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Unlocking the Business Value of IoT Data

Ivan Đikić — Mon, 25 Aug 2025 15:01:08 +0000

As IoT devices generate more data than ever before, the question is no longer how to collect it – but how to use it. From real-time monitoring to predictive analytics, the real value lies in what businesses do with the data next.

At the core of every Internet of Things (IoT) device lies an embedded system. Sensors, actuators, and similar components give these devices the ability to observe, measure, and interact with their surroundings. Combined with advanced connectivity, they generate and transmit vast volumes of data continuously.

But it’s not just the volume – this data is also incredibly rich and complex. The data harvested by IoT devices captures intricate patterns and subtle nuances, revealing details about the world in ways we couldn’t access before.

From smart home temperature readings and fitness tracker heart rate data to logistics fleet performance metrics and real-time outputs from industrial plants, each data point holds the potential to reveal valuable insights – insights that drive smarter decisions and greater efficiency.

However, the real power of IoT doesn’t lie in just collecting and transmitting data. It lies in analysis. By applying advanced analytics, we unlock new opportunities: predictive maintenance algorithms detect failures before they occur, machine learning models reveal hidden behavioral patterns, and real-time analytics enable instant decision-making in critical situations.

Real-time monitoring for smarter decisions

One of the most transformative applications of IoT is real-time monitoring. The use cases can range from tracking the efficiency of industrial machinery and managing energy use in smart homes to patient health monitoring or gauging traffic flow in smart cities. By providing live data, IoT enables stakeholders to make quick, informed decisions that could greatly impact efficiency and performance.

If we add ML and AI to the mix, an IoT system can predict potential failures or maintenance needs. It’s a shift from reactive to proactive maintenance that helps reduce downtime, lower costs, and maintain product quality.

Take industrial machinery as an example. Each machine can be equipped with numerous sensors to measure different performance parameters. These could include temperature, pressure, humidity, vibration, power consumption, RPM, throughput, and more, depending on the nature of the machine.

These sensors feed data into a transmission device – typically an IoT gateway – which forwards the data to a central server, forming the foundation of an effective IoT data pipeline. Depending on location and infrastructure, this can be done via wired networks, wireless protocols, or even satellite communication in remote areas.

Once the data reaches the server, it’s processed and stored. Algorithms compare incoming data against predefined thresholds. If a value falls outside the acceptable range, the system automatically sends alerts to designated engineers or managers via email or SMS.

To visualize this in real time, an interactive dashboard can be developed, accessible through a web browser or dedicated mobile app. It would present charts and graphs for each performance metric, along with historical data to help identify trends or anomalies.

With machine learning and AI in place, the system can go a step further – predicting potential failures or maintenance needs based on subtle patterns. This enables proactive measures to be taken before a problem arises, reducing downtime and costs.

By implementing such a system, businesses could significantly enhance their ability to track and optimize machine performance, leading to improved productivity, lower costs, and better product quality.

Personalization with IoT data

Another valuable use of IoT data is personalization. Each machine continuously generates data about its own usage and performance. This data can be used to adjust operations to fit individual performance profiles – either manually or automatically.

Machine learning models can be trained on historical data to define optimal operating conditions. If the real-time data signals a deviation, AI can step in to make immediate adjustments.

In manual setups, operators monitor real-time parameters and adjust settings themselves. For instance, if a dashboard shows a machine running too hot, the operator can reduce the load or activate cooling. This approach requires skilled personnel who can interpret data and act accordingly.

To automate this process, machine learning models can be trained on historical data to define optimal operating conditions. If the real-time data signals a deviation, AI can step in to make immediate adjustments. For example, if a machine typically performs best at a specific temperature, AI can activate the cooling system when the temperature rises.

To enable this, AI would need control over certain aspects of the machine’s operations. This can be achieved by integrating AI within the machine’s control system. However, it’s important to note that giving AI control over machinery could have safety implications, so it’s crucial to have safeguards in place to prevent accidents.

