The Journey to Transformational Observability
This is part of a series of contributed articles leading up to KubeCon + CloudNativeCon on Oct. 24-28.
While data is growing at an ever-increasing rate, IT budgets are not keeping pace. And as data management costs keep increasing, companies are not seeing a corresponding increase in the value they generate from their observability data initiatives.
Although there is an increase in the number of users who want to harness data to make decisions, access and usability of data remain a challenge. As data sources multiply, the need for data interoperability and correlation increases so that users can access, unify and use data from different systems and functional groups.
Without proper processes and technology, organizations cannot fully harness observability data and may compromise on risk management and the service quality they can offer their customers.
Enterprises are looking for ways to increase the value they get from their data investments while managing spiraling costs. They are looking for ways to make data available to a broader set of users with better access and control.
Relying on control, such as routing data to observability tools and lower-cost storage to help manage costs, is insufficient. Beyond the management and control of data, enterprises seek more value and real-time actionability. With better control plus data augmentation by enrichment and correlation, enterprises respond to issues faster and uncover opportunities to improve customer experience and launch new products.
The path to true observability is a journey, and understanding your stage helps identify people, processes and technology gaps to get more business value from your data investments. Here are four stages enterprises can use to evaluate their practices and tools and deliver better products and services to their customers.
Stage 1: Ad Hoc Log Monitoring
In this stage, organizations acknowledge the need for observability, but their focus is limited to logging and monitoring individual systems and applications. The data sources may include one-off servers or applications, and the destination is generally a log management tool or affordable storage like an AWS S3. Logs are collected and aggregated by individual users for a specific use.
Different groups may search for tools for individual use or adopt open source technology to build their log management solutions. The primary use case is log aggregation and manual analysis to identify issues.
Basic parsing and exclusion capabilities help identify problems and manage data costs. Observability tools are likely owned by the operations team, who must dedicate cycles to help other groups access the data they need.
Stage 2: Planned Control
Organizations with enhanced data control have better processes and tools to enable more comprehensive data access and greater control. There is a companywide recognition of observability needs, and enterprise solutions replace one-off log management tools.
Data ingestion comes from various cloud infrastructures, Kubernetes, or streaming platforms like Kafka. Destinations include a variety of messaging queues, observability and security platforms. Many users, including developers, ITOps, DevSecOps and SREs, use the solution for log management and analysis.
Capabilities such as remapping, aggregation, filtering, and data sampling help users with superior data control. Sophisticated access control and routing meet the data needs of a wide range of users in the organization.
Stage 3: Optimized for Actionability
In this stage, enterprises have better control of their data and extract higher value from their observability investments. Observability processes and infrastructure are optimized to get actionable insights, improving MTTD/R and speed of product launches.
Organizations are sophisticated in enriching data for better context and correlating cross-domain data to uncover deeper insights. They leverage OpenTelemetry for additional context.
In addition, enrichment and correlation happen while the data is still in motion instead of waiting for analysis when it has reached its destination, like a data lake. As a result, these organizations can proactively identify patterns before they become security issues or customer complaints.
Stage 4: Transformational
Organizations at the transformational observability stage have evolved beyond working in data silos and have advanced DevOps, DevSecOps and DataOps approaches. They apply observability principles and technology to transform their business. They treat data like an enterprise asset captured from multiple sources, including business applications such as CRM and customer data platforms and use insights to activate workflows in security or customer-facing applications.
Achieving transformational observability requires real-time cross-domain correlation and intelligent data processing. DataOps and DevOps join to harness enterprise data to its fullest. Insights become actionable and are used to gain a competitive advantage by delivering a superior customer experience and products.
The shift to digital is accelerating, and data is a critical competitive advantage. As businesses manage complex supply chains and deliver sophisticated customer experiences that span dozens of applications and systems, the need for data access, control, actionability and security increases.
Customer expectations force companies to be more responsive, and increasing security threats and regulations require organizations to be more vigilant and proactive. In this market dynamic, organizations must put necessary processes and technologies in place that help them use their observability data to be efficient, compliant and competitive.
Understanding where the organization stands on its observability journey will allow them to evaluate its gaps and formulate a path that aligns with the company’s business goals.
To hear more about cloud native topics, join the Cloud Native Computing Foundation and the cloud native community at KubeCon + CloudNativeCon North America 2022 in Detroit (and virtual) from Oct. 24-28.
