Businesses in the Middle East are relying on data more than ever. Every decision, every digital service, every automated workflow depends on data moving through pipelines, dashboards, scripts, and platforms. But things break. Data arrives late. Dashboards stop refreshing. Pipelines fail without warning. And often, nobody knows why until the damage is done.This is why observability is becoming important. It gives you a clear view of how your data systems behave, where issues start, and how to fix them before they affect customers or decision-makers.And as companies in Saudi Arabia, the UAE, and across the region adopt cloud, AI, and automation, observability is moving from “nice to have” to “we need this to keep things running.”

What Observability Really Means

Observability isn’t the same as monitoring. Monitoring tells you what happened. Observability helps you understand why it happened.
  • Monitoring is an alert.
  • Observability is the explanation.
IBM describes observability as the ability to understand a system’s internal state by looking at the data it produces — logs, metrics, and traces. It’s the same idea used in cloud engineering, but now it’s just as relevant to data analytics teams.In analytics terms, observability means:
  • seeing how your pipelines behave
  • understanding the flow of your data
  • spotting problems early
  • knowing where to fix issues
  • improving reliability
  • making analysts more effective

Why Observability Matters for Data Teams in the Middle East

Digital transformation in the region is happening fast. Saudi Vision 2030, UAE Digital Government Strategy, and major investments in AI mean more organizations depend on data streams and automated analytics.That brings benefits, but also pressure. When a dashboard breaks, an entire department may stop. When a pipeline delays, forecasting becomes useless. When a model uses old data, decisions go wrong.Observability helps avoid that. It gives analysts, engineers, and managers more control. It turns analytics systems from “black boxes” into systems you can understand, measure, and trust.

Which Teams Benefit the Most?

Observability helps anyone working with data:
  • analysts
  • BI developers
  • data engineers
  • managers who rely on daily dashboards
  • marketing teams using automated reporting
  • finance teams using forecasting
  • operations teams relying on real-time data

The Three Pillars of Observability

Observability usually relies on three kinds of data. You need all three to fully understand your analytics systems. They are:

1. Logs

These are detailed records of events. A pipeline step fails. A connection times out. A transformation runs successfully. Logs tell you what happened and when.

2. Metrics

These are measurements. 
  • How long a job took. How many rows were processed?
  • How many errors appeared?
Metrics help you spot patterns and track performance over time.

3. Traces

These show the path data takes through your system. If your pipeline has five steps, traces show you how long each step took, where delays happened, and what slowed the process.Together, logs + metrics + traces give you a full picture. Without them, you’re guessing.

So, How Observability Improves Analytics Work

Here’s what observability helps you do in real daily work:
  • detect data quality issues early
  • catch pipeline failures before they reach stakeholders
  • understand slow dashboards
  • debug Power BI refresh problems
  • reduce downtime
  • improve reporting accuracy
  • make forecasting models more reliable
  • increase trust in your analytics
Most data problems are not visible. Observability makes them visible.

A Practical Workflow: How to Build Observability Step-by-Step

Here’s a simple approach that any organization can start with — even without advanced tools.

1. Collect the right data

Gather logs, metrics, and traces from:
  • Power BI refresh logs
  • SQL query execution logs
  • ETL/ELT pipelines (Power Query, Azure Data Factory, Python scripts, etc.)
  • Data warehouse ingestion jobs
  • Automation tools (Power Automate, Fabric Data Factory)
Start small. You don’t need everything at once.

2. Create simple dashboards

Build dashboards that track:
  • refresh success + failure rates
  • processing time
  • error messages
  • volume changes in datasets
  • data delays
  • unusual spikes or drops
Even a simple dashboard can prevent major failures.

3. Set alerts

Alerts notify you when something is off. For example:
  • “Dashboard refresh failed”
  • “Pipeline took longer than usual”
  • “Dataset volume dropped by 60%”
These alerts help you act before users complain.

4. Automate checks

Automated scripts can:
  • check data freshness
  • validate row counts
  • compare today’s output to yesterday’s
  • detect missing values
Automation keeps observability running even when teams are busy.

5. Document the context

Observability is more effective when you understand the business meaning behind your data.Answer questions like:
  • What decisions depend on this dataset?
  • What happens if this dashboard fails?
  • Who relies on this pipeline?
Context helps you prioritize which issues matter most.

6. Review and refine regularly

Observability is not a “set it and forget it” thing.Every month:
  • check what failed
  • add new metrics
  • improve alerts
  • document recurring issues
  • streamline the monitoring
Your data systems grow. Your observability must grow with them.In the Middle East, where organizations move fast and depend on real-time digital services, observability can prevent financial losses, customer frustrations, and operational delays.

Common Challenges — and How Observability Solves Them

1. “We don’t know why dashboards fail.”

Observability shows the exact failing step.

2. “Our data is not fresh.”

Metrics + alerts track refresh delays automatically.

3. “We only notice issues when users report them.”

Observability reveals problems early, not after damage happens.

4. “We spend too much time debugging.”

With traces, you see where the slowdown happened instead of guessing.

5. “Leadership doesn’t trust the data.”

A stable, observable system increases confidence.Observability Matters Now more than ever before, because AI, automation, and cloud services are becoming the standard in the region. As analytics systems grow, so does the risk of hidden issues. Observability is no longer just for data engineers. Analysts, BI teams, and managers all need to understand how the system behaves.If your teams don’t build these skills, analytics will remain fragile — hard to maintain, slow to debug, and difficult to trust.

How the IMP Helps You Learn Observability

The Data Analysis & Business Intelligence Diploma from IMP teaches the skills you need to manage and improve real analytics systems  the same systems that benefit from observability.You learn:
  • how data pipelines work
  • how to structure analytics workflows
  • how to check data quality
  • how to detect issues early
  • how to build reliable dashboards
  • how to automate monitoring and checks
These are real, practical skills that help you run analytics systems with confidence.So, if you want to train your team or upgrade your own skills, IMP can help you learn the foundations and apply them in real projects.