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IoT Analytics: Benefits, Challenges, Use Cases & Vendors [2024]

Cem Dilmegani
Updated on Jan 11
4 min read
IoT Analytics: Benefits, Challenges, Use Cases & Vendors [2024]IoT Analytics: Benefits, Challenges, Use Cases & Vendors [2024]

By 2020, it is expected that there will be between 20 and 30 million IoT units in the marketplace, according to a study conducted by Gartner. With this much data being collected, the need for a way to analyze it grows exponentially. Many of the enterprise applications for IoT analytics, such as in manufacturing, finance, telecom, healthcare, and others have unlimited potential when data is managed and analyzed correctly.

To meet this need, IoT analytics has emerged as the broader category of uses and applications designed to help analyze the data obtained by IoT sensors. Once this data has been properly analyzed it can then be used to help make better, data-driven decisions for organizations that are in search of a competitive edge.

Benefits of IoT analytics

There are a wide range of benefits that can be realized with the help of IoT analytics. The biggest is the actionable intelligence and insights that they bring. This can in turn lead to:

  • Increased visibility and control, leading to faster decision making
  • Scalability and growth in new markets
  • Reduced operational costs through automation and smarter utilization of resources
  • Creation of new revenue streams through the resolution of problems and challenges
  • More accurate attribution of problems, leading to better solutions – faster
  • Faster problem solving and prevention of recurring problems
  • Improved customer experience through personalization based on previous purchases
  • Product development

Source: Gartner

There are a number of applications and use cases for IoT analytics.

For industrial and manufacturing:

Predictive maintenance: For manufacturers and similar, taking data from a sensor and building a model around it to better anticipate when equipment may need repair can be hugely beneficial. Elevator manufacturer ThyseenKrupp has been following this methodology and has found that it not only prevents downtime, but aids technicians in finding the right cause of the problem faster.


  • Process: With smart tools and components, businesses can collect usage data in order to locate strengths and weaknesses and adjust accordingly.
  • Quality: Fero Labs explains that testing products in capital investment heavy industries is costly. Tests require production samples to be sent to laboratories which is a slow and manual process. Instead, companies can use sensor data to predict quality issues and identify the correct set of inputs for quality optimization.
  • Cost: For example, industrial analytics vendor Fero Labs helps manufacturers optimize energy usage

Industrial infrastructure security: Take for example a warehouse that remains under surveillance overnight. IoT sensors on motion detectors can ‘learn’ how much activity counts as an ‘event’ and alert human operators if something happens that exceeds this threshold. Over time, as more data is collected, the effectiveness of the anomaly detection based on IoT sensors only stands to improve. This is because machine learning grows better with data without the need for human intervention to set complex rules as to what counts as an event.

For marketing and sales:

Social analytics: By combining sensor data, social media, and video data, event managers can enhance the experience of participants based on subtle and rapid changes in things like facial movements and body language. IoT sensors make it work through a form of analytics known as ‘sentiment analysis’, supported by cameras as rich data sources, coupled with biometric sensors on key actors in such events, such as coaches in the case of live sports.

For consumer products:

Streaming analytics: Continuous data processing requires continuous data collection. This type of analysis will become increasingly common when it comes to endeavors such as self driving cars that need to be able to respond in an instant when something changes.

Consumer product usage: Many products today are connected and provide data for manufacturers about how they are used. Having this understanding of what happens to products after they arrive in their final destination can help businesses to create products that are more useful, more effective, and ultimately sell better. This can also help with marketing and sales efforts when combined with data collected on buyer and audience demographics and information.

Challenges to implementation

While the benefits of IoT analytics are clear, sometimes their implementation can come with difficulties. Some of the biggest challenges associated with IoT analytics include:

Time series and data structures: The sensors supported by IoT analytics often receive tons of static data that doesn’t mean much until something happens to change it. The relationship between long periods without change and what causes an event can be difficult to ascertain and use in our diagnostic or predictive efforts.

Balancing scale, speed, and storage: Finding the right balance between storing enough data, analyzing it quickly enough, and the ability to scale these two processes with your business can be difficult – particularly when it comes to data that is highly time sensitive. This is particularly true when storing enough data to have historical comparisons can meet that the requirements to hold on to it all are getting increasingly hefty – and then too also need to be managed and secured.

Getting the right talent to manage it all: IoT analytics require developers, database specialists, data scientists, data processing specialists, and a range of other highly specialized and in-demand skillsets.

IoT analytics vendors

The number of platforms for IoT analytics is growing daily. A few vendors are outlined in the chart below, some of which you may already be familiar with. Additionally, there are a number of features that many businesses look for, including:

Data blending: Combining data from multiple sources into a functioning and useful data set.

Rules engine: Software executing one or more business rules in a runtime production environment.

Device shadow: JSON document used to store and retrieve current state information for a selected device.

NameProduct launchFundingCapabilities
Arcadia Data2012$11.5M
-Multiple analysis cycles daily giving direct access to data -Data insights through a secure integrated data management platform
AWS IoT Analytics2017PublicPredictive analytics and visualization
Fero Labs2015PrivatePredictive analytics and visualization
Hitachi Lumada2017Public-Predictive analytics
-Energy procurement and management
Microsoft Azure Stream Analytics 2015Public
-Real time dashboards for instant decision making -Out of the box functionality that lowers the need for developers
For consumer analytics: -Improved engagement and decreased churn -Diagnostics, usage, and predictive trends For industrial analytics: -Install base insights and condition monitoring -Asset diagnostics, including capacity and utilization
-Search, monitor, analyze and visualize machine generated big data from multiple sources -AI to detect anomalies and predict outcomes
-Temporal Analytics Engine for all data types and cycles -Real time, historical, prescriptive, predictive, and software analytics

It’s clear that IoT analytics are here to stay and that businesses who aren’t using them, are only missing out on the wealth of data and information they empower. Interested in learning more about IoT analytics and other key technological advances that are changing the way we do business?

Further Reading

Check out our researches for a wide range of IOT/AI related topics

If you want to leverage IoT in your business, you can check out IoT software, IoT analytics platforms, and IoT companies.

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Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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