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IoT Analytics: Benefits, Challenges, Use Cases & Vendors

By 2024, it’s anticipated that there could be anywhere between 60 to 70 million IoT devices on the market according to a research carried out by Gartner. With the amount of data being created in the market, the need for an approach to analyse it is growing exponentially. A lot of enterprise applications to IoT analytics, including in finance, manufacturing telecom, healthcare and many more, have endless potential in the event that data is properly managed and analysed correctly.

To address this requirement, IoT analytics has emerged as a broad range of applications and uses that are created to analyse the data collected through IoT sensors. After this data is correctly analyzed, it can be utilized to aid in making more informed, data-driven choices for businesses seeking to gain an edge.

There are many kinds of advantages that can be realized through IoT analytics. The most significant is the useful insights and actionable intelligence that they can provide. This could create:

Improved control and visibility, which results in faster decision-making
Growth and scaling in the new markets
Automation has reduced operational costs thanks to automation and better utilization of resources
New revenue streams are created through the resolution of issues and obstacles
A more precise attribution of the problem that leads to faster and better solutions.
Rapider problem solving and reduction of repeated problems
Enhance customer experience by personalizing Based on purchases made in the past
Product development

There are many applications and uses to use IoT analytics.

For manufacturing and industrial use:

Predictive maintenance: For companies and similarorganizations, obtaining the data of a sensor and constructing an algorithm around it to be able to anticipate the time when equipment will require repair could be extremely useful. Elevator maker ThyseenKrupp has been using this approach and found that it not just reduces downtime, but also assists technicians to pinpoint the source of the problem quicker.


Process: Using sophisticated tools and components, companies can gather usage data to identify the strengths and weaknesses of their products and adapt in line with the findings.
The quality: Fero Labs explains that testing products for capital investment in industries is expensive. Testing requires samples from production to be shipped to labs which is a lengthy and manual procedure. Companies can instead utilize sensor data to anticipate quality issues and determine the right quantity of inputs to ensure the best quality improvement.
Cost: For example, industrial analytics vendor Fero Labs helps manufacturers optimize energy usage

Security for industrial infrastructures For instance, the warehouse that is kept in constant surveillance for the duration of the night. IoT sensors in motion detectors will learn what constitutes an “event” and notify humans when something occurs which exceeds the threshold. With time, as more data is being collected to improve the accuracy of anomaly detection system based on IoT sensors is bound to increase. This is due to the fact that machine learning improves with the help of data without the requirement for human intervention to establish the rules of what is considered an event.

For sales and marketing:

Social analytics using sensors as well as social media and video, event planners can improve the experience for participants by analyzing subtle and swift changes in things such as the body language and facial movements. IoT sensors can help with a method of analysis called’sentiment analytics that is that is supported by cameras as data sources, and paired with biometric sensors that identify important participants in these occasions, like coaches for live sports.

Consumer products:

Streaming analytics: Continuous data processing requires continuous data collection. This kind of analysis will become more popular in projects like self-driving vehicles that have to be able to react immediately whenever something happens.

The use of consumer products is increasing. products are now connected and give manufacturers information regarding how they’re utilized. Knowing what happens to the products once they have reached their final destination will allow companies develop products that are more useful, efficient and ultimately, sell better. This could also aid in sales and marketing strategies when combined with data on demographics of the buyer and audience as well as other data.

Implementation challenges

Although the advantages from IoT analytics are evident but sometimes their use can be a challenge. Some of the most challenging issues related to IoT analytics are:

The time-series and the data structure: Sensors that are supported by IoT analytics typically receive a lot of data that don’t have any significance until something changes it. The connection between long time periods of time without any change and the factors that triggers an event is difficult to determine and utilize for our diagnostic or prescriptive actions.

The balance between speed, scale and storage: determining the perfect balance between storing sufficient data, and analysing it in a timely manner and being able for scaling these tasks to meet the needs of your business is difficult , particularly with regard to data that is extremely time-sensitive. This is especially true when the data you store to allow historical comparisons satisfy the requirement to keep all of it is becoming more large, and they must be controlled and secured.

Finding the right talent to handle it all: IoT analytics require developers experts in databases, data scientists, database specialists as well as data processing specialists as well as a variety of highly skilled and sought-after skills.

IoT analytics vendors

The variety of platforms available for IoT analytics is expanding each day. Some of the vendors are listed in the table below with some that you are already familiar with. There are also many things that businesses are looking for, for example:

Data Blending Data blending: Combining data from different sources to form a useful and valuable data set.

The rules engine is software that executes some or all business regulations in the runtime environment of production.

Shadow of the device: JSON document used to save and retrieve information about the current state of the device you have selected.

It’s obvious that IoT analytics will be around for a while and those who don’t use these analytics, are losing out on the vast amount of data and information they provide. Are you interested in finding out the more details about IoT analytics as well as other significant technological advancements that are revolutionizing our business practices?