How do you manage raw telemetry data?

Raw telemetry serves as the foundational data for analytics, yet it can significantly impact costs. It is crucial to thoroughly analyse the telemetry's flow and usage in order to pinpoint the appropriate service and handling processes needed. When choosing storage and processing methods, it's essential to align them with the specific requirements of your telemetry use case.

When choosing a data lifecycle policy, it's important to weigh various tradeoffs. One key consideration is whether to prioritise speed to market or cost optimisation. Sometimes, it makes more sense to focus on speed—rushing to market, introducing new features, or meeting tight deadlines—instead of initially investing in cost optimisation. Leveraging your organisation's data classification strategies, you can tailor a lifecycle policy to guide raw telemetry data through different services. Establishing time-based milestones helps establish expectations and promotes data aggregation and production, shifting the focus from mere data collection to a more comprehensive data management approach.

Each IoT application generates diverse types of data with varying storage requirements, such as telemetry data, logs, or historical records. Therefore, a one-size-fits-all storage solution is rarely optimal. By carefully assessing your use case's unique demands, including data volume, access patterns, and retention policies, you can make informed decisions about which storage services to employ.

For instance, high-frequency, real-time telemetry data might necessitate fast and scalable databases, while historical or archived data may be better suited for cost-effective, long-term storage solutions like cold storage or object storage. By aligning your storage choices with your use case, you not only enhance data management efficiency but also control operational costs and ensure data availability when and where it's needed most.

Storing raw archival data on cost-effective services is fundamental strategy in cost optimisation. IoT ecosystems often produce an immense volume of raw data, much of which might not require frequent access, but still needs to be retained for compliance, analysis, or historical reference.

To optimise storage costs, organisations should leverage specialised archival services or long-term storage solutions designed to accommodate this low-access, high-retention data. These cost-effective services, such as cold storage or archival tiers, are typically priced at a fraction of the cost of primary storage options, making them a practical choice for preserving data economically.

By adopting this approach, enterprises can ensure that raw archival data is securely stored and readily available when needed, while effectively managing operational expenses, a critical consideration in the resource-intensive IoT landscape.