Cat or no cat, your enterprise data is not a thought experiment.

Is Your Data Trapped in a Black Box like Schrodinger’s Cat?

The Data Observer
5 min readSep 28, 2021


Back in college, you might have learned about the physics thought experiment that launched a thousand memes, Schrodinger’s Cat.

Imagine a cat sealed in a box, along with — hear me out — a vial of cyanide, lump of uranium, geiger counter, and a hammer.

As the uranium decays, it will trigger the geiger counter, causing the hammer to fall and break the vial, releasing poison gas and killing our feline pal.

So is the cat still alive? Here’s the crazy part. According to quantum physicists, the cat is both alive AND dead until we open the box.

(I told you this was a thought experiment.)

Digital Businesses Rely on Data

Your company’s mission-critical data is a lot like Schrodinger’s Cat. It’s trapped in a metaphorical black box into which you have very little visibility.

Although your data may not be “dead,” it’s likely clogged by bottlenecks, suffering from quality and consistency issues, and costing you way too much in resources and labor.

Your data wasn’t always like this. But enterprise data infrastructure has become much more complicated in the last decade as businesses undergo digital transformation. That means more applications and resources are added to environments, compute capabilities are rapidly scaled to meet increased demand, and integrations across data repositories creates new types of data sets that are created on the fly.

This shift to real-time, operational analytics is having a profound effect on your data. It’s no longer about a bunch of database servers in your data center. Now it may be your legacy cluster and thousands of compute cores processing mission critical data on AWS.

It’s no longer data warehouses crunching historic data for monthly reports; it’s real-time applications making key operational decisions in real time.

It’s no longer about using the same old data sources; it’s about gathering data from data streams, enterprise systems, millions of devices, and the plethora of new, real-time sources and delivering them for business consumption.

The amount and scale of activity is massive, and it’s only increasing. Enterprises need to validate this data and account for where it is and how it behaves. That’s why you need data observability.

The Limits of APM and One-Dimensional Data Monitoring

Observability started off as a trend among application performance monitoring (APM) tools. It took old-fashioned systems monitoring, which grew up in the data center and server era, and added a layer of analytical intelligence. This enabled APM tools to proactively infer and detect system issues before they become actual problems. And it helped IT teams cope with the fast-paced, dynamic and distributed nature of cloud-native application deployments.

But APM-based observability only goes so far. It doesn’t drill down sufficiently into the mission-critical data workloads and pipelines of today’s digital businesses to anticipate potential problems nor automate simple solutions.

Neither do the one-dimensional data monitoring solutions out there. Some of these tools are great at monitoring data at rest, but fail at detecting clogged data pipelines and other problems that rely on streaming data, or data in motion. Some can manage a single data platform well, but force you to buy half-a-dozen other tools if you want a full view of all your corporate data. Still others lack automation and predictive capabilities, creating extra work for data engineers, usually after the problems have already reared their ugly heads.

Proactively Detecting “Unknown” Problems

Your data operations have become too large and complex to be managed using weak, mismatched tools or manually by data engineering teams.

That’s where Data Observability comes in. It provides a 360-degree view of data at rest, when it’s being processed, and the data pipelines through which it travels. Comprehensive Data Observability keeps your data infrastructure in tip-top shape. In other words, Data Observability helps data engineers and analytics teams:

  • Observe: Detect patterns of potential problems (including unknown unknowns) across complex data environments by analyzing the external outputs of data operations, including performance metrics, metadata, utilization, and more.
  • Analyze: Infer from observations to improve data reliability, scalability and cost effectiveness. Much of this data, compute, and pipeline intelligence is difficult or impossible to capture without correlating events and using advanced analytics.
  • Act: Once you know what the issues are, you can quickly take action or employ automated actions suggested by your data observability platform to fix the problem, often before it impacts performance or causes a slowdown..

The Advantages of Data Observability

There’s no shortage of existing legacy tools that seem to do the things that data observability tools do. They may handle simple legacy relational databases and applications. But they are rarely up for the task of managing modern data repositories and pipelines.

Take the area of performance monitoring. Data observability tools can track data processing performance and not only predict a potential bottleneck, but automatically act to prevent the outage and ensure you meet your SLAs. They can also check your compute utilization in order to make sure you don’t overbuy unnecessary services or software. Modern data observability tools also resolve problems faster, enable more efficient scaling, and ensure streaming and other data is continually ingested.

Beyond performance monitoring is data management. Sure, there are data governance technologies, such as data catalogs and data quality tools. They tend to focus on known or expected issues, and only static data — not data in the pipeline. They still require IT to do a lot of granular, manual work.

True data observability tools go beyond legacy data cataloging and quality tools by monitoring for a broader range of data risks, including data in motion. They also automate many more tasks to reduce manual work, and use machine learning to sharpen their detection capabilities — areas such as data movement, structural change (schema drift), and data trends (data drift). In short, data observability tools can provide more powerful, efficient, and intelligent data management capabilities than their predecessors.

We can’t forget the area of data pipeline management, where data observability tools have no peer. They can make pipelines easier to manage by providing continuous data validation through its entire journey. They can also increase the speed of migration to the cloud, lower data storage and processing costs while achieving your SLAs. In other words, reduce the management time and cost of your data pipelines in the cloud era.

Photo by Luku Muffin on Unsplash