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How Data Tools Can Support Observability

While data has the power to bring about significant change and leave a lasting impact on society, it also has the potential to create headaches for those responsible for maintaining data quality.

Modern data systems are well-equipped to handle and store big data. However, the sheer volume of data that modern companies process makes simple data monitoring and querying ineffective. Most companies don’t store their data in a singular data warehouse. Instead, they may store their most sensitive data on secure internal servers while relying on external servers to handle the rest. Increasingly, companies rely on a mixture of internal and external tools and applications to operate, which can lead to missing logs and data quality problems.

Throughout this post, we will analyze how businesses can monitor, manage and better understand their data issues through a process known as data observability. In this article, we’ll provide an understanding of data observability, assess the benefits and challenges of data observability, and explain how data visualization tools such as Astrato can help businesses in their observability and data governance journey. 

What is data observability? 

Before we can discuss the trends, benefits, challenges, and how data visualization tools support data observability, we must first define precisely what data observability is. 

Data observability is best described as a blanket term for a number of activities and technologies that enable the understanding, diagnosing, and monitoring of data quality, and the overall health of your data across multiple applications, servers, and tools.

Data observability builds on the principles of data monitoring – which is the process of collecting and analyzing. However, where data observability differs from monitoring is that it’s not focused solely on capturing and displaying data, but instead on anomaly detection and helping your data teams to review logs and understand the health of your system. Once this has been achieved, a data observability platform will allow a data operations team (DataOps) to analyze inputs and outputs from your entire system infrastructure. 

What are the benefits of data observability? 

Data observability is vital to allowing a DataOps team to function as it helps organizations that have disconnected or siloed tools to get a better understanding of the overall health of their IT systems. Arguably, the biggest benefit of data observability is its ability to simplify root cause analysis by enabling end-to-end data visibility across your organization’s entire IT structure. This enables you to see  the entire picture of disparate systems, allowing IT teams to quickly identify a data quality issue or resolve a bottleneck in the data pipeline.

Unlike data monitoring which requires you to first identify the issues before you’re able to track them and determine the health of your systems (i.e., pre-defined metrics), data observability makes it possible for skilled IT teams to quickly repair or resolve issues that were previously unhidden – which results in a much faster resolution for your DataOps team.

Due to the ‘all-seeing’ nature of data observability, teams can identify issues in real-time and automate your system’s healing process, significantly reducing data downtime. 

Simply put, a good data observability tool gives a DataOps team a complete view of your organization’s data, allowing you to conduct better data monitoring and engineering while maintaining consistent data quality across all your organization’s data pipelines.

What are the challenges of data observability?

While there are several benefits of using an observability platform, it’s important to understand that implementing data observability tools can pose a few challenges along the way.

The most common challenge companies face when trying to use an observability platform, or a particular observability tool, is the existence of data silos. For data observability to work, it must be able to connect and integrate across all systems throughout your organization. However, many companies, when building their data pipelines, fail to integrate every data source. In turn, the effect of an observability platform will be diminished as it will be unable to conduct anomaly detection and resolve any potential data issues on other data pipelines that aren’t connected to one another. 

Even when there is integration between an internal and external data source, issues can still occur when using an observability platform, as the average company pools its data from hundreds of sources. 

How can data visualization tools support data observability? 

Data observability allows your organization to see the health of your data and various systems in real-time. So, what better way to complement your data observability platform than with a data visualization tool that delivers a best-in-class data visualization and analytics solution powered by live data?

Astrato is that tool. Our no-code, drag-and-drop functionality makes data analytics and insights accessible to all users, allowing your data teams to focus on the complexities of monitoring and ensuring data governance throughout your organization.

Furthermore, with Astrato being a cloud-based business intelligence solution, your teams can say goodbye to bottlenecks from legacy systems and data silos that render most observability platforms unusable.

To get the most out of any data observability platform, it must integrate with other tools to support the business’s goals. At Astrato, our data visualization solution can fully complement and support your data observability platform, allowing your business to improve data reliability but also say goodbye to any unknown unknowns that can hinder your decision-making process. 

So what are you waiting for? Get in touch with one of our team today to book a demo & learn more.