How a Leading Port Operator Transformed Data Reliability with Infarsight

Summary

One of North America's largest port terminal operators was facing a growing challenge: inconsistent, unreliable data across their critical analytics functions. With dozens of terminals, JV partnerships, and complex operational workflows, their data pipelines were stretched thin. Infarsight stepped in to build a business-as-usual (BAU) analytics operations layer, eliminating noise, restoring trust in daily dashboards, and freeing up analysts to focus on decision-making rather than data repair.

The Situation

In port operations, data is more than reports, it’s real-time readiness. From EBITDA to crane utilization, over 100 KPIs across financial, operational, HR, and enterprise analytics drive daily decisions.

But beneath the surface, data processes were fragile.

• ETL/ELT jobs failed quietly.
• Dashboards missed refresh windows.
• Analysts played detective more than analyst.
• Leaders hesitated to act on data they couldn’t trust.

The result: uncertainty at the moment decisions mattered most.

The Challenge

The analytics stack spanned:

• Financial KPIs: Revenue, Demurrage, EBITDA, P&L
• Operational Metrics: Turnaround time, Lift counts, Yard and crane efficiency
• HR Insights: Productivity, Overtime, Attrition trends
• Enterprise Views: Across divisions, JVs, and site-level breakdowns

Yet none of this worked without guaranteed data reliability.
The organization needed monitoring, automation, and peace of mind.

What We Did

Infarsight was engaged to implement a business-as-usual data operations model that ensured every KPI was both visible and trustworthy.

We focused on three core actions:

1. Real-Time Monitoring

Wrapped every ETL/ELT job with active observability to detect issues before dashboards broke.

2. Proactive Resolution

Introduced playbooks, alerting, and auto-remediation to handle feed failures without manual intervention.

3. Reliable Data Availability

Ensured complete, timely, and consistent KPI coverage across all analytics layers, from the terminal to the boardroom.

How We Did This?

Building trust in data doesn’t start with dashboards-it starts at the pipeline. Here’s how we helped a global port operator restore data reliability from source to insight:

1. Instrumented Every ETL/ELT Job

We wrapped every pipeline-from ingestion to transformation—with automated monitoring to detect delays, anomalies, and failures in real time.

2. Built a Self-Healing Layer

Instead of waiting for alerts (or angry analysts), we implemented retry logic, fallbacks, and failure-routing mechanisms to reduce manual interventions.

3. Centralized Logging & Alerting

We set up a unified observability layer-so one team could track issues across tools, sources, and time zones, without hopping between logs or emails.

4. Established Data SLAs

We worked with business teams to define what “good data” meant for them—then enforced SLAs with automated checks for freshness, completeness, and accuracy.

5. Created a Business-as-Usual Model

We didn’t build a one-off dashboard. We built an always-on reliability framework that scaled with new KPIs, sources, and teams—without skipping a beat.

The Results

Voice from the Ground

“Before Infarsight, data issues were always someone’s problem. Now, they’re just solved, quietly, automatically.”
Head of Analytics

Why It Matters?

This wasn’t a one-time fix. It was the foundation for sustainable analytics maturity.
With Infarsight, data reliability moved from reactive to routine.When your dashboards are predictable, your decisions can be bold.