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Systems are Too Complex for Traditional Observability


Observability must encompass the different systems that comprise modern applications, rather than a single system like in the early days.

Achieving observability sounds great when systems are contained. Now that data has exploded, everyone is walking on eggshells around their legacy systems and trying to integrate third and fourth-party data. Observability is…complicated.

Download Infographic Now: The growing role of observability

Companies must understand how the components of their data ecosystem work together. These complex ecosystems can easily create unnecessary security risks and data quality failures without some level of observability. So how can organizations build observability into their entire data stack? They have to consider it from more angles than just the big three—logs, metrics, and traces. Instead, they need to take a new approach.

See also: Pursue Monitoring, but Don’t Forget Observability

What is the goal?

It isn’t just looking at the systems. Observability gives organizations the chance to react more quickly to threats and bottlenecks. In many cases, as the system gets smarter, it can prevent these problems, moving companies from a reactive to a proactive maintenance model.

It also provides context for events. Relying solely on logs or metrics doesn’t tell a company why something happened or when it might happen again. It doesn’t provide logical steps to fixing an issue or preventing it in the first place. And it extends beyond events to include development lifecycles and even end-user experiences.

Observability is even more important in enterprises where distributed systems can make collaboration truly monstrous to attain. It provides vital clues to the health of the entire system, continuously alerting and documenting “unknown unknowns” or problems that arise because of the system’s complexity.

A final goal lies in discovering and measuring the business value of any digital services. Again, companies can measure end-user experiences, optimize software and other product releases to fulfill what customers actually want, and meet other business-oriented needs. This is the only way to make the decisions that matter most to moving the needle in business.

See also: Monitoring or Observability? Why Not Both?

Variety matters

Traditional observability includes three components:

  • Logs: A record of an event that happens on a system. These are automatically created and cannot be changed.
  • Metric: A numerical representation of data. These are not single events but an overall measure of system health or function.
  • Trace: A record of a series of events. These happen within the same request flow on one or more systems.

These are no longer enough to encompass a full process, however. Enterprises are dealing with system complexity and a multitude of initiatives and goals. It must encompass all these different systems, rather than a single system like in the early days.

According to the Eckerson group, decision-makers must consider five different disciplines:

Business observability

Imagine a company being able to monitor a newly released service from inception to pilot to full release. With business observability, companies can make critical decisions about the business value of products and tools. Using data like transactions and cost, the company can identify trends and previously missed correlations.

Operations observability

Imagine understanding an entire ecosystem from its utilization to its availability to all users. Operations observability keeps companies freshly updated on the health of their ecosystem. This is the realm of IT, DevOps, and other Ops departments.

Pipeline observability

Data is more than the new gold; it’s the new oxygen. Data pipeline observability keeps an eye on those pipelines to ensure that data is working for the business. This observability removes bottlenecks from data delivery and also checks peripheral systems.

Model observability

As more companies integrate machine learning and artificial intelligence into their operations, model observability ensures that these new initiatives are working for real business value. In fact, a majority of these types of initiatives fail without some way to measure governance and delivery.

Download Infographic Now: The growing role of observability

Data quality observability

Garbage in, garbage out. Data quality makes or breaks initiatives from simple planning to trend forecasting to machine learning and beyond. Digital transformation cannot happen without validating, tracking, and profiling data assets across applications. This element also feeds into DataOps initiatives.

Making observability a reality involves people, tools, and a single source of truth

Gathering data isn’t enough. Relying solely on human teams isn’t enough. Observability blends tools that can monitor and learn from each system event and offer context that human teams can respond to. The presence of cloud systems makes human observation models of the past brutally inefficient, but with observability tools, teams can keep up.

Companies must address key obstacles to observability first.

  • Data silos: Integrating partner data, legacy systems, and all data sources are vital to creating system-wide observability.
  • Data volume and velocity: Not only are teams grappling with unprecedented volumes of data, but that data is coming at them at lightning speeds. Tools will need to address the volume and velocity of today’s data ecosystems.
  • Manual processes: Troubleshooting, documenting, instrument coding— the more manual components an observability process has, the harder it is for the team to watch and respond to the whole ecosystem.

Establishing a single source of truth—the holy grail of data management—provides unaltered observability across all components. It works by taming cloud complexity and allowing the entire enterprise to collaborate for business value from the same data. Any tool that claims to offer observability must address disparate data sources and system complexity.

A single source of truth also makes troubleshooting easier. Whether it’s the data pipeline, operations, or something else, an enterprise needs context and real answers from the system. This moves teams from a reactive state to a proactive state and ensures timely responses to any issues that arise.

Competing in 2022 and beyond

Observability is another differentiator for the modern enterprise. It ensures all systems work for the common business value and makes remaining in compliance with changing regulations a reality. Without it, the left hand can’t know what the right hand is doing while trying to perform delicate, life-saving surgery. The left foot can’t know what the right foot is doing while walking along a death-defying ledge. Whatever the metaphor, businesses must embrace the full observability spectrum to compete in the global economy today and in the future.

Download Infographic Now: The growing role of observability
Elizabeth Wallace

About Elizabeth Wallace

Elizabeth Wallace is a Nashville-based freelance writer with a soft spot for data science and AI and a background in linguistics. She spent 13 years teaching language in higher ed and now helps startups and other organizations explain - clearly - what it is they do.

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