Data Observability: Data Management Best-Practice Explained

Haley Teeples September 13th, 2021

The best practices and methodologies to manage data quickly

Companies are in hot pursuit of the best practices and methodologies to manage their ever-increasing variety and volume of data quickly and efficiently. Once they discover what works best for them and their use cases, they can establish other management goals to help sustain and improve data reliability. Data Observability is a prime example of what a company can achieve to improve the reliability of their existing and future data. Data Observability means a company fully understands the health of its current data and is often used in combination with other best practices, like DevOps and DataOps, to maximize data pipeline productivity and eliminate data downtime. 

According to a Towards Data Science article by Barr Moses, Data Observability utilizes “automated monitoring, alerting, and triaging to identify and evaluate data quality, and resolve discoverability issues.” Automating data processes through machine learning and artificial intelligence results in healthy data pipelines and allows consumers to access clean data for proactive business decisions.

In the article, Moses goes on to explain that Data Observability research is consolidated into five pillars. Each pillar encompasses questions that provide data users with a holistic perspective on data health and help pinpoint data in the event of possible downtime.

• Freshness: Is the data recent? When was the last time it was generated? What upstream data is included or omitted?
• Distribution: Is the data within accepted ranges? Is it properly formatted? Is it complete?
• Volume: Has all the data arrived?
• Schema: What is the schema, and how has it changed? Who has made these changes and for what reasons?
• Lineage: For a given data asset, what are the upstream sources and downstream assets impacted by it? Who are the people generating this data, and who is relying on it for decision-making?

As mentioned previously in this blog, DataOps is an emerging data management methodology that combines the agile practices of DevOps with quality-driven manufacturing principles and operations management to optimize the data supply chain from source to consumer. This holistic approach to data management is the basis of Zaloni’s DataOps platform, Arena. In addition, the Arena platform provides companies with the tools needed to implement the DataOps methodology within their organization. For instance, DataOps brings together 1st and 3rd party data pipelines into one “single pane of glass” view to help build upon the holistic approach of Data Observability at a grander scale. This birds-eye view delivers what one may envision as an infinite cycle, much like an infinity loop (as seen below), that scales as data transformation occurs across pipelines. 

At Zaloni, we have drawn connections between DataOps and the Data Observability pillars through use cases we’ve implemented within the Arena platform. For instance, utilizing operational metadata can help a company obtain Data Observability in the platform and understand data freshness. Fresh data is incredibly beneficial for data consumers, as this data is traceable, up-to-date, and can be accessed immediately in real-time. Lineage is another pillar of Data Observability that contributes to data quality. Arena users can view the lineage of a data set to understand where that data originated from, any changes made over time, and the quality of the given data set. Lineage helps data stewards and analysts to quickly pinpoint potential errors in an environment and resolve any data quality issues at hand. 

Ultimately, a strong discipline of DataOps guides companies on the path to great Observability in the pillar areas. The increased visibility across a company’s entire data ecosystem allows data teams to overcome data complexity to increase time to insight, reduce costs, and maximize data success. 

On March 9th, 2022 Moa Passador, Zaloni’s Director of Solutions Engineering, will walk you through the value of end-to-end data lineage and will demonstrate how lineage beyond the data catalog provides data stewards and data citizens with complete observability of their data.

Save your seat for exclusive live insights here or click on the below image.

about the author

Haley Teeples

Haley Teeples is a recent graduate from North Carolina State University and has been working at Zaloni for over a year. She initially joined Team Z as a Marketing Intern and then worked as a Technical Documentation Intern on the Engineering team. With a clear passion for content creation and learning in the data management space, today, Haley serves as an associate on Zaloni's Product Marketing team.