How Observability Accelerates Business Transformation with More Successful Outcomes

Without observability, digital transformation could be a risky journey, resulting in poorly performing services that will ultimately impact both the customer experience and the bottom line.

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Modern IT infrastructure and applications that enable new digital services have become a major area of investment over the past few years. With operational efficiency being so critical for successful business outcomes, CIOs can no longer rely on legacy log management and monitoring technologies to ensure reliability of modern infrastructure and applications.

While data has been and will continue to be the currency of digital transformation, it is valuable only if you can extract value from it easily and cost effectively.

The fundamental problem with extracting this value is that software is growing more and more complex. Applications have gone from being monolithic to being highly distributed in nature, and the use of microservices, containers, and elaborate orchestration tools makes it difficult to pinpoint, correlate, and understand what’s going on in the system with the data collected. At the same time, environments are also growing more complex, with digital services sprawling across clouds and on-premise systems. 

That’s where observability comes in. Observability is about ensuring that you can deliver reliable infrastructure and digital services in the face of increasing complexity of networks, systems, and applications. Without observability, digital transformation could be a risky journey, resulting in poorly performing services that will ultimately impact both the customer experience and the bottom line.

While it appears that observability is a straightforward goal, many companies are realizing that existing monitoring tools are unable to keep up with the massive data volumes created by modern digital systems. CIOs and business leaders need to rethink how they can solve today’s high-volume data and infrastructure management problems with strategy and architecture around observability.

How observability aligns IT and business

The IT world has been adopting new operational models, such as BizDevOps and DevSecOps. These movements signal the importance of aligning IT and business around security, data collection, and usage to improve digital operations. However, the increased complexity of our digital world often leaves IT and business teams struggling to manage high-volume, high-velocity data and to make sense of that data. 

The rise of BizDevOps 

Implementing a successful BizDevOps strategy is about using data that correlates IT efforts with its impact on business outcomes. For example, customer retention is an important KPI to track for any business offering digital services or products. CTOs are responsible for technical problems related to applications and systems, but they also play an important role supporting key business performance metrics such as customer retention. If their processes and systems are not reliable, technical issues could lead to poor customer experiences that result in churn. To closely monitor churn, engineering and IT teams need to observe and track the rate of customer cancellations and to try to correlate those cancellations with their root causes.

The gateway to observability is gathering those metrics in a centralized, queryable data store and making that data accessible in real time. This enables operators to build dashboards and get a high-level view of the health of their systems. Eventually, IT teams will need to jump from a dashboard-level view and drill down into problems to understand why their applications are behaving in a certain way. That’s where logs come in. Digging into your system and application logs can help explain certain behavioral problems that metrics don’t provide. 

How we think about scale and performance

Log management today has become incredibly expensive. Despite a growing number of use cases for IT and security organizations, the cost of ingesting, storing, and querying log data can far exceed the value generated. Today, IT leaders are faced with a complex range of decisions:

  • How should I evaluate modern observability analytics platforms?

  • Can I ensure reliability and performance of infrastructure and applications at scale?

  • How quickly can I acquire the skill sets to adopt AI/ML technologies in order to compete?

To answer these questions, you must first understand the true cost of operating home-grown log management systems based on open-source technologies or the cloud-hosted versions of the same open-source stack. Although modern IT teams have been historically successful using these log management solutions to improve mean time to resolution (MTTR), it has become clear they were not designed to be optimal solutions for today’s log volumes. When evaluating observability platforms for cost-effectiveness, scale, and performance, focusing on design early on can help overcome the following obstacles:

  • Coupling of storage and compute. Even if zero compute is required, organizations have to pay for a CPU that is running 100% of the time. Or teams pay for storage that is not required, just to accommodate the CPU demand for ingest.

  • Single-tier architecture. The biggest disadvantage is lack of scalability. If the layer becomes overwhelmed, it impacts the entire system.

  • Data swamp. Avoid building a highly disorganized data swamp where retrieving and querying data is difficult.

For performance-driven organizations that demand real-time access to large volumes of log data, architecting solutions around object storage provides huge cost benefits as well as allowing organizations to decouple storage from compute. Decoupled storage and compute allows teams to pay for only infrastructure resources they consume without dedicated processing and storage resources. 

Modern observability solutions, such as EraSearch, which optimize object storage in ways that provide a unified, highly-reliable, distributed system can help organizations maximize the ROI of observability at scale and exceed business SLAs without operational burnout.

Meanwhile, forward-looking IT leaders are increasingly adopting advanced functionalities like ML and AIOps tools. Without a solid, scalable architecture and a strategy for observability data, organizations will encounter challenges in implementing AI.

Upcoming industry trends

The pandemic accelerated some trends that are going to make observability data management important going forward – moving to the cloud and multi-cloud, adopting modern development and operations practices such as DevOps and SRE, and leveraging data to drive predictive patterns. 

A key statistic from Era Software’s 2022 State of Observability and Log Management report indicates that an overwhelming majority (96%) of IT professionals surveyed share that using data effectively to solve problems outweighs just storing data.

As a result, many industries are adopting DevOps principles with observability to protect revenue. In the gaming industry, the cost to business because of server outages, game response lag, or generally poor performance can tally in billions of market cap. Powerful observability capabilities are crucial, including processing high-volume data, managing fluctuating observability data from variable user loads, and running concurrent real-time queries on a massive scale.

Real-time data will be more important for cybersecurity. Modern applications and workloads have expanded our digital attack surface faster than we can protect it. While cyber adversaries act with speed, many organizations still take hours or days to troubleshoot security incidents. Combating today’s threats will require a different way of using data to improve response efforts and decrease vulnerability.

Observability data management sits in the center of everything, removing the complexity and toil of accessing data so IT and security organizations can collect, store, analyze, and deliver real-time insights where they matter most.

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