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Five Internet of Things (IoT) Myths Busted

A recent report from the McKinsey Global Institute estimates that IoT will have an economic impact of $4 to $11 trillion on the global economy by 2025 in the healthcare, retail, and smart home segments.

According to a Consumer Technology Association (CTA) study, 83 million households and approximately 22 million homes today own more than one smart home product. 

IDC estimates that IoT spending in Europe will reach $202 billion in 2022 and will continue to grow at double digits until 2025.

Despite the global impact of the COVID-19 pandemic, the IoT market is still growing this year, albeit at a slower pace than in previous years. IDC expects this growth to continue in 2021 across various sectors as operations, projects and employees begin to get back on track.

In 2021, most of the spending has remained in the hardware category (modules and sensors) – the “Things”. However, from the next year on, the IoT data collection and analytics software category – the “Internet” – is expected to take center stage, with a growing number of IoT data applications hitting the market and booming.


What's still holding back IoT adoption?

As per different estimates, COVID-19 and the associated uncertainty will continue to impact some industries over the next several years, creating mixed industry metrics during the transition to the next normal.

The lack of compelling business cases in different industries and verticals is another barrier on the way to IoT mass adoption. Many SMBs are still looking at large enterprises with hefty budgets to pioneer the IoT technologies and generate value from IoT data collection. On the other hand, many enterprises in traditionally slow-adoption industries such as construction, transportation, or agriculture are waiting for innovative startups to hit the market with proven white-label solutions they can acquire and own without investing in their own IoT R&D.

One more factor that is still holding back IoT software mainstreaming is the myriad of myths surrounding the IoT space.

Through extensive conversations and discovery calls with our business prospects and existing customers, we at have identified five myths about IoT that still concern business leaders today and hold them back from investing in pilot and full-fledged IoT software projects.

In this article, we’re going to debunk these myths based on our firsthand experience with building and deploying high-performance IoT and Industry 4.0 solutions that help other businesses advance technologically, financially, and operationally.

Myth # 1: IoT is just an expensive high-tech dashboard

So why invest in it whereas there’re traditional and less expensive ways to collect and visualize data?

The truth is that IoT is a complete reimagining of value creation and a way to increase, improve and accelerate it through data.

Not only does IoT help companies collect and analyze data; it also helps turn it into useful insights for problem-solving and fast and informed decision making.

Consider, for example, how the speed of real-time data collection and analysis has dramatically accelerated the collection of information for epidemiologists and medical researchers dealing with Covid-19 treatments, testing methods, and vaccine options.

Although many companies are already collecting data, a lot of them still have no clue how to use it for robust business intelligence (BI).

Combined with data lakes and processing platforms, IoT automatically generates real-time analytics and alerts to enable users to identify trends and take actions much more effectively. Solutions and ideas can then be shared with other teams, locations, and applications, thereby increasing their value for the entire enterprise. When properly integrated, real-time data can be used in important innovations and strategies.

Deploying the Internet of Things intelligently requires a thorough understanding of the business and how it generates value. It also means knowing the company’s pain points so that it can invest in IoT where it matters the most and where it can realize its full value. Thus, where and how IoT is deployed is an important strategic decision that should be driven by the business strategy set by senior leadership.

Real-world case: How to achieve a 90% increase in customer insights through a custom IoT-based dashboard

One of the leading European retail companies has once turned to for help building an interactive real-time dashboard to solve the following internal issues:

  • Inability to define customer clusters by gender, age, and other breakdowns, resulting in inefficient marketing campaigns and slow follow-ups across departments and locations. 
  • Lack of understanding of customer behavior and immediate response programs to trigger predictive analytics and improve sales.
  • Lack of customer insights as a result of the above, resulting in business stagnation, lack of targeting, personalization, and more.


As a result of their cooperation with us, the company got a custom IoT solution allowing them to map each client’s needs and solve their most sophisticated requests.

