Observability Is Storytelling: And Every Good Story Needs Context

In today’s digital world, observability is more than metrics, logs, and traces—it’s about storytelling.

Every system, every service, and every user interaction tells a story. But just like a novel missing key chapters or a film with no plot, data without context is hard to follow, harder to trust, and almost impossible to act on. If we can't understand the message behind the data, how can we confidently respond to incidents, improve experiences, or measure success?

The Story Is in the Signals

At its core, observability is the ability to answer: “What’s happening, and why?” To do that well, we need structured, meaningful signals. And that means shaping the data—not just collecting it.

• Logs that describe what's happening and why.

• Metrics that highlight the scale and severity.

• Traces that tie everything together into a narrative.
  

But the story only makes sense when there’s context—when developers insert clear breakpoints, meaningful log messages, or useful metadata that reveals what “good” and “bad” look like.

Why Context Matters

You can ingest terabytes of telemetry every day, but if the data lacks clarity, correlation, or intent, you’re left with noise—not narrative. Worse, poor data can create false stories: blame the wrong system, miss the root cause, or overlook a silent failure.

That’s why context is king in observability. It’s not just about what we collect, but how and why we collect it. Thoughtfully shaped data tells a clearer, more trusted story—one that engineers, product owners, and leadership can all rally behind.

Data Pipelines Are the Editors

Great stories don’t just happen—they’re edited, refined, and published. Observability data pipelines serve that role: enriching, shaping, filtering, and routing the raw signals into something useful.

Poor tooling or unmanaged data flows? That’s like publishing a novel with broken grammar and missing chapters.

We’re fortunate to work with platform partners who provide the tooling to shape data into readable, meaningful stories—tools that empower teams to see, understand, and act.

Know What “Done” Looks Like

Deploying observability tools without a clear strategy is like writing a story without knowing the ending. You’ll end up with scattered chapters, mixed messages, and frustrated readers.

That’s why success in observability starts with a clear definition of “done”—what insights you need, what outcomes you expect, and how your strategy supports the business. Otherwise, you risk deploying tools for the sake of it, rather than solving real problems or delivering real value.

Here are industry-specific examples that show how observability with context turns raw data into meaningful stories that drive business outcomes. Each highlights how telemetry tied to business logic and context enables better decisions, faster recovery, and stronger customer experiences.

🚢 Shipping & Logistics – Lessons from Maersk’s Digital Journey

Contextual Observability Story: During my 7 years at Maersk, I was part of a massive digital transformation as the company shifted more and more of its global logistics business online. In such a fiercely competitive industry, everything starts with quoting and pricing—the moment a customer wants to move goods from point A to B. That digital experience has to be fast, accurate, and responsive.

But that’s just the beginning. The real challenge lies in tracking containers as they move across the globe—through ports, customs, rail networks, and ocean schedules. Delays can come from anywhere: a congested port, a misaligned customs clearance, or a temperature-controlled container going out of range. And every delay or deviation can trigger penalties, lost trust, or even broken SLAs.

What observability made possible was not just technical alerting—it was business storytelling:

• Why was this shipment late?

• Did the tracking API fail, or did a legal hold in customs block it?

• Did the reefer (refrigerated container) go out of temperature range due to a hardware fault or a telemetry gap?

• Were we routing correctly, or did a carrier handoff break down?
  

Observability provided that context, helping us stitch together data from tracking systems, customs integration layers, vessel schedules, temperature sensors, and customer-facing apps—all into one clear, actionable narrative.

Business Outcomes Measured:

• On-Time Delivery (OTD) rate

• Temperature compliance & reefer health

• Quoting speed & win rates

• Exception management resolution times

• Customer experience (NPS, portal usage, ticket volumes)
  

With proper observability, we weren’t just troubleshooting systems—we were ensuring the world's goods moved reliably, customers stayed informed, and penalties were avoided. And ultimately, it allowed Maersk to compete on customer experience, not just capacity.

🏦 Banking & Financial Services

Contextual Observability Story: A failed transaction isn't just a failed POST request—it's a potential lost customer. Context includes customer journey ID, account type, payment network latency, and recent fraud flags.

Business Outcome Measured:Payment success rate, abandonment rate, fraud prevention effectiveness. Observability helps trace issues back to specific networks or app versions, not just surface-level service errors.

🛡️ Insurance

Contextual Observability Story: Quote engines rely on data from multiple risk models, third-party APIs, and customer input. If pricing decisions slow down or fail, it could be due to missing underwriting data, expired API tokens, or location-based scoring failures.

Business Outcome Measured: Quote-to-bind conversion, time to quote, policy issuance speed. Observability helps detect when a specific scoring engine is causing quote lag or mispricing.

👔 Recruitment / HR Tech

Contextual Observability Story: Candidates dropping out of job applications mid-process? The issue could stem from UI load time, slow resume parsing, 3rd-party assessment APIs, or mobile incompatibility—each of which leaves a trace.

Business Outcome Measured: Application completion rate, recruiter response time, job match accuracy. Observability helps identify where in the candidate journey things slow down or break trust.

⚡ Energy & Utilities

Contextual Observability Story: A spike in support calls after a smart meter rollout may correlate with data ingestion latency, incorrect meter readings, or billing misalignments caused by time zone errors in processing pipelines.

Business Outcome Measured: Billing accuracy, time to resolution, customer churn.Observability helps tie together telemetry from field devices, cloud processing, and billing systems to quickly isolate issues.

🛍️ Retail & E-commerce

Contextual Observability Story: A dip in checkout conversions isn’t always due to a frontend bug. It could be inventory system lag, promo code validation errors, payment gateway slowdowns, or misfired A/B tests—all of which are observable with the right context.

Business Outcome Measured: Cart abandonment rate, sales conversion, NPS. Observability helps paint the full picture of the digital shopping journey, from search to sale.

Final Thought

Observability is not just data—it’s the story of your systems, your customer experience, and your reliability. Make it readable. Make it meaningful. And most importantly, make sure the story you're telling aligns with the outcomes your business needs.