(FGC/Shutterstock)
In today’s data-driven world, data observability is a key concept for organizations looking to effectively manage their data. Simply put, it means having the ability to constantly monitor and understand the status of your data. This includes tracking where it comes from, where it’s going, whether it’s being sent on time and in the right quantities, its quality, and recent changes in behavior. Data observability helps answer important questions about your data and ensures data reliability. This article details what data observability is, why it’s important, the benefits it brings, and the right time to implement it.
What is data observability?
Data observability is the ability for an organization to see and understand the state of its data at any given time. “State” means where it’s coming from and where it’s going in the pipeline, is it moving on time and in the expected amount, is the quality high enough for the use case, is it working properly? Or whether it is working properly. Has it changed recently?
Some questions that can be answered regarding data observability are:
- Are your customer tables getting new data on time or are they lagging?
- Are there duplicate shopping cart transactions and how many?
- Was the huge drop in average purchase price just a data issue, or was it real?
Data observability examines different aspects of data, including values (Summit Art Creations/Shutterstock)
- Will removing this table from the data warehouse affect anyone?
Observability platforms are designed to provide a continuous and comprehensive view of the state of your data as it passes through your data pipeline, making these questions easy to answer.
Common data observability activities include monitoring the operational state of data to ensure it is current and complete, and detecting and uncovering anomalies that may indicate data accuracy issues. This includes mapping data lineage to upstream tables to quickly identify the root cause of problems, and mapping downstream lineage to tables. Analytics and machine learning applications to understand the impact of problems.
When data teams unlock these activities, they can systematically understand when, where, and why data quality issues occur within their pipelines. That way, you can prevent those issues from impacting your business and work to prevent them from occurring in the future.
Data observability enables these fundamental activities, making it the ultimate data wish list for any organization: healthier pipelines, data teams with more free time, more accurate information, more It’s the first step to a happy customer.
Why is data observability important?
Organizations are constantly pushing to better leverage data for strategic decision-making, user experience, and operational efficiency. All of these use cases assume that the data being run on is trusted.
Data observability is the purview of the data team (Gorodenkoff/Shutterstock).
In reality, all data pipelines experience failures. The question is not “if” but when and how often.What is the data team? can Control is how often problems tend to occur, how big their impact is, and how stressful you feel when resolving these failures.
Data teams lacking this control lose trust in the organization, limiting the organization’s willingness to invest in analytics, machine learning, automation, and more. On the other hand, data teams that consistently deliver reliable data can earn the trust of their organizations and make the most of their data to drive their business forward.
Data observability is the first step to gaining the trust of your organization and ensuring the level of control you need to ensure a reliable data pipeline that ultimately extracts more value from your data. It’s important because it is.
What are the benefits of data observability?
What do you get when you have complete observability of your data pipeline? The bottom line is that data teams can ensure that the data that reaches the business is fresh, high quality, and reliable. . This increases the reliability of your data.
Let’s take a closer look at the specific benefits of data observability.
- Mitigating the impact of data issues—If a problem occurs, it will be understood and resolved faster. Ideally before reaching a single stakeholder. Data outages are always a risk, but observability greatly reduces their impact.
- Reduces firefighting efforts for data teams—Spend less time responding to and remediating data outages. That means a lot of your time will be spent building things, creating automation, and other fun parts of data engineering and data science.
- Increased stakeholder trust in data– Once your analysis no longer shows questionable data and you no longer hear about problems with your ML model, you can trust the data and believe it is suitable for making decisions about or integrating into your products and services. Start thinking.
Data observability started as a data pipeline and table testing tool before becoming its own product category (Tee11/Shutterstock)
- Increased investment in data from businesses– When stakeholders can trust their data, they can feel confident using it in more places across the business. This means you can allocate more budget to your data and data teams.
History of data observability
The concept of “data observability” emerged in the late 2010s. It was initially inspired by internal efforts to improve data quality and monitor data pipelines and tables at companies like Uber, Netflix, Airbnb, and Lyft.
Most of these data teams first developed some kind of pipeline testing system before moving on to developing true data observability tools.
Ultimately, small and medium-sized businesses with light technical teams also woke up to observability features. However, we did not have the capacity to build these solutions in-house. Therefore, data observability SaaS solutions were created to fill the gap.
Data observability and you
Is your organization ready for data observability? You may be facing one of the following situations:
A high severity data failure has occurred
The most obvious time to invest in data observability is immediately after an outage is resolved. Every organization is busy, and getting buy-in to take precautions against future outages can be difficult. The moment after an outage is the perfect time to invest in data observability, as all parties agree on preventing future problems from occurring.
Pipeline becomes complex
Is data observability in your future? (ZinetroN/Shutterstock)
Teams can’t wait to be caught off guard by inaccurate, corrupted, or outdated data. His one change in the schema can cause a violent uproar, with catastrophic consequences. Change means growth, but it also means unpredictability. Data observability is technology’s answer to that unpredictability. Data observability platforms introduce predictability and reliability to complex data pipelines. You can’t manually keep your data catalog up to date using spreadsheets or occasional debriefings. You need better visibility into your data pipeline and anomalies as soon as they occur.
Moved to a hub-and-spoke data team structure
Data observability helps teams understand how their work fits into the larger data puzzle within the organization. Schema changes, new data sources, and pipeline additions are tracked and communicated with data observability. This helps teams understand the impact of changes that may seem small but can have large ripple effects. Data observability is an effective communication tool. As data writes out the story, data observability acts as a transcript.
In an era where data influences every decision, data observability is the foundation of data quality and reliability. This allows organizations to not only quickly identify and fix data issues, but also prevent them from occurring in the first place. As data systems become more complex and organizations adopt specific data team structures, the need for data observability becomes more apparent. By investing in data observability, organizations can reduce the impact of data issues, spend less time responding to issues, gain stakeholder trust, and increase commitment to data-related initiatives. You can attract investment. Data observability efforts began with tech giants like Uber and Netflix, but are now within the reach of organizations of all sizes through his innovative SaaS solutions. If you’ve experienced a data outage, are working on a complex data pipeline, or have migrated to a specific data team structure, now is the time to learn about data observability for a data-driven future. This may be a great opportunity to hire.
About the author: Kyle Kirwan is the co-founder and CEO of Bigeye, a provider of data observability tools.in
During his career, Kirwan was one of Uber’s first analysts. So he launched his Databook, the company’s data catalog, and other tools used by thousands of internal data users. He then co-founded Bigeye, a Sequoia-backed startup that works on data observability.I’ll get it to Kyle. on Twitter @kylejameskirwan or on LinkedIn.
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