Using CCTV cameras and video analytics servers (including Facial Recognition, Queue Detector, People Counter, Activity Visualizer and Analytics system packages), our IoT engineering team delivered a custom tracking device and dashboard providing the following types of data:

  • in-depth store visitor analytics, 
  • direction and heatmap analysis,
  • timeline comparison, 
  • information about the queue time and the number of people waiting, interval comparison and selection, etc.


This custom IoT-based dashboard allowed the company to see a 90% increase in customer insights, a 50% increase in the immediate response rate, and a 60% rise in the accuracy of customer patterns. 

As such, the value that the whole business has gained from IoT development is significant.

Myth #2: IoT demands software engineers with sophisticated and hard-to-find skills

While it’s true that a typical IoT development project requires skills in hardware, networking, security, software engineering and design, mobile application development, automation, data science, AI/ML/DL and Mixed Reality (XR), most of roles on the IoT team can be combined or developed from existing employee re-skilling or re-training.

To nail it even more, companies should take advantage of globalization and outsourcing, which provides access to the untapped pools of the IoT talent  and allows for faster recruitment cycles and overall turnaround. Leveraing external R&D teams specialized in IoT/hardware/embedded development also helps lower down IoT dev budgets and optimize expenses.

Employee training must take place along the entire value chain. For instance, companies will need more cost engineers experienced with cost modeling tools to estimate equipment and logistics costs in procurement.

The ability to identify opportunities for improvement requires training and education. Organizations can train employees to think and act differently, become more involved in problem-solving and develop local solutions that can then be disseminated across the enterprise across technical platforms for maximum impact.

Given the breadth of new skills needed for teaching specific technologies and new ways of working, some companies are developing programs in partnership with higher education institutions.

One of the main candidates for helping to secure IoT is quantum computing. However, the idea of ​​converging IoT and quantum computing is not a new topic, it has been discussed and covered by various researchers, but nothing comes close to practical applications so far. Quantum computing is not yet ready, and it is still several years before it can be implemented on a commercial scale.

However, progressive software companies like ensure their software engineering talent’s future readiness by introducing an internal Quantum Computing course. It’ll be led by one of our senior engineers currently working on a Deutsche Bank project. Its key goal will be to increase our employee’s understanding of Quantum and how it’ll help secure future IoT solutions. We want to ensure they can easily “plugin” when quantum computing is ready to go mainstream.

Myth #3: A successful IoT development journey requires 100% digital readiness

Not only is it optional to be 100% ready, but this is also impractical and possibly even counterproductive. In fact, it is enough to be digital and flexible to hit the IoT developer journey.

In many cases, companies spend too much time planning. More importantly, getting started right away with a digital transformation office. This central team oversees pilot projects and guides the organization through a seamless and fast learning process. Like an executive “engine,” the transformation office helps a company grow over time with proven methodologies, best practices, and a holistic leadership vision for transformation.

Agile working methods enable rapid development, refinement, and continual improvement. Agile enables companies to move in sprints and iterate quickly to quickly learn from their failures and create an even better product faster.

What’s important is that whenever the company considers outsourcing its IoT project development, it should make sure its potential technology partner is 100% digital-ready.

Here’re just some of the features of a digitally-ready IoT development partner:

  • Access to the newest technologies including AI/ML, deep learning and computer vision, edge computing and Cloud, 5G, NB-IoT/LTE-M, mixed reality (AR/VR/DR);
  • Own pool of IoT engineering talent;
  • Own R&D center for IoT/Embedded/Hardware;
  • Own reusable code/components/ML/DL models library;
  • Agile methodology, job parallelization approach;
  • Quality, continuous delivery and security certifications;
  • Portfolio of verifiable IoT projects.

Myth #4: Industrial IoT (IIoT) requires greenfield facilities

Some business leaders believe old, legacy facilities are an obstacle to digital transformation and that outdated equipment needs to be replaced. Certainly, new equipment will be required. But equating the IoT with completely new from-scratch sites capable of fully automated production is a big exaggeration. Much of the industrial IoT value lies in improving existing facilities: connecting and optimizing existing infrastructure and expanding it by choosing new equipment on an ongoing basis.

