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IoT Data Management: Mastering the Intelligence of Connected Devices

What if we could easily and massively gather measurable intelligence about the physical world? This is the promise behind every connected solution. However, to extract real business value and formulate concrete reactions from collected information, attention to the design of the data model is key. As part of our comprehensive IoT development services, we help you bridge the gap between raw hardware signals and cloud-based intelligence.
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What is IoT Data Management?

Simply put, IoT data management is the process of collecting, organizing, and protecting data from connected objects. It differs from traditional software data management because of the scale and nature of the data.

In standard IT, data is often structured and enters the system via human input. In IoT, data collection is automated, continuous, and happens at a massive scale. Managing this data volume requires specialized data management systems that can handle everything from a simple temperature sensor to complex industrial machines.

The Different Kinds of IoT Data

To build a successful data management solution, you must first understand what you are collecting. Not all data is created equal.

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Human Interaction and Heavy Payloads

When a device has actuators like buttons, levers, or a screen, it transmits traces of human interaction. Beyond numerical values, some iot applications require streaming heavy payloads, such as a video feed from a surveillance camera. This significantly impacts your data storage needs.
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Telemetry and Sensor Data

Some telemetries belong to the category of “well-known” metrics. Classic iot sensors monitor the direct surrounding of a connected device. This includes:

  • Environmental conditions: Temperature, atmospheric pressure, humidity, wind-speed.
  • Spatial positioning: Accelerometer, gyroscope, GNSS coordinates.
  • Internal values</strong: Battery level, power consumption, storage space.

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    Digital Twins and Reported Properties

    For configurable devices, a common way to manage them is to link their settings to a digital twin. To reflect the desired properties managed by an administrator, the device needs to emit back what they have applied in the form of reported properties. This kind of traffic is less verbose but essential for data integrity.
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    Debugging and Diagnostic Data

    For debugging sessions, a remote operator may want to fetch journal log entries or non-consumer-facing data. The targeted device is expected to respond correctly to commands sent on the fly. This requires a data flow that is bidirectional and highly responsive.

    Technical Constraints: Connectivity and Security

    The size and frequency of data emission determine a set of constraints. These must be confronted with the chosen technology of connectivity.

    Some collection patterns won’t be possible on a limited network with a small allocated bandwidth. To process data effectively, the format of the payload and associated compression must be optimized. For example, instead of repeating identical emission timestamps, they can be enumerated only once.

    Choosing the right protocol is a foundation of your strategy. Whether it is MQTT for lightweight messaging or cellular for global reach, your connectivity choice dictates your data accessibility

    . Explore our expertise in Device Connectivity to learn more about these protocols.
    Security should never be neglected. A lot of guarantees are strongly coupled with the elected encryption scheme: confidentiality of exchanges and authenticity of emitters and receivers. In the age of data protection, ensuring that your iot systems are secure from the sensor to the cloud is a legal and operational requirement.

    The Power of Edge AI in Data Management

    A modern management in iot strategy often involves Edge AI. Instead of sending every raw data point to the cloud, you can process information directly on the device.

    By using Edge AI, you can:

  • Reduce Data Volume: Only send relevant events or anomalies.
  • Enhance Privacy: Keep sensitive data on the device.
  • Improve Real-time Analytics: React to critical events in milliseconds without cloud latency.

    • This local intelligence is a game-changer for operational efficiency. Discover how we implement Edge AI to optimize data management at the source.

    Edge AI Services

    Time Handling: The Heart of the Data Lifecycle

    The freshness of data can really matter for some devices while being totally irrelevant for others. Therefore, is it worth sending already stale data before the latest one if no exhaustive historization is needed?

    This kind of question has direct consequences on the device caching strategy. A device may work in various modes:

  • Sleeping mode: Mute while in the factory or transit.
  • Passive monitoring: Regular usage with low emission frequency.
  • Active mode: High volume of collection and emission during emergencies.

    Choosing the right protocol is a foundation of your strategy. Whether it is MQTT for lightweight messaging or cellular for global reach, your connectivity choice dictates your data accessibility

    . Explore our expertise in Device Connectivity to learn more about these protocols.

