Busting Data Observability Myths

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By Rohit Choudhary, co-founder and CEO of Acceldata

By now, everyone’s heard the expression “every company is a data company.” 

It’s true. Data has become the lifeblood of every organization, and large enterprises are contending with petabytes of it. More data means more opportunities, and also more challenges. 

When juggling this volume data, it’s critical to have a 360-degree view into data health, processing, reliability and pipelines. Thankfully, data observability tools have emerged to help companies do just that. 

According to Gartner, data observability can be defined as, “the ability of an organization to have a broad visibility of its data landscape and multilayer data dependencies (like data pipelines, data infrastructure, data applications) at all times with an objective to identify, control, prevent, escalate and remediate data outages rapidly within expectable SLAs.”

Gartner goes on to note that “data observability uses continuous multilayer signal collection, consolidation and analysis to achieve its goals as well as to inform and recommend better design for superior performance and better governance to match business goals.”

Think of data observability tools like the many sensors that exist to keep complex machines, like planes, in safe and working order. The largest commercial airliner is equipped with upwards of 25,000 sensors. If an issue arises within any given system, pilots or maintenance are alerted to the problem so that it can be remediated before any serious consequences occur.

There’s no need to take the entire plane out of service since issues can be quickly pinpointed and fixed. Similarly, data observability tools give companies comprehensive visibility into their data so that they can identify, fix, and prevent issues, making the data stack more reliable. 

Data observability tools are quickly becoming table stakes for all enterprises. But despite this, there are still a few common myths and misconceptions surrounding the technology—let’s dispel them. 

1. Data observability and data monitoring provide similar benefits

Data observability tools are not to be confused with data monitoring tools, which are narrow and passive in scope and use pre-built rules and metrics to provide information about a sub-system or a system. Data observability tools, however, provide broad visibility aiding in the detection, investigation, and remediation of issues across multiple layers. 

Data monitoring tools are not sufficient and cannot provide the real-time insights and intelligence required to handle complex data pipelines and systems. For example, something like Oracle database monitoring is a niche monitoring tool for the Oracle database and cannot provide insights into a data cloud like Snowflake and a lakehouse like Databricks.

2. If a company has application performance monitoring (APM), it doesn’t need data observability

APM solutions focus on the health of applications and different types of telemetry data such as logs, metrics, and traces. But unlike data observability tools, APM cannot provide insights into the data layer and systems. Take an APM tool vendor like Dynatrace, for instance; it can’t provide data reliability or cost optimization for a data platform like Snowflake. 

Data observability tools, on the other hand, focus on the health and reliability of data across multiple layers including compute, infrastructure, pipeline, users, and spend. This empowers organizations to identify the root cause of a data problem, including why it happened, what is needed to fix it, and how to prevent it from occurring again.

3. Organizations that have a data catalog don’t need data observability

Data catalogs are exactly what they sound like—a catalog of a company’s data. While having an organized inventory of one’s data assets can be helpful for managing data, it’s just one small piece of the puzzle. 

Organizations still need data observability to tackle common data pains like scaling and performance issues, cost and resource overruns, and data quality and outage problems. When it comes down to it, data catalog tools can’t provide the level of deep insights that modern data teams need.  

4. Data observability is in its infancy

There’s a common misconception that data observability is a brand-new technology. In reality, enterprises have been deploying and reaping the benefits of data observability for over four years now. 

In fact, Gartner has included data observability in twelve different reports, and companies like Dun & Bradstreet, a leading provider of analytics, have been using data observability for years. Adoption is growing as more enterprises realize the benefits of data observability, but the technology already has a proven track record for providing value. 

In today’s economic climate, many companies are tightening their belts. They need solutions that help them run their business efficiently, smoothly, and reliably in order to maximize impact and keep customers happy. Data is every company’s most valuable asset, and data observability tools are indispensable for keeping an eye on data health and ensuring business continuity. 

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