Optimization at scale

The immense pool of IoT data available offers a valuable foundation for large-scale optimization. Used effectively, it can help businesses cut costs, improve efficiency, and boost safety.

Data-driven energy management can unlock major savings in energy-intensive industries by revealing usage patterns, exposing inefficiencies, and guiding targeted improvements.

One of the best optimization examples can be found in energy-intensive industries. Here, data-driven energy management strategies can lead to significant cost savings. By closely monitoring usage trends and anomalies, companies can gain insights into periods of peak and low energy demand. This information can help identify inefficient equipment or processes and facilitate necessary modifications. The detailed understanding of when and where energy is being used can be invaluable for making targeted changes to optimize energy use and reduce waste.

Machine learning brings additional power, using IoT data to optimize business processes through accurate forecasts and actionable insights. Trained on historical energy data, predictive models can forecast future energy demands with a high degree of accuracy. This capability enables organizations to align their energy generation or purchase plans with anticipated needs, helping to avoid both energy wastage and unexpected expenses. These models can also detect patterns that may not be evident to human analysts, leading to innovative energy-saving strategies.

Benchmarking is another essential element in optimizing energy usage. By comparing a facility’s energy performance with similar facilities or industry standards, organizations can assess their energy efficiency in a broader context. This can help identify performance gaps and set ambitious but achievable energy management goals. Additionally, it can inspire the adoption of best practices from top-performing peers, leading to continuous improvements in energy efficiency.

Lastly, regulatory compliance is an essential aspect of energy management in many industries. Many industries are required to monitor and report energy use. An automated system can ensure data is tracked in real time, generate reports as needed, and even flag potential compliance issues before they escalate. This not only reduces the risk of penalties but also allows organizations to focus more on strategic energy management initiatives.

Making IoT data work for you

Harnessing IoT data effectively can transform how businesses operate, compete, and innovate. Real-time monitoring, personalized automation, and system-wide optimization are no longer futuristic concepts – they’re practical tools available today. Organizations that know how to extract real value from IoT data will be better equipped to deliver for their customers, adapt to change, and lead in a data-driven world.

This article is adapted from our Guide to Successful IoT Implementation, where we cover the technical foundations and strategic considerations behind building modern IoT solutions. For more insights, download the full guide.

The post Unlocking the Business Value of IoT Data appeared first on Infinum.

New to Software Development? Four Technologies You Should Explore

Ivan Đikić — Fri, 24 May 2019 15:30:00 +0000

If you’re having a hard time deciding which technology you want to learn, you’re not alone. The vast majority of beginner engineers struggle with this decision. Fortunately, we’re here to help!

First and foremost – get informed! As you make this tough decision, it’s important to research the current state of the affairs in each technology of your interest.

No time for research? No problem! We’ve compiled details from various sites and sources to give you a general overview of the current state of iOS, Android, JS and backend development. What’s latest and greatest with each of these technologies? We’ve got all the info you need right here.

iOS development

Since its first release 11 years ago, iOS has matured quite a bit, and now supports two programming languages: good ol’ Objective-C and shiny, new Swift, which you can use to escape Java. And as you might have heard, you can also drag’n’drop the UI.

iOS has very good support for machine learning and AR, so it is up to you to build something cool. One of the main advantages over its competitors is security and low fragmentation. As Apple is also the one who controls the hardware, you can expect top notch performance.

After you become familiar with iOS, you can use that knowledge to build Mac applications or to dive deep into server-side Swift.

One of the disadvantages of Apple development is the entry cost which is much higher than in any other branch of technology; because not everyone has a Macbook lying around the house.

We fixed this problem by providing each student with a MacBook Pro for the duration of the Infinum Academy as well as a teacher who will show them all the tips & tricks there is to know. Now there is nothing in the way between you and your future iOS development career!

Android development

The first official Android version was released 11 years ago. Today, it is developed and maintained by Google and Open Handset Alliance. Even though you can’t contribute to the source code, Android is an open source project.
This means that the code is publicly available, so anytime you feel adventurous you can dive into it.

