Skip to main content


Ever since the pandemic, using Internet of Things (IoT) in business processes has become a tempting concept. Basically, this means that the physical device has its own CPU in the form of a microchip that can participate and collaborate in the execution of business processes. This is called the Enterprise IoT. These physical devices act as data collection points, where inputs from various machines involved in the process can be calculated, and then these devices can issue control commands based on the context of the inputs and applicable business rules. This results in reduced human intervention and higher process efficiency which is namely the USP of the enterprise IoT.

The IoT Proof Of Concept (POC) of sending device data to the cloud is relatively easy to build, but the challenges become more complex when deploying enterprise-level IoT on a large scale. These challenges include integration with OT (Operational Technology) managed factory systems and enterprise systems, data, and critical infrastructure security, gaining insights from device data, and accomplishing all of these without breaking the bank.

Compared with building a POC or a consumer IoT solution, the enterprise IoT environment poses more complex requirements. The POCs using consumer components will not be integrated with the company’s existing IT infrastructure but will run in their own silos, requiring them to have their own databases, networks, security, applications, and analytics. Although they can be assembled quickly, they are designed for one-time use and lack strict production quality testing, safety policies, maintenance plans, and continuous support.

In addition, these POCs usually have a 2-tier architecture, where data collection is at the edge, but processing/analysis and intelligence are in the cloud. Cloud service providers can easily connect to their services by providing pre-configured runtimes. However, factors such as cost, data privacy, and network latency make it impossible to expand deployment beyond POC. As a result, most POCs cannot scale, nor can they have any real impact outside of the business group that sponsors them. However, these POCs are still in their infancy and there are many challenges in implementing this concept.


Despite the massive technological advancement, even with the promise of newer and more advanced communication models, many people are still skeptical about the prospects of the Enterprise IoT. The main challenges of adopting Enterprise IoT are:

1. Lack of Highly Structured Data

Clearly defined and structured data is the foundation of any successful IoT implementation. This is because computers need clearly defined categories under which data should be collected. If there is no correct classification, all data will be garbled to the machine. Although the data is unstructured, further analysis of the data becomes very difficult. The data can be judged based on three main parameters: quantity, type, and speed. In addition, for data that is ready for calculation, it should be clean data without errors. However, in many places, data entry is still manual. This is where human error occurs, which in turn makes the machine unable to read the data.

Deciding which data to capture is also a major challenge. Providers provide specialized IT services to model your data, and you can always outsource database management plans to such third-party service providers. The amount of data and potential capture speed make data an important consideration for enterprise IoT adoption.

2. Use Edge Computing to Scale IoT

In some cases, device data needs to be processed in real-time, even if the network connection is lost, key business functions can continue to operate, which is a necessary condition for life-protecting safety systems or critical infrastructure. Edge computing can solve these IoT challenges by bringing data processing and analysis closer to the devices that generate the data. Device data can be prioritized and routed to when and where it is needed. Instead of all device data, just send a small portion of the data to the core site is used for long-term storage or further processing.

Artificial Intelligence and Machine Learning (AI/ML) enable companies to proactively discover potential problems in real time so that they can take corrective actions to improve product quality and reduce potential downtime through predictive maintenance. This requires data scientists to develop, test, and deploy ML models for predictive maintenance. These models are then deployed to perform real-time inferences at edge sites. IoT solutions need to reflect the best practices of modern IT environments based on containers, kubernetes, agile development, AI/ML, and automation.

3. Consistent Approach Covering Multiple Use Cases

Another major challenge facing enterprises is to identify the right use cases for higher return on investment in IoT. At the macro level, IoT seems to solve many optimization problems. However, at the micro-level, when this idea needs to be implemented, it is difficult to determine which exact processes can be automated using IoT, and which processes should be left as they are. This will require a lot of investment to study the white space that can be inserted using IoT.

Using a modular approach to build edge architectures can flexibly deploy any of the components shown in meaningful places – from core data centres to remote edge sites to meet the needs of various use cases including IoT. By using open APIs, developers can access data programmatically. Likewise, IoT with open-source components will allow companies to build vendor-agnostic solutions that best suit their needs from the core to the edge.

4. Analytical Modelling

In order for IoT to work properly, an accurate data model is needed to actually analyse the data and make decisions. If there is no model to analyse the data, the data itself is worthless. This means that a good model must be developed to meet the expectations of IoT implementation. However, this is a time-consuming and expensive process. Every company has a different mode of operation, and each business function and process is a special case. Different stakeholders and different devices interact with each other in different ways. However, it is time-consuming and expensive to build a customized model for a specific situation. This is why there is a need for a universal model that can work in various situations. Until a general model suitable for various use cases can be made, the prospects for IoT in the enterprise seem bleak.

However, this is only half of the problem. After a good model is realized, it must go through various stages of testing and repetition before it can become sufficiently accurate and accurate to achieve reliability. A small defect in the model may have a chain reaction, in which a false positive or negative at each stage will cause the entire production line to perform the wrong operation.

5. Quantity, Complexity, and Security

Another major problem with the adoption of IoT is the type of different devices. Many devices share architecture, but many other devices from different OEMs have very different architectures and operations. This is a major consideration when devices communicate with each other because the control signal sent by one device may not even be interpreted as a valid command by another device. This problem is very similar to the early stage of the Internet before the homogeneous communication of the general protocol. This is where a common interface is needed, which can parse the incoming commands and output the converted commands to the next device in the process.

Data security is also one of the most pressing issues for any organization. For the enterprise IoT to be a viable option considered by the company, it must have stronger security measures. These are essentially devices, have their own ecosystem, and communicate with each other via the Internet. Therefore, concerns about possible security breaches are very effective concerns. With the continuous development of the enterprise IoT, suppliers, original equipment manufacturers, and customers must all work together to establish reliable security protocols and guidelines.


Enterprise IoT has great potential to disrupt the way business is conducted. The true benefits of the correct implementation of the enterprise IoT are now recognized by most business leaders, and enterprise IoT plans are being carefully considered in the strategic roadmaps of various organizations. However, there are many challenges to successfully adopting this technology, some of which are issues of serious concern. However, as the way we process and analyse data continues to evolve and the technology industry makes more progress, the enterprise IoT may become the next big event in the industry.

Leverage Pratiti’s expertise in IoT development to co-create digital solutions at speed. Reach out to us today to engineer your digital journey today. We are an innovative product development company that possesses rich expertise in crafting and delivering software solutions using digital technologies. The Pratiti team helps their customers realize value while working with integrity, certainty, and insight; thus becoming the trusted technology partner in the customers digital transformation journey.

Our Services

Digital Product Company | Healthcare Software Development Services | Industrial IoT Solutions | Digital Twin Platform


Nitin Tappe

After successful stint in a corporate role, Nitin is back to what he enjoys most – conceptualizing new software solutions to solve business problems. Nitin is a postgraduate from IIT, Mumbai, India and in his 24 years of career, has played key roles in building a desktop as well as enterprise solutions right from idealization to launch which are adopted by many Fortune 500 companies. As a Founder member of Pratiti Technologies, he is committed to applying his management learning as well as the passion for building new solutions to realize your innovation with certainty.

Leave a Reply

Request a call back