Internet of Things
Transforming field data into an actionable asset for operational performance, data, and AI
The Internet of Things, or IoT, encompasses all technologies that allow sensors and connected objects to send data from the field back to information systems. This data can relate to industrial equipment, buildings, networks, vehicles, or infrastructure, and can be processed in real-time or continuously to monitor, control, and optimise operations.
The challenge is no longer limited to connecting objects. It's about making the data collected reliable, integrating it into the organisation's information assets, and making it usable for concrete applications. It is this combination of IoT, data platforms and artificial intelligence that transforms field data into lasting business value.
The IoT has become strategic because many organisations need a more nuanced, faster, and more concrete view of what is actually happening on the ground. In industry, in buildings, in infrastructure, or in logistics, the ability to capture continuous signals allows for a better understanding of usage, anticipation of certain deviations, optimisation of resources, and reduction of downtime or extra costs.
Feedback shows that these systems can produce tangible gains, provided they are conceived beyond simple technical deployment. Reduced maintenance costs, improved productivity, energy optimisation, or better service quality: the benefits exist, but they depend heavily on the ability to structure data and connect it to the rest of the information system.
This is often where projects encounter their limits. In many cases, IoT data remains locked within specialised platforms, used for local monitoring but rarely cross-referenced with other company data. Consequently, it struggles to feed into more advanced analyses, overall management tools or higher-value AI applications.
A purely technological approach is therefore not enough. Deploying sensors, connectivity and an IoT platform does not, in itself, guarantee value creation. To be sustainable, IoT must be considered an integral component of the data and AI strategy. This implies addressing data governance, integration with existing architectures, security, scalability and, above all, the business use cases that justify the investment. It is this logic that enables the transition from a proof of concept to industrialisation.
How does this expertise translate at JEMS?
The IoT is approached as a strategic data source to be sustainably integrated into the data and AI value chain, from sensor to business use. With this approach, we link the IoT, data, and AI to transform field signals into concrete, robust, and industrialisable uses.
Consulting
We intervene at the early stages to define use cases, analyse existing systems, assess IoT maturity, and establish a roadmap adapted to business, data, security, and architectural requirements.
Architecture
We design complete devices integrating sensors, actuators, gateways, connectivity, networks, IoT platforms and display interfaces, with a coherent and sustainable approach.
Integration
We connect field data to the company's data platforms to avoid silos and enable its use in dashboards, advanced analytics, and, where relevant, in artificial intelligence systems.
Industrialisation
We support scaling up, governance, data quality, security, and adoption, in order to embed IoT projects in a sustainable logic of operational performance and scaling.
Business Value
A well-structured IoT approach allows for a better understanding of the field, improved operational responsiveness, and more reliable decision-making. When integrated into the company's data platform, data from connected objects becomes usable well beyond simple supervision.
They can then power maintenance, energy optimisation, industrial control or logistics monitoring uses, with more sustainable and measurable value.
Field data is becoming accessible and usable on a large scale.
Maintenance costs and unplanned downtime decrease.
Energy consumption and resource use are better managed.
Trades are gaining visibility on equipment, sites, and operations.
AI and automated uses rely on richer, more contextualised data.
The transition from POC to industrialisation is becoming more realistic and better controlled.
VISION & PERSPECTIVE
In the coming years, IoT will become increasingly integrated directly into organisations' data and AI platforms. The growth in telemetry volumes, cost constraints, and the need for responsiveness will drive more distributed architectures, with greater filtering, processing, and decision-making at the edge. The most mature companies will focus less on multiplying connected objects and more on better governing, cross-referencing, and leveraging the data they produce.
Artificial intelligence will play an increasing role in this evolution. It will facilitate the detection of anomalies, the interpretation of weak signals, the generation of indicators and the automation of certain decisions. In parallel, OT and IT cybersecurity issues, compliance, machine-to-machine identity and sustainability will gain more weight. Therefore, the IoT is not an isolated project, but a structuring component of a data and AI strategy focused on usage, performance and scaling up.
TO GO FURTHER...
Blog article
Modernising the information system: how Géo Vendée is transforming its territory through data?
FAQ
What is the IoT?
The IoT brings together technologies that enable connected sensors and objects to collect and transmit data from the field to information systems.
Quels sont les avantages d'une solution IoT pour une entreprise ?
It allows for better operational control, optimisation of resources, reduction of certain costs and improvement of service quality thanks to field data.
Why integrate IoT data into a data platform?
Because when isolated, they remain largely unusable. Integrated into a data platform, they can be cross-referenced with other organisational data to generate more value.
The difference between IoT and AIoT is that IoT refers to the interconnection of devices, while AIoT refers to the interconnection of devices with artificial intelligence integrated.
The IoT focuses on the collection and transmission of field data. AIoT combines IoT, data, and artificial intelligence to enhance analysis, automation, and decision-making.
Les principaux obstacles aux projets IoT sont la sécurité, la confidentialité des données, le coût de mise en œuvre, l'interopérabilité des appareils, le manque de normes, le manque de compétences spécialisées et la gestion d'un grand volume de données.
The most frequent obstacles are data silos, difficulty in industrialisation, security concerns, data quality, and a lack of alignment with business uses.
Why is IoT a key enabler for AI?
Because it provides continuous, contextualised and actionable field data, essential for feeding analysis, forecasting or predictive maintenance models.
IoT creates value when it enables the transformation of field data into concrete levers for steering, optimisation, and decision-making. By integrating connected objects into a global data and AI strategy, organisations can better exploit their operations, improve their energy efficiency, and strengthen the robustness of their use cases. JEMS supports this transformation with a comprehensive, structured, and value-oriented approach, from the sensor to the use case.
