What Is Data Observability, and Why Do You Need It?
What data observability means, why it’s crucial, and why organizations and data teams need to pay attention to it.
Introduction
Several years ago, most organizations used simple data pipelines and adopted data infrastructure to handle a small amount of operational and nearly constant data extracted from a few internal sources. Today, the narrative is no longer the same — organizations now have an increasing number of data use cases, and many data products now rely on dozens or even hundreds of internal or external data sources. On the one hand, modern organizations have adopted big data infrastructures and advanced technologies to meet these growing needs.
On the other hand, the growing complexity of the data stack, sheer velocity, variety, and volume of data generated and collected paves the way for unknown unknowns and more complex issues such as schema changes, unexpected drifts, poor data quality, data downtimes, duplicate data, and more. The numerous data pipelines, various data storage options, and an array of enterprise applications also add to the complexity of data management.
Nowadays, data/analytics engineers and business executives responsible for building, maintaining, and operating data infrastructures and systems are often overwhelmed. More often than not, they try as much as possible to always keep the data systems operational and functional. Unfortunately, nothing is perfect, and large volumes of data can not always be 100% error-free. No matter how heavily data teams invest in the cloud or how advanced an analytics dashboard is, all plans are futile if unreliable data is ingested, transformed, and pushed downstream.
Additionally, modern data pipelines are somewhat interconnected and non-intuitive. As such, internal and external data could become faulty, inconsistent, inaccurate, missing, change abruptly, and eventually affect the correctness of other dependent data assets. When any of this happens, data and analytics teams need to be able to dive deep and figure out what is wrong and how they can resolve the data issue.
But without a holistic and comprehensive insight and visibility into the entire data stack and lifecycle, achieving this becomes a nightmare. This is where data observability comes in handy as a valuable tool that data teams and organizations need to ensure data quality, plus an effective and reliable flow of data in everyday business operations.
After researching and reading up several resources, I decided to share my thoughts on what data observability means, why data observability is important and why organizations and data teams need to pay attention to data observability. My target audience includes data teams and organizations looking to realize their data-driven vision and become data-driven in this new year. It’s also a good read for software engineers interested in data.
What Is Data Observability?
Observability is primarily used in software systems and engineering, but it’s also essential in the data niche. Software engineers monitor the health of their applications with tools such as AppDynamics, DataDog, and NewRelic — data teams also need to do the same.
Forbes defines data observability as a set of tools that allows data and analytics teams to track the health of enterprise data systems, identify, troubleshoot, and fix problems when things go wrong. In other words, data observability refers to an organization’s ability to maintain a constant pulse of their data systems by tracking, monitoring, and troubleshooting incidents to minimize and eventually prevent data issues, downtime and improve data quality.
Data observability comprises an umbrella of technologies and activities that collectively allows data and analytics teams to trace the path of data-related failures, walk upstream and determine what is faulty at quality, infrastructure, processing, and computation level. Eventually, this helps data teams measure the operative and effective use of data while helping them understand what’s happening across every stage of the enterprise data lifecycle.
In a way, I’d like to imagine data observability as a state where data professionals (i.e., data scientists, engineers, architects, executives, analytics engineers, e.t.c ) can quickly identify and solve data problems, experiment to improve and scale data systems, optimize data pipelines to meet business requirements, and strategies.
Like the three pillars of observability, data observability comprises the following five pillars — each pillar provides answers to a series of questions that enable data teams to gain a holistic view of data health and pipelines when combined and consistently monitored.
- Freshness: Did all the data arrive, and is it up to date? What upstream data is omitted/included? When last was the data extracted/generated? Did the data arrive on time?
- Volume: Did all the data arrive? Are the data tables complete?
- Distribution: Where was the data delivered to? How complete and useful is the data? Is the data reliable, trustworthy, accurate, and consistent? How was the data transformed? Do the data values fall within an acceptable range?
- Lineage: What are the downstream ingesters and upstream sources of a given data asset? Who is generating the data? Who will use the data for making business decisions? At which stages do downstream ingesters use the data?
- Schema: Is the data in the correct format? Did the data schema change? Who made changes to the data?
In the next section, I’ll briefly outline the importance of data observability and why every data team should incorporate it into their data stack and strategy.
Importance of Data Observability
“What you don’t monitor, you can’t measure, and what you can’t measure, you can’t manage or improve” is a phrase I coined when drafting this post and brainstorming about the importance of adopting a data-observability-driven culture.
This is to say that without visibility into data pipelines and infrastructures, data and analytics teams would be merely flying blind (i.e., they can’t fully understand the health of the pipeline and/or understand what’s happening between data inputs and outputs). This inability to understand what’s happening across the data lifecycle comes with several disadvantages for data teams and organizations.
As organizations become more data-driven, their data teams become bigger and specialized. In such scenarios, complex data pipelines and systems are more likely to break due to insufficient coordination, miscommunication, or concurrent changes made by members of the team. Oftentimes, data engineers often don’t get to work on revenue-generating activities because they’re always fixing one data or pipeline issue or trying to understand why a business dashboard looks out of whack. I know this can be a pain in the arse sometimes.
From eroded customer confidence, trust, and experience to lost revenue, reduced team morale, productivity, and compliance risk, organizations lose a lot when data pipelines break. As enterprise data systems become multi-layered and more complex, data observability answers the WHY questions behind broken pipelines and helps organizations speed up innovation, boost efficiency and eventually reduce IT costs by avoiding unnecessary over-provisioning and optimizing data infrastructure.
In addition, data observability helps data and analytics teams to monitor the health of data by maintaining the constant pulse of its distribution, volume, value, metadata, lineage, and freshness. When combined with effective implementation of alerting and monitoring, data observability can enable organizations and their data teams to quickly identify and recover from unplanned outages caused by data quality issues and increase the adoption of data products.
Furthermore, data observability provides an end-to-end view of data pipelines and eliminates data downtime by applying the best principles of observability and DevOps to the pipelines (i.e., using triaging, alerting, and automated monitoring to identify and evaluate discoverability and data quality issues). Fixing a data problem or broken data pipeline is likened to finding the needle in the haystack. When data teams don’t know that something broke in a data pipeline or data is altered, let alone how it happened, they’re more likely to compromise the integrity of experiments, analytics, and stakeholders’ trust.
Finally, I believe that organizations and data teams need data observability to ensure that the data quality, accuracy, value, and trustworthiness aren’t compromised and data pipelines aren’t broken. Without a robust data observability approach, data and analytics teams would still struggle to ensure consistent data and pipeline reliability using an iterative and agile methodology or perform focused root cause analysis that yields effective and quick resolutions.
Conclusion
Data Observability is pivotal for becoming data-driven. With the advancements in data observability techniques, the data governance strategies and data quality frameworks that were once “nice-to-have” philosophies are now actionable. To a considerable extent, I strongly agree with this post from Monte Carlo Data which states that data observability is one of the pioneers of data reliability and also the next frontier of data engineering.
When data and analytics teams measure and monitor data pipelines, they gain control over data at rest and data in motion. The teams also spend less time firefighting or debugging data pipeline problems and more time on important data initiatives. Eventually, this would lead to increased adoption of data products and trust across an organization (i.e., more employees become data consumers, knowing how and where to access the data they need to work. Business and C-level executives also learn to stop second-guessing analytics amd become heavily reliant on data for business decisions).
