Data Observability Defined

In recent years, businesses across industries have become more reliant on data not only for decision making but also expanded use cases that include the use of data (after having gone through transformations, of course) feeding directly into a product or customer interaction. As a result, the revenue implications of decision making or customer experiences make ensuring high-quality data crucial for success in this landscape. However, with the growing volume and complexity of data sources and unclear distribution of responsibility for good data hygiene, maintaining data quality becomes increasingly challenging.

What is Data Observability?

Data observability, based on but different from software observability, is a proactive approach that focuses on ensuring data quality by providing visibility and insights into data pipelines and infrastructure. A more colloquial approach to defining data observability is that it addresses these generic questions:

• Is the data “right”?
• How up to date is this dashboard?
• Why did the report break?

There’s a variety of ways that those questions could occur:

• A 3rd party API deprecating an endpoint
• An upstream transformation query that changed a referenced object in your reporting query
• An incorrectly entered value in a spreadsheet isn’t caught before the csv is fed into your ingestion pipeline and as a result, doesn’t take into account that the automated data type promotions turn your integer into a string.

As you can probably tell from the increasingly specific scenarios, if you keep pulling on the strings, there’s an almost uncountable number of ways that data quality issues can be introduced into an ecosystem. As the number of tools, upstream sources, and personnel counts grow due to support business growth, the number of ways that data quality issues can be created grows exponentially. 

To that end, data observability tools provide you a degree of visibility into your data at any given time, letting you know where issues exist, so that you can improve data quality. 

{{inline-a}}

Benefits of High-Quality Data

Adopting data observability practices offers several benefits to both businesses and data teams:

For businesses, good data quality ensures that decision-makers have access to reliable, accurate, and up-to-date information. This enables better-informed decisions, optimized processes, and the identification of new opportunities, driving growth and competitiveness. 

For data teams, data observability simplifies the process of identifying and resolving data issues, increasing efficiency and reducing time spent troubleshooting problems. With more time, teams can finally get to those model or infrastructure optimization, exploratory analyses, and new features that sit behind a stack of urgent requests to fix data quality from work queues.

If you’re looking to learn more about the driving principles behind data observability, decide whether it’s time to buy a tool, or just learn about trending data concepts, subscribe to our blog by entering your email address to the right hand side of this post or talk to our team directly. If you’re ready to experience time savings and increased trust in your data team for yourself, sign up here with a completely free account!