    • When zooming out at the scale of the whole fleet, the notion of priority evolves. If a subset of devices requires increased attention (for example, during a critical firmware update or a localized sensor failure), the platform must be able to ingest their telemetries first and limit the bottleneck of regular ingestion.

    Effective IoT data management is the engine behind professional fleet management, allowing you to monitor health, performance, and security across thousands of assets in real-time.

    Main Challenges: Ingestion Pipelines

    ETL vs. ELT Strategies

    Should the focus be set on “Extract, Transform, Load (ETL)” where the data validation and calibration come right at the beginning? Or is “Extract, Load, Transform (ELT)” more suitable, with raw data rapidly accumulated in data lakes?
    • Decoding: Data may need to be decoded depending on the protocol and format.
    • Cleaning: Non-conformant or corrupted values are removed.
    • Normalization: This ensures forward and backward compatibility between multiple generations of devices.

    Data Lifetimes and Storage Tiers

    How long should the data stay in the system before being moved or discarded? This interrogation has implications on aggregation and compaction tactics.

    The speed in which information has to be gathered guides our decisions about tiers of storage:

  • Cold Storage: Perfect for archiving treated data not relevant to day-to-day operations.
  • Hot Storage: A quick database or cache layer to provide responses to services interested in recent data.

    • Note that in some industries, legislation and compliance requirements may have the final say on data retention.

    Engineers at Witekio 11

    Generating Business Value with Data Management

    01. Front-End Applications

    To provide the right level of intelligence, the needs of these front-end applications must be supported by back-end services gating the data via REST APIs or GraphQL endpoints. These servers themselves have to obtain the values from the storage layers.

    02. Database Selection

    For optimal query efficiencies, the correct type of database must be put in place, usually column-oriented or timeseries. Partitioning and sharding must give useful metrics at the fleet level while allowing a quick focus on specific devices.

    03. System Alerts

    In active monitoring, receiving quasi-live feedback is vital to react to alerts. The final medium of delivery (emails, notifications) depends on the criticality and the responsibility of the administrators.

    Visualizing IoT Architecture

    To understand how these components work together in a real-world iot project, watch our expert breakdown

    SUCCESS STORY

    Case Study: Velan’s Connected Valves

    Velan needed support developing connected IoT valves for the nuclear energy industry. The valves provide reliable and up-to-date telemetry to customers.

    To make this possible, data from internal valve sensors needed to be migrated in real time to a custom web application dashboard. Witekio ensured this was done in a secure and accessible manner.

    By designing a robust data model, we helped Velan move from simple manufacturing to providing predictive maintenance services. This transformed their business model and increased operational efficiency for their nuclear plant clients.

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    How to get started

    7 Steps
    to a Successful IoT Data Project

    • Define Business Goals: Focus on the “why” before the”how”.
    • Design the Data Model: Plan for the future to ensure data effectively management.
    • Select Connectivity: Match your protocol to your environment.
    • Implement Security: Build data security into every layer.
    • Set Up Ingestion: Choose between ETL and ELT based on your data volume.
    • Define Lifecycle: Manage storage costs with hot and cold tiers.
    • Analyze and Iterate: Use data scientists to find hidden patterns in your data.

    FAQ: IoT Data Management

    It allows for predictive maintenance, better business decisions, and improved operational efficiency by turning raw sensor signals into clear insights.
    You must follow compliance requirements like GDPR and industry-specific regulations. This includes managing the data lifecycle and ensuring data security.
    While possible, timeseries databases are usually better for handling the high-speed data streams typical of iot systems.

    Witekio: Your Partner for Device Data Management

    Witekio simplifies device data management, enabling businesses to efficiently capture, analyze, and act on connected device data. From telemetry collection to secure data ingestion, we ensure your devices transmit actionable insights.

    With our expertise in protocol selection, data storage optimization, and real-time responsiveness, we help you transform raw data into measurable intelligence.

    Ready to unlock the value of your data?

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