By adding sensors, applications, and connectivity to existing equipment, companies can collect data and transform it into business intelligence that is available to employees right at their fingertips. The Internet of Things and new technologies can help employees manage results at all value chain stages.

Let’s take a connected car as an analogy. Before the advent of sensors, drivers had little understanding of what was happening under the hood in real-time. Today, dozens of sensors in the digital cockpit collect a wealth of data about the engine, transmission, suspension and other locations and transmit it to the interactive real-time dashboards.

Drivers can now anticipate and fix problems on-site to optimize their vehicle performance with the lowest possible margin of error.

Myth #5: IoT projects require a considerable upfront investment

Contrary to popular belief, implementing an IoT strategy and taking advantage of Industry 4.0 does not require a significant upfront investment. Companies can facilitate smooth transformation by starting small and letting their business cases drive technology adoption (rather than the other way around).

Other keys to ensuring the long-term success of an IoT strategy include using an open and scalable cloud platform for analytics and adopting a security-by-default approach to digital infrastructure deployment.

For example, smaller manufacturers do not always have the resources for large initial capital investments in large equipment. Engaging with an OEM that provides industrial capabilities as a service is changing that – opening up new business opportunities.

Most of the custom IoT projects we do at start small and scale as the customer begins to benefit from the business with their experimental solution. As a strategic IoT technology partner, we do our best to lower each client’s project development costs.

We achieve this by: 

  • leveraging appropriate tax incentives for cloud computing and R&D,
  • leveraging our reusable code library and cost-effective deep learning models,
  • distributing development across multiple low-cost locations and so on.

Looking to build a custom IoT solution?

Leverage our R&D Center for IoT & Embedded Systems!

Domains & Solutions

  • Machine Learning, Deep Learning
  • Virtualization & Cloud
  • Smart Conf Room – Sentiment Analysis
  • IoT Devices & Protocols
  • Wireless Modules
  • BLE Bluetooth Low Energy
  • Device Efficiency
  • Secured Connectivity
  • ARM processors
  • Augmented & Mixed Reality
  • Modified kernels for FaaS
  • Long and Short Range Connectivity
  • M2M Applications

Tools & Frameworks

  • Docker, Kubernetes, MicroServices, FaaS, AWS, Azure, OpenStack, OpenVINO, OpenCV, CUDA, AR Core, AR Kit, Vuforia
  • Unity, WebGL
  • Sensors, Actuators

Protocols & Standards

  • NBIoT, LTE, UMTS, GSM, LoRaWan

Final Thoughts

A Beecham Research IoT User Survey found that 96% of executives involved in successful IoT projects rated the following three factors as important or very important to success with your projects:

  • clarity in business strategy,
  • senior management engagement, and
  • vailability of technical and financial resources. 


It is also clear from Beecham’s research that close collaboration between an in-house team and experienced suppliers is at the heart of many successful projects.

In 49% of successful projects, the development team combined expert solution providers and internal IT resources. In contrast, among unsuccessful projects, 57% relied only on internal resources, and only 17% mixed an internal team with external experience.

This is confirmed by our own experience as a custom IoT software developer.

Many clients come to us with a failed or stalled project, often managed in-house. It is a common mistake to think that the communication part of a project will “just work”, and very little attention is paid to this element in planning and development. The project is written off as failed because data is not being collected and transferred properly. Devices are switched on and off from the network, information gaps appear, and an incomplete picture is obtained, which devalues ​​the entire project.

The number one reason for these failures is poor initial project design and insufficient time, resources, and experience to plan a network with clear results. Not enough attention is paid to IoT connectivity or issues. The belief that all of this can be handled in-house using open-source software and standard connectivity is at the heart of many project failures.

Most IoT project managers elude how much the technical complexity increases as IoT deployments scale. This process is clearly not linear and goes to the point of quickly breaking through internal systems, stressing the support organization, degrading the customer experience, and ultimately hurting the brand. Even a project’s most precise business goals become irrelevant if data is not collected efficiently and reliably.

IoT projects can be complex and costly but not as expensive as mistakes. And that’s so true!

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