Since the first Android release, there have been 28 major updates. If you try to name all of them, you might get very hungry. Why? Because with the exception of the first release (which doesn’t have a code name), all the other names are confectionery-themed. This includes tasty titles like Lollipop, Marshmallow, Nougat, Oreo and, the latest, Pie.

The update names are released in alphabetical order. So, you can already start guessing what the sweet code name will be for release 29, starting with the letter Q.

The most difficult challenge you will come across with Android is fragmentation. Unlike on iOS, there’s no drag&dropping code nor are apps developed for one specific device hardware or OS version.

In Android, apps need to run on thousands of different devices, and the code is written to run on the largest device base platform, which counts in billions. Also, you will learn a new programming language, Kotlin, which is the official Android language since 2017.

Backend development

In the fast-moving world of web development, modern backend programming revolves around API. That means crunching data to quickly serve hungry clients on mobile and web platforms. Nowadays, the focus in backend development is on code structure, programming techniques, testability and performance.

An efficient developer should be armed with a wide range of skills, from understanding HTTP communication to fluently writing SQL and everything in between.

For building the backend system we embraced Ruby on Rails. Or was it the other way around? We’re not sure anymore because the main focus for Rails has always been developer happiness. It’s armed with domain specific language for each component in the system meaning configuration, tests, routes, migrations, object-relational mappings, and SQL queries are understandable and concise almost like a specification in natural language.

Besides aesthetics framework has a lot to offer to both beginners and seasoned web developers. Built for rapid prototyping, your next big application will be up and running in a matter of minutes. Rails is now at 6th major version and still one of the most popular web frameworks in the field.

Many of the niceties mentioned about Rails are owed to Ruby programming language. Similar to Rails, Ruby was created with simplicity and expressiveness in mind. It’s stripped of excess punctuation, like colons, curly braces around conditional statements and in some cases even parenthesis. With 3rd major version on the way, what can we expect? Well unofficially, Matz, the creator or Ruby shared a few goals, making current release three times faster, new concurrency model and type checking tool.

Oh, and both Ruby and Rails are open sourced. Not only are they free to use, but you can also learn from them and give back by contributing to their evolution.

JS development

JavaScript is still one of the fastest-growing programming languages in the world. To top it off, JavaScript is one of the top three most sought-after languages from a commercial
point of view. According to a Stack Overflow survey of more than 20,000 IT professionals, JavaScript is the most commonly used language.

Among the most popular libraries and frameworks, JavaScript-based tools occupy the first three places. With all that said, it is safe to say that JavaScript isn’t going anywhere anytime soon.

When it comes to frameworks, JavaScript has a lot to offer. There is no universal answer for picking the best framework, and not all projects benefit from using one.

Angular 2, and other versions leverages modern design principles, lessons learned from mistakes in Angular 1 (also known as Angular JS) and good practices from other frameworks. It might look complicated at first, but it is certainly worth picking up at some point. React is quite specific since it’s not a framework – it’s missing state management. That’s why you’ll hear its name in conjunction with Redux quite a lot.

There are lots of great frameworks like Vue.js, Ember, Backbone, etc. but nobody should bother to learn all of them. It is important to understand one and others will be much easier to master later.

Ready to learn?

There are pros and cons with each of these technologies, but you can’t go wrong with any of them! In reality, it depends on your preferences.

If these technologies spark your interest, and you want to learn how to build apps, you are in the right place! We’ll teach you for free. No joke.
Plus, at Infinum Academy, we have top lecturers who specialize in each technology; they know the ups and downs, ins and outs. One of our lecturers, Filip Gulan, was once himself a young apprentice. After he successfully finished the program, he was offered a job at Infinum. Today, he is an iOS Team Lead. He really came a long way, and so can you.

No matter what you choose, it’s important to keep learning. If you feel like Infinum Academy is up your alley, apply now. We can’t guarantee you’ll be the next Bill Gates, but we’ll make sure to teach you useful skills that will help you jumpstart your career.

The post New to Software Development? Four Technologies You Should Explore appeared first on Infinum.