Optimizing IoT telemetry storage costs: Retention strategies

Multi-tiered IoT telemetry storage for cost optimization

The escalating volumes of telemetry from connected devices, sensors, and actuators place a substantial burden on storage infrastructure and budgets. Effective management of this data requires a strategic approach, where multi-tiered storage plays a pivotal role. This concept involves placing data on different storage types based on their value, access frequency, and retention period, which significantly reduces operational costs without compromising analytical capabilities.

Leading cloud providers offer various storage tiers with corresponding costs and performance. For instance, AWS S3 features Standard, Standard-Infrequent Access (S3 Standard-IA), and Glacier/Deep Archive, while Azure Blob Storage offers Hot, Cool, Cold, and Archive. Google Cloud Storage also provides Standard, Nearline, Coldline, and Archive. Storage costs can vary: for example, AWS S3 Standard is approximately $0.023 per GB for the first 50 TB per month, whereas Azure Blob Hot (LRS) offers around $0.018 per GB. For archive tiers, prices can be significantly lower, reaching approximately $0.00099 per GB in AWS and Azure for deep archive. However, it's crucial to consider not only the cost per GB but also hidden expenses such as charges for requests, data retrieval, and egress traffic.

Automated data lifecycle policies enable seamless data movement between tiers as it ages, ensuring cost control while maintaining dataset availability when needed for analytics or compliance.

Data aggregation strategies to reduce storage volumes

Large-scale IoT deployments generate immense volumes of high-frequency data from sensors and devices. Sending every data point to the cloud incurs extreme bandwidth costs, storage challenges, and delays in anomaly detection. Processing data at the edge – aggregating, downsampling, and performing local analysis before transmission – significantly reduces costs while improving real-time responsiveness.

Key aggregation techniques include:

• Temporal aggregation: Collecting high-frequency metrics (e.g., per-second) and aggregating them into configurable intervals (per-minute, per-hour), calculating statistics such as average, minimum, maximum, and percentiles. This can reduce data volume by 90%+ under stable conditions.
• Decimation: Transmitting every Nth sample, discarding intermediate values. Suitable for slowly changing processes.
• Event-triggered aggregation: Sending data only when values exceed thresholds or change significantly.
• Statistical summaries: Instead of full raw data, statistical summaries (mean, min/max, standard deviation, quantiles) are transmitted, providing a trade-off between information preservation and data volume.

Data aggregation can be implemented at various levels: at the sensor/device level (Perception Layer), at the aggregation layer (gateways, edge/fog nodes), and at the cloud/application layer. This helps reduce bandwidth usage, save energy and storage, eliminate duplicate or irrelevant data, and improve data quality.

IoT telemetry data lifecycle management (DLM)

Data Lifecycle Management (DLM) in IoT is a structured approach to securely managing IoT devices throughout their operational journey, from initial setup to decommissioning. This includes automated data movement between storage tiers and its deletion in accordance with defined policies and regulatory requirements.

Regulatory data retention requirements are critically important, especially in sectors like energy, healthcare, and manufacturing, where retaining operational data for a specified period is legally mandated. For example, GDPR requires identifiable data to be stored no longer than necessary for its defined purpose. This means retention policies should not be arbitrary but justified by specific business objectives or compliance requirements.

Cloud platforms provide tools for object lifecycle management, such as AWS S3 Lifecycle Policies, Azure Blob Storage Lifecycle Management, and Google Cloud Storage Object Lifecycle Management. These tools allow for automatic data movement to cheaper storage tiers or deletion after a defined period.

It is also important to note that some storage tiers have minimum retention periods, and deleting data before this period may incur additional costs. For example, S3 Standard-IA has a minimum of 30 days, Glacier Flexible – 90 days, and Deep Archive – 180 days.

Impact of retention policies on analytical capabilities and cost

The choice of IoT telemetry data retention policies is always a compromise between data granularity, retention period, cost, and opportunities for deep analytics and machine learning. Overly aggressive data reduction can limit future analytical capabilities, while excessive storage of raw data leads to unjustified costs.

Long-term storage of detailed raw data is critical for scenarios requiring in-depth analysis, such as:

• Predictive maintenance: Training machine learning models to predict equipment failures requires large volumes of historical operational data.
• Anomaly detection: Detailed data allows for the detection of subtle deviations from normal device behavior, which may indicate potential problems or cyberattacks.
• Research and development (R&D): Developing new products or optimizing existing processes may require archival data to study long-term trends.
• Compliance and audit: In some industries, regulatory requirements mandate the storage of original data for extended periods for auditing and verification of operational reliability.

At the same time, for many monitoring and reporting scenarios, aggregated data is sufficient. For example, for tracking general energy consumption trends or the occupancy of automated facilities, minute-by-minute or hourly aggregations fully satisfy needs. This significantly reduces storage volumes and associated costs while retaining sufficient information for decision-making.

Balance is achieved by carefully defining the value of each data type for specific business cases and applying appropriate retention and aggregation policies. It is important to continuously monitor data access patterns, not just their size, to decide what to move to lower storage tiers.

IoT telemetry retention policy selection matrix

To make informed decisions regarding IoT telemetry storage, the following matrix is proposed, considering key criteria:

Implementing retention strategies in AZIOT

AZIOT, as an enterprise knowledge base and facility automation platform, integrates a wide range of protocols such as MQTT, Modbus, BACnet, KNX, Zigbee, Z-Wave, LoRaWAN, Matter, SCADA, BMS, and ERP. This enables the collection of telemetry from diverse connected devices and sensors. To optimize storage costs, AZIOT employs approaches that include edge computing for initial filtering and aggregation before data reaches central storage. The Unity Base platform, underlying AZIOT, allows flexible configuration of rules and scenarios for data management, including its movement between different storage tiers and aggregation based on defined time intervals or events. Monitoring tools and dashboards in AZIOT provide solution architects and data engineers with the necessary visibility to track data volumes, access frequency, and the effectiveness of applied retention policies. This enables prompt adjustment of storage strategies, ensuring a balance between cost and analytical needs, as well as compliance with audit and access control requirements.

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Practical steps to optimization

Optimizing IoT telemetry storage costs is an ongoing process that requires continuous monitoring and adaptation. Start by auditing current data sources and their business value. Determine which data requires high granularity and fast access, and which can be aggregated or moved to cold storage. Implement automated data lifecycle policies and regularly review their effectiveness. Remember that investments in an architecture supporting multi-tiered storage and efficient aggregation pay off through significant reductions in operational costs and increased flexibility for future analytical needs.