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All About IoT Analytics

By 2024, it’s anticipated that there could be anywhere between 60 to 70 million IoT devices available in accordance with a study carried out by Gartner. With this amount of data generated and analyzed, the need for a method to analyze it increases exponentially. Many of the applications used by enterprises that use IoT analytics, like in finance, manufacturing telecom, healthcare and many more, have endless potential provided that the data is handled and analysed correctly.

To address this requirement, IoT analytics has emerged as a broad range of applications and uses created to analyze the data collected through IoT sensors. Once the data has been thoroughly analyzed, it is able to be utilized to make more informed, data-driven choices for companies looking for a competitive edge.

There is a variety of benefits that can be realised 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 scaleability in new markets
Automation has reduced operational costs thanks to automation and better utilization of resources
The creation of new revenue streams by resolving issues and obstacles
A more precise attribution of the problem that leads to more effective solutions and faster
Rapider problem solving and reduction of problems that recur
Personalization of customer experience by analyzing previous purchases
Product development

There are many applications and uses to use IoT analytics.

For manufacturing and industrial use:

Predictive maintenance for manufacturers and other similar companies, collecting the data of a sensor and constructing models around it to be able to anticipate the time when equipment will require repair is extremely useful. Elevator maker ThyseenKrupp has been using this method and found that it not just stops downtime but also aids technicians in determining the root of the issue faster.


Process: Using sophisticated equipment and components, companies can gather usage data to identify the strengths and weaknesses of their products and adapt to improve.
The quality: Fero Labs explains that testing products that require capital investment industries is expensive. Tests require samples of production to be taken to laboratories which can be a time-consuming and manual procedure. In contrast, businesses can make use of sensors to identify problems with quality and pinpoint the most appropriate combination of inputs needed for the best quality improvement.
Cost: For example, industrial analytics vendor Fero Labs helps manufacturers optimize energy usage

Security for industrial infrastructures Consider the warehouse that is kept under surveillance all night. IoT sensors in motion detectors will learn what constitutes an “event” and will notify humans when something occurs that is more than the threshold. As time passes, and more data is gathered to improve the accuracy of anomaly detection using IoT sensors is bound to rise. This is due to the fact that machine learning improves by utilizing data, without the necessity for human intervention to establish complicated rules for what is considered an event.

Marketing and sales:

Social analytics by combining sensors as well as social media and video information, event organizers 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 by utilizing a type of analytics called’sentiment analysis which is that is supported by cameras as data sources, and paired with biometric sensors that identify the key players in these events, for example, coaches when it comes to live sports.

Consumer products:

Streaming analytics: Continuous data processing requires continuous data collection. This kind of analysis will become more popular in initiatives like self-driving automobiles that must be able to react immediately when something is changed.

The use of consumer products is increasing. products are now connected and give manufacturers information regarding how they’re utilized. Understanding what happens to the products once they reach their final destination will allow companies develop products that are more useful, efficient and ultimately more successful in selling. This is also helpful in sales and marketing efforts when combined with information on demographics of the buyer and audience as well as other data.

The challenges to implementation

Although the advantages from IoT analytics are obvious but sometimes their use can be a challenge. Some of the most challenging issues that are associated with IoT analytics are:

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

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 functions in your company can be challenging, especially with regard to data that is time-sensitive. This is especially true when the data you store to allow historical comparisons be a challenge as the demands to keep the data are becoming increasingly large, and they must be controlled and secured.

Finding the right person to handle it all: IoT analytics require developers experts in databases, data scientists, database specialists and data processing experts, and many other highly-specialized skills that are in high demand.

IoT analytics vendors

The number of platforms that support IoT analytics is increasing every day. Some of the vendors are listed in the table below and some of them you might already be familiar with. There are also many things that businesses are looking for, such as:

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

Rule engine software which executes some or all business regulations in an environment for production runtime.

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

It’s evident that IoT analytics will be around for a while and companies that aren’t using these analytics, are losing out on the vast amount of data and information they can provide. Are you interested in finding out the more details about IoT analytics as well as other significant technological innovations that are revolutionizing our business practices?