and the amount of data produced by a sensor in the manufacturing assembly line, for instance, would take a lifetime to assess. This means we are looking at an immense volume of data that organizations will struggle to handle.

A recent survey incidentally also revealed that over 26% of companies said that their IoT initiatives were successful. However, many organizations do struggle with incorporating and handling IoT because they simply lack the right systems that can handle the volume, velocity, and variety of IoT data. Without IoT analytics, it would not be possible for organizations to glean the benefits of the tech.

IoT analytics is incredibly versatile and can be used for any purpose. Whether it is assessing the current condition of manufacturing equipment or studying market trends, IoT data analytics can be used in any industry and to perform any operation. Furthermore, analytics can also act as a lynchpin for different functions like industrial automation, developing cloud solutions, creating mobile apps, and hardware development.

Profit and non-profit organizations will see a massive increase in the volume of data coming into their system and, if their analytics infrastructure is not ready, it will lead to a significant reduction in the rate of data analysis, which means lower operational efficiency. In other words, data analysis will take place at slower speeds, even possibly denying some of the benefits of IoT, like the real-time analysis of data. However, with IoT analytics, it is possible to maintain data analysis at an acceptable rate. Both for-profit and non-profit organizations will benefit from this version of the data analytics platform.

Is IoT Analytics that special?

Yes, it is. It’s important to remember that the IoT isn’t just about sensor data. The ultimate goal of IoT analytics is understanding people rather than things.

(Source: gartner.com)

Other kinds of data than sensor data that are involved in IoT projects include:

• Video feeds
• Mobile geolocation data
• Product usage data, which isn’t necessarily sensor data
• Social media data, which can be collated with IoT data
• Log files (computer-generated records of operations and events in software applications, networks, etc.)

To say that these types of data aren’t specific to the IoT is to miss the point. In many cases, the value of sensor data only becomes clear when it’s integrated and correlated with other data sources.

Let’s take a look now at some use cases for IoT analytics and business intelligence that can drive transformative business impacts across a number of verticals.

Use Cases

Improving Marketing and Sales

IoT Analytics plays an important role in boosting the marketing and sales of businesses by helping in the following scenarios:

• Anticipating Customer Needs – IoT Analytics helps you to collect and analyze customer requirements and trends based on product usage and reviews. It would also help in forecasting future purchases while aiding in the creation of new consumable resupply models.
• Help Deliver New Value-Added Services–It is through IoT Analytics that you can aggregate data from original sources to perform analysis, prediction, and action.
• Flexible Billing and Pricing – With the capture of the right data from various sources, it is possible to plan outcome-based pricing and subscription models. This also aids in the increase of value-added market penetration.

Real-Time Data Analysis for Manufacturing Sector

An entirely automated IoT Analytics aids in using real-time data to watch out for certain patterns and send alerts to the concerned departments. Manufacturers in all the major industries – electronics, automotive, chemical, durable goods, etc. have all heavily invested in IoT Analytics to improve their efficiency and production.

New manufacturing equipment with intelligent sensors is already incorporated in these industries to help with smart manufacturing. This generates huge monetization opportunities aiding in revenue generation initiatives and cost containment.

For example, ThyssenKrupp partnered with two other companies CGI and Microsoft Azure to send alerts when their elevators need repairs. Predictive maintenance is the name of the application that sends alerts when an elevator is about to go out of function and even teaches the technicians the areas of error.

Streaming Analytics for Real-Time Usage

Data streaming, the continuous processing of data that is processed continuously can be used successfully in real-time. The data that comes in changes constantly, meaning there is no beginning and there is no end.

The invention of open-source technologies like Apache Kafka, Apache Beam, and Apache Flink made real-time continuous streaming possible. You no longer have to wait for batch processing long cycles of data because all the analysis can be done as and when the data comes in… no more processing, no more wait.

For example, take the case of a financial institution, say a bank. They can analyze the instance of theft or fraud by watching the data that comes in. In fact, they can watch the data patterns to prevent fraud as well.

Acts as a Healthcare Game Changer

The significant changes made by healthcare through IoT are remarkable. People and apps are connected in a way that was never deemed possible before. This can, not only improve healthcare outcomes but, will also drastically reduce healthcare costs as well. Sensors embedded in medical devices will help doctors understand medical emergencies even before they arise.

These sensors are embedded in diagnostic equipment, surgical robots, personal health and fitness equipment, drug dispensing systems, and implantable devices. The data is collected and analyzed for real-time monitoring. Additionally, the equipment themselves is monitored for minimizing downtime and avoiding potential failures.

The rise of health apps and connected medical devices has been a game-changer in the medical industry because they provide patient-centered analytics. The parameters are set (in the apps or devices) to automatically trigger alerts and initiate a response from concerned healthcare givers when the problem is detected.

Video Analytics Aids in Surveillance

It is sad that while IT infrastructures evolve to make our lives easy and safe, the same thing turns dangerous as well. While devices are connected and they send you alerts and notifications, that could be the reason for harm as well – for security.

IoT analytics can detect anomalies and protect us from critical situations. Surveillance using video analytics can be used for preventing crimes and accidents.

Now enterprises no longer rely on closed-circuit cameras for protecting their premises from unwanted intruders. They can use video analytic techniques fitted with smart sensors, instead. When the video is combined with data, it is possible to glean far greater insights for predicting future events.

Video analytics help in understanding shopper habits when sensors on devices are placed in retail centers, they can prevent road accidents and traffic jams by pinpointing the time and location of maximum traffic and alerting the users/motorists. Video surveillance can also be placed in worksites to ensure worker safety or for improving security (through face recognition techniques).

Closing Thoughts

IoT analytics is the best choice for organizations with an eye for the future. Data from IoT is already at 5 quintillion bytes and will only continue to grow in the future. Furthermore, IoT devices are going to draw the data from several different sources, which makes it even harder to use because integrating data from heterogeneous sources is incredibly challenging. However, what IoT analytics does prove is that it is the solution most organizations need. The analytics solution is perfectly suited for the rigors of analyzing IoT data, giving organizations insight into operational efficiency, a better understanding of the market, and a much-needed competitive advantage.

If you are thinking of making an investment in IoT, now would be a great time. With our vast experience, we can assist you to fulfil your IoT needs. Contact us now.

0