In today's digital age, telemetry data (i.e., logs, metrics, events, and traces) helps provide insights into system performance, user behavior, potential security threats, and bottlenecks. However, this data's increasing volume and complexity lead to uncertainty about data quality and completeness, undermining confidence in downstream analytics. To maximize telemetry data utilization, organizations need to focus on establishing trust in their telemetry pipelines.

Here are seven ways telemetry pipelines can help build confidence in data:

1. Provide optimal data without cost overruns

Telemetry pipelines provide capabilities to optimize data for cost-effective observability and security. By reducing, filtering, sampling, transforming, and aggregating data, organizations can effectively manage the flow of information to expensive analytics systems, potentially decreasing data volume by up to 70%. Teams must trust that the data exiting the pipeline is accurate, in the right format, and relevant. By monitoring the data flow at various pipeline stages and running simulations, they can ensure that the data is processed and delivered as intended.

Furthermore, data patterns and volumes will change as businesses evolve. Even a minor modification in application code can generate unexpected logs, quickly exhausting an observability budget. Configuring the telemetry pipeline to identify and address such data variations and provide timely alerting can shield organizations from unforeseen expenses. Prompt notifications of unusual data surges enable teams to analyze the incoming information confidently.

2. Store low-value data, and redistribute if needed

Many organizations filter or sample data before sending it to expensive data storage systems to reduce costs. However, compliance requirements or the need for future incident debugging may necessitate storing complete datasets for a specific period, typically 90 days or even up to a year. A telemetry pipeline can send a data sample to analytics platforms while diverting the remaining data, pre-formatted and ready-to-use, to affordable storage options like AWS S3. When required, the data from low-cost storage can be sent back to the analytics systems via the pipeline, also known as rehydration. This allows teams to confidently handle compliance audits and security breach investigations by rehydrating the data through the pipeline when needed.

3. Enable compliance

Organizations are required to comply with various privacy laws, such as GDPR, CCPA, and HIPAA. Telemetry data may contain personally identifiable information (PII) or other sensitive information. If this information isn't appropriately scrubbed, it can result in the unintended distribution of sensitive data and potential regulatory fines. A telemetry pipeline uses techniques such as redaction, masking, encryption, and decryption to make sure data is protected and used only for the intended purpose. If some data changes in a way that allows PII data to sneak into the pipeline, in-stream alerts can identify the issue, notify teams, or even take automated remediation actions.

4. Orchestrate data

Establishing effective data access and collaboration has long proven challenging for DevOps, security, and SRE teams. Often, data is sent to a system, locked away, and made inaccessible to other teams due to formats, compliance, credentials, or internal processes. However, with a telemetry pipeline serving as the central data collector and distributor, teams can ensure that the correct data is readily available to any observability or security system when needed. This allows DevOps, security, and SRE teams to perform their jobs effectively and guarantees that users only receive the necessary authorized data. Such data governance and policy enforcement are critical to enabling trusted data distribution.

5. Respond to changes

DevOps and security teams rely on telemetry data to address various issues, like performance and security breaches. However, these teams face the challenge of balancing their objectives of reducing MTTx (mean time to resolve incidents) and managing data budgets. There is a constant concern that they may not collect enough data in case of an incident, resulting in significant observability gaps.

Telemetry pipelines allow teams to efficiently capture all the necessary data and only send samples to high-cost analytics systems. In the event of an incident, the pipeline can respond and quickly switch to an incident mode, sending complete and detailed data to a security information and event management (SIEM) system. Once the incident is resolved, the pipeline reverts to its normal sampling mode. By implementing this pipeline, teams can have confidence that they'll always have access to the required data when needed.

6. Deliver business insights

Telemetry data is valuable for extracting meaningful business insights. For example, an e-commerce company can gain real-time business insights through metrics such as product orders, cart checkouts, and transaction performance, which can be extracted from telemetry events and logs and are generally unavailable in business intelligence systems. Using pipelines, such a business can extract these metrics or even create new ones in real time. And organizations can confidently analyze and visualize their reports. The data is aggregated, enriched, and delivered in easily consumable formats using visualization tools.

7. Ensure current data

The data sources and content must be current to ensure that users have the latest information for incident resolution and decision-making. A telemetry pipeline makes it easy and efficient to onboard new data sources, format and prepare data for usage, and refresh data in data lakes with additional information. Regular updates or additional information may be required when data is stored in data lakes. In such cases, a loop pipeline can retrieve the data from the lake, enrich it with the latest information, and return it to the data lake. This keeps the data current and ready for use.

Importance of trust in telemetry data

Confidence in telemetry data has become essential in today's digital world. As organizations face the challenges of managing vast and intricate data, trust in that data has become increasingly important. Telemetry data provides valuable insights, but organizations need to manage and control telemetry data effectively to unlock its full potential. Investing in telemetry pipelines and prioritizing data quality and understanding are essential to achieving clarity and confidence in digital operations. These steps help organizations make informed decisions, boost customer satisfaction, and establish trust in their services and products.