A short discussion about the traps you might fall in when developing IoT services for your product on your own.

Peter Kesch, MBA
EMEA Business Development, Vice President
(peter.kesch@belladati.com)

About the Author

Peter is a seasoned manager with more than 25 years of experience in IT consulting, product management, business process improvement, business development, restructuring, and team building. Throughout his career, he has worked in both executive positions and startups, always focused on driving effectiveness, agility, and sustainable profitability.

With a strong IT background, Peter is able to communicate fluently with consultants and developers alike, while his extensive business experience ensures that teams and organizations stay aligned with economic goals and measurable outcomes.

At BellaDati, Peter is responsible for Business Development across the EMEA region. His deep understanding of technology, analytics, and digital transformation makes him the ideal person to discuss why IoT services have become a cornerstone of modern Business Intelligence strategies.

The following article explores the growing importance of IoT, highlighting the ten most common traps companies encounter when building their own IoT frameworks—and how to avoid them.

IoT services are a “must-have”!

In today’s industrial IT landscape, the role of data has changed dramatically. It is no longer sufficient to run machinery and factory systems in isolation. Customers expect their production units, machines and equipment not only to work, but also to communicate, provide information, and generate insights. The Internet of Things (IoT) has become the enabling technology that connects the physical world with digital intelligence.

The expectations of customers are clear. They want to monitor their machines in real time, receive alerts when unusual behavior occurs, compare performance across factories, and even predict failures before they happen. They want this information to be accessible at any time, on any device, and in a form that allows them to take action quickly.

For product companies, this raises the fundamental question: should IoT capabilities be developed internally, or should they be integrated from an existing IoT framework?

At BellaDati, we have a clear view on this question:

“Integrate an existing IoT and analytics framework – and select BellaDati ☺.”

However, the decision is yours to make. Based on our experience, here are the Top Ten Traps you might fall into when developing IoT services for your own product on your own.

Trap No. 1: Simple Connectivity vs. Real Insights

When companies first consider IoT, the initial idea is usually straightforward: connect devices, stream sensor values, and display them. This seems easy. With today’s communication modules and charting libraries, adding basic connectivity to a machine and showing values on a screen is no longer a big task.

The trap lies in assuming that this is enough. Customers today expect more than raw values. They want to interact with the data, compare machines, identify trends over time, and combine IoT signals with other information from their operations. Simply presenting numbers without the possibility to filter, correlate, or analyze them quickly becomes insufficient.

Imagine a factory manager who can only see temperature values from machines, without the ability to drill down by shift, compare with maintenance logs, or set thresholds for alerts. What begins as an impressive showcase turns into frustration, as customers expect much more flexibility.

Recommendation:
If your customers are satisfied with static values and limited functionality, building basic connectivity may be sufficient. But if they expect true insights, flexibility and interactivity, you should not underestimate the effort – integrate an existing IoT framework.

Trap No. 2: Data Quality & Consistency

In theory, sensor data should be accurate and reliable. In practice, it often is not. Devices may send duplicate values, experience dropouts, or generate noisy signals. In addition, data from different devices may arrive at different times or in different formats, making direct comparison difficult.

If you simply collect raw sensor streams and display them, customers may be misled. Spikes in vibration data might be caused by a communication glitch rather than a real mechanical problem. Reports created at different times of the day may not match, because they are based on live feeds that constantly change. Over time, trust in the data declines, and customers stop relying on your IoT service for important decisions.

A typical example is a production line with dozens of sensors, each with its own sampling rate and transmission delay. Without cleansing and alignment, combining these values creates a confusing picture rather than a clear analysis.

Recommendation:
Plan for data consistency from the beginning. Implement mechanisms for cleansing, normalization and alignment of IoT data. Provide consistent datasets for analysis, not raw streams, to ensure trust in the results.

Trap No. 3: Lack of Data Context (Enrichment)

Raw sensor values on their own are of little value. A number such as “85” could mean temperature, pressure, or vibration – without context, it is meaningless. Customers want to know which machine the value belongs to, under what conditions it was recorded, and how it relates to other events in the process.
Enrichment goes beyond simply explaining data — it means enhancing it with business context and generating actionable insights. Knowing the current status of a machine is useful; understanding additional factors such as its actual processing duration or standby time is what truly creates value.

If IoT data is not enriched with metadata such as machine ID, production batch, location, or operator, it remains isolated. Customers struggle to make sense of it, and insights are limited. It is only through enrichment that data becomes information, and information can become knowledge.

Consider a factory where sensors measure energy consumption. Without linking this data to production schedules, machine types or product types, it is impossible to optimize efficiency. The values are there, but the meaning is missing.

Recommendation:
Always combine IoT signals with business and process context. Enrichment is not optional – it is what makes raw data useful for analysis and decision making.

Trap No. 4: Underestimating Data Volume & Performance

IoT systems produce vast amounts of data. A single production unit with multiple sensors can generate millions of data points per week. If this data is stored in transactional systems or analyzed without optimization, performance issues quickly arise. Reports slow down, dashboards freeze, and core business systems may even be affected.

The trap is assuming that existing infrastructure can handle IoT workloads. Traditional relational databases are optimized for transactions, not for continuous streams of time-based sensor data. Trying to use them for IoT can overload systems and lead to unacceptable performance for both analytics and operations.

A generic example is a company that logs sensor readings directly into its ERP database. At first this works, but as data grows, the ERP slows down, impacting everything from order processing to invoicing. What started as an add-on IoT function becomes a risk for core business processes.

