The $1.2M Data Disaster: Why Basic Monitoring Isn’t Enough
In the world of data, what you don’t see can cost you—big time.
A major retailer learned this the hard way when a subtle data issue slipped through their systems, leading to a staggering $1.2M loss in just 72 hours. No, their servers didn’t crash. Their dashboards didn’t light up with red alerts. Everything seemed fine—until it wasn’t.
So, what happened?
Their monitoring system, like most, was designed to catch failures, not flaws. It missed:
✅ Schema drift – A slight change in how data was structured, breaking downstream analytics.
✅ Silent null values – Missing data fields that led to incorrect reports and faulty business decisions.
By the time they realized the issue, the damage was done—lost revenue, incorrect inventory orders, and customer dissatisfaction.
The Hidden Risk in Data Pipelines
Most companies rely on traditional monitoring to check if systems are running. But uptime doesn’t equal accuracy. Sneaky data issues like schema drift, missing values, or inconsistent formats often go unnoticed—until they snowball into costly problems.
This is where data observability comes in.
How OSD Prevents These Costly Mistakes
At OSD, we believe businesses shouldn’t have to play detective with their own data. Our end-to-end observability platform does more than just monitor—it proactively detects anomalies, schema changes, and silent data quality issues in minutes, not days.
With OSD, companies can:
✅ Identify schema drift before it disrupts operations.
✅ Catch silent null values before they corrupt analytics.
✅ Automate anomaly detection and alert teams instantly.
Don’t Let Data Cost You Millions
Data is the backbone of decision-making. But when bad data goes undetected, it can lead to millions in losses, incorrect strategies, and broken customer trust.
Is your business relying on outdated monitoring? It’s time for real observability—so you can catch the unseen before it becomes unfixable.
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💡 Want to see how OSD can safeguard your data? Let’s talk.
#DataQuality #Observability #RetailData #DataDriven