Recommendation:
Keep IoT data separate from transactional systems. Use data stores and analytics engines optimized for time-series and high-frequency data. Plan for scalability and performance from the beginning.

Trap No. 5: Neglecting IoT Security & Permissions

Each connected device represents a potential vulnerability. IoT expands the attack surface, as devices are often distributed, may run lightweight protocols, and are not always updated regularly. At the same time, the data collected by IoT services can be highly sensitive, reflecting production performance or even intellectual property.

The trap is assuming that existing IT security is enough. Standard authentication and access control for enterprise systems may not apply directly to IoT devices or their data streams. Without careful design, devices can be compromised or sensitive data can be exposed.

In addition to external risks, internal access also matters. Managers may require aggregated views across factories, while operators should only see detailed information for their own machines. Without fine-grained permissions, either too much information is shared, or too little.

Recommendation:
Design IoT security and authorization as an integral part of your solution. Ensure secure device communication, encrypted data streams, and a flexible permission model for different user roles.

Trap No. 6: Siloed Systems & Integration Challenges

IoT data rarely exists in isolation. For valuable insights, it must be combined with information from ERP, MES, CRM, or other enterprise systems. Customers expect to see complete pictures, such as how machine performance impacts delivery times or quality outcomes.

The trap is developing an IoT service that cannot integrate with these systems. Without integration, IoT remains a stand-alone dashboard that shows interesting numbers but does not support real business decisions.

Imagine a company monitoring machine uptime without connecting it to maintenance records. They may see frequent stops, but without knowing which maintenance actions were performed, they cannot understand the real cause. Integration is essential to move from data to insight.

Recommendation:
Keep your IoT service open. Support industry protocols and provide APIs for integration with enterprise systems. Ensure customers can combine IoT data with other sources as needed.[1] [2] [3]  Always remember, the communication has to be both ways: IoT information has to be collected and IoT information has to be distributed.

Trap No. 7: Ignoring User Interface & Tool Integration

Collecting and analyzing IoT data is one part of the challenge – presenting it to users in a usable way is another. Customers expect intuitive dashboards, easy-to-understand charts, and even integration into tools they already use, such as Excel, PowerPoint or mobile apps.

The trap is focusing on backend data collection while neglecting the frontend. If customers cannot easily work with the data, they will create their own workarounds, copying values into spreadsheets or building shadow reports. This undermines the value of your IoT service and leads to fragmented information.

A typical scenario is a maintenance manager who needs a chart for a meeting but cannot export it properly. Instead, he takes screenshots or manually retypes numbers, losing time and introducing errors.

Recommendation:
Invest in user-friendly interfaces and tool integration. Provide dashboards, exports and mobile views that match customer expectations and daily workflows.

Trap No. 8: Poor Data Accessibility & Alerting

IoT is not only about storing data – it is about delivering the right information at the right time. Customers expect to be notified when something important happens, not only to discover it later in a report.

The trap is focusing solely on data collection without building proper alerting and distribution mechanisms. Without alerts, issues may go unnoticed until it is too late. Without flexible accessibility, users may not be able to act when they are away from their desks.

Consider a machine overheating during the night. The data is collected, but without alerts, nobody notices until the morning. Production is stopped, and costly delays occur. This could have been avoided if an alert had been sent instantly to the responsible technician.

Recommendation:
Ensure your IoT service includes alerting and flexible distribution channels. Provide notifications via email, SMS or mobile push, and make dashboards available securely across devices.

Trap No. 9: Lack of Automation & Resilience

IoT services handle continuous data streams from many devices. If these processes are not automated, the workload becomes unmanageable. Manual imports, restarts after errors, or inconsistent updates make the system unreliable.

The trap is underestimating the need for automation. Devices will go offline, networks will fail, and data will arrive late. Without automated retries and fallback mechanisms, data is lost and trust in the system declines.

For example, if a sensor loses connection for several hours, the data should be buffered and synchronized later. Without automation, gaps remain and analyses are incomplete.

Recommendation:
Implement automation and resilience for data handling. Ensure that ingestion, transformation and recovery processes run automatically and reliably.

Trap No. 10: Underestimating Maintenance & Evolution

Developing IoT services is not a one-time project. Once customers start using them, they will request new features, better analytics, and integration with new devices. Devices themselves need firmware updates, platforms evolve, and protocols change.

The trap is to believe that once IoT connectivity is implemented, the task is complete. In reality, the work has only begun. Without continuous investment, your IoT service will quickly fall behind and lose competitiveness.

Just imagine a company that launches a monitoring service for machines but fails to add predictive features when customers ask for them. Competitors catch up, and the service becomes irrelevant.

Recommendation:
Plan IoT as a long-term journey. Allocate resources for ongoing maintenance, updates and new features. Only this way can IoT services remain valuable over time.

Summary

At first sight, developing IoT services for your product may seem easy. Add connectivity, show some charts, and you are done. But once you look closer, the complexity becomes visible. Data has to be cleaned and enriched, systems have to scale, security must be ensured, integration has to be provided, users expect friendly interfaces and alerts, and maintenance is a continuous task.

If you only want to provide simple connectivity and static charts, you might manage on your own. But if you aim to deliver professional IoT services with scalability, flexibility and long-term value, you should think carefully about building everything yourself.

By choosing an IoT framework like BellaDati, you can avoid these traps and accelerate the development of a production-ready IoT solution.

At the end of the day, the decision is up to you ☺.