Starting as a thing for data scientists, efficient data management has now become crucial to the successful operation of many businesses. Data volumes rapidly increase as companies massively digitize their services, move to the cloud, and adopt automation. According to recent estimates, 402.74 million terabytes of data are generated daily, and chances are the volume will only keep growing.
How do you keep all this data in order, ensuring its freshness, accuracy, and reliability?
Data observability is a solution.
It's a new, powerful concept that emerged several years ago as a set of practices that help data teams evaluate the overall health of the organization's data. It's more than data governance or monitoring. Data observability brings a strong focus on data quality and efficient use.
More about data observability and how it differs from other data management approaches below.
What is Data Observability?
Data observability is the process of data monitoring for accuracy, health, and efficiency through specialized automated software. It enables data engineering teams to set up continuous delivery of reliable data for building data products or applying it across an organization for informed decision-making.
According to Gartner, Data observability is the ability of an organization to have broad visibility of its data landscape and multilayer data dependencies at all times with an objective to identify, control, prevent, escalate, and remediate data outages rapidly within expectable SLAs.
Why Does Data Observability Matter?
Observability enables data teams to detect and fix data quality issues as soon as they emerge, preventing any negative impact on the system's operation. This way, they solve some critical tasks most enterprises processing large data volumes face, including:
Improved data quality allows organizations to make crucial business decisions and use data to implement innovative technology, such as integrating machine learning algorithms.
Early issue detection and diagnostic to automatically detect and fix bottlenecks in data pipelines.
Reduced downtime of data-based softwareto provide better services to end-users and ensure business continuity.
Cost optimization through early detection of redundant data and pipeline analytics to manage expenses.
Beyond these business benefits, observability is becoming a necessity as old data quality monitoring practices cannot handle the scale of distributed, cloud-centric data infrastructures. Instead of an alert-based approach, organizations require data observability platforms that bring a high level of automation and operate in real-time independently.
We know that no team has the time and energy to continue to define and manage such data quality rules for the entire warehouse - whether the offering is packaged as a low code system, a pretty UX, or tests in dbt, or whether its SQL directly. Time of data teams is best spent bringing new data, new insights to advance the business and not chasing issues or managing data observability systems." - Sanjay Agrawal, Сo-founder and CEO at Revefi.
Five Principles of Data Quality: What It Takes to Have Reliable Enterprise Data
When explaining observability, it's essential to consider what elements of data quality it helps with. Overall, five principles, such as freshness, distribution, volume, schema, and lineage, allow you to measure and track data reliability and quality within your organization.
1. Freshness
Freshness is when data is updated in a timely manner. The data decays unevenly from industry to industry, so you must understand its cadence and detect any critical changes in real time. While data lags may not cause severe consequences for in-house teams, when the customer-facing data becomes stale, it could negatively impact the operation of your service and business reputation. End users will get information they cannot rely on to make critical decisions and will, predictably, cease using the service that supplies such data. Besides, stale data is a common reason for data pipeline disruptions, which is another reason you must set up continuous data updates.
2. Distribution
Distribution means data values are within normal ranges or, simply put, the data is trustworthy. When data is properly distributed, it's accurate, and the risk of data quality issues is minimized. On the other hand, distribution deviations may point out data quality issues or changes in the related data sources.
Recent research by Revefi revealed that data quality and cleanliness are the most significant challenges when working with data for 58% of IT professionals. 57% of the survey respondents have encountered inaccurate data, and an equal percentage have stated that low data quality has led to poor decision-making. Therefore, the distribution aspect is the core one to solve the basic concerns of engineering teams and those working with data.
3. Volume
Volume is about the correct number of rows in tables and data completeness. In a broader sense, volume refers to the amount of data your software systems generate and process through different pipelines. It indicates whether your data intake meets the required threshold and remains on the proper level. If, for example, the data volume looks too high or low, it can indicate the pipeline is broken, or the source system produces excessive data you may not need. This way, volume tracking tips you on how to balance data without overfilling your records with irrelevant or excessive values. It also helps to cut expenses on data storage and processing.
4. Schema
Shema represents how data flows are organized within the organization to enable monitoring and auditing of changes in data tables. It includes the format, type, and relationships between data assets, ensuring data consistency across multiple systems.
Besides providing a clear structure of your flows and pipelines, the schema allows you to optimize unnecessary resources. You can significantly cut data lake or warehouse expenditure by forbidding excessive data access and usage. Such was the case of ThoughtSpot: they cut their cloud data platform (CDP) spend by 30% thanks to Revefi.
5. Lineage
Lineage means assets across data upstream and downstream are properly connected, and interactions with data at different stages are tracked. Lineage helps data teams monitor the entire life cycle of data and detect the exact stage at which something went off. With data observability tools, you know exactly where anomalous or erroneous data appear, what activities caused it, and how it impacts downstream users.
These are the main aspects of cloud data practices that allow engineering teams to ensure consistently high quality and reliability of data across the system. They prevent the core issues and mistakes that can happen while you handle large volumes of data.
Data Observability Beyond Data Quality
However, ensuring data quality is only one use case for the data observability approach. In fact, as data observability keeps evolving, so do the observation categories now also covering such categories as:
Data quality
Data Pipeline
Compute
Utiilzation
Performance
Spend
It is also crucial for ensuring Data Governance and Data Compliance within an organization.
Data Observability vs Other Data Management Approaches
Observability is often confused with other data management approaches that allow tech companies and enterprises to keep their data assets in order. Even more so when the use cases grow, and the boundaries become blurred. However, it's worth differentiating observability from other methods as you may need to use it separately or, vice versa, combine several data management approaches.
Data Observability vs. Data Ops
Data Ops is dedicated to building and managing data pipelines. The similarity with DevOps isn't accidental, as Data Ops applies the same CI/CD approach (continuous integration and deployment).
So, what is data observability compared to Data Ops? These are data health monitoring processes and practices that enterprises can layer atop data pipeline development. Developers can glean meaningful insights from their data observability tools and reports on early signs of data anomalies and data issues.
Data Observability & Data Governance
Data governance sets unified enterprise-wide data quality standards and rules on how to sustain them. So, generally, governance is about setting strict policies that will meet stakeholders' strategic visions on how to ensure data integrity.
Observability platforms, in turn, provide the necessary means to instrumentalize quality monitoring and detecting and correcting issues. Moreover, data teams can take detailed and clear reports from observability tools to board discussions to prove that governance policies are met.
Overall, data observability and governance complement and support each other. The goal of a data governance program is to eliminate data silos and integration issues that can affect data observability efforts. On the other hand, data observability powers governance by monitoring any fluctuations in data quality, availability, and lineage.
Data Observability & Data Monitoring
Data monitoring leverages machine learning to recognize typical data behavior patterns and notify users of discrepancies. However, most external services monitor the pipeline performance without monitoring the data quality itself.
The data observability platform extends monitoring, allowing data engineers to check data integrity and correctness end-to-end. Such a comprehensive approach ensures the necessary consistency of data quality. It prompts specialists to decide whether they need to improve data sources or modernize ETL tools, or whether there's a problem with BI software that cannot process the incoming data.
Data Observability vs Data Quality
Another aspect to consider about "What is data observability and what is it good for?" is its impact on data quality. In our previous article about the most common data issues, we outlined 5 quality metrics:
Accuracy
Completeness
Consistency
Timeliness
Relevance
Data observability tools allow you to add context to these common data quality problems: you get insights on what happened, where, who's involved, and who suffers from the data downtime.
Even though data observability contributes to data quality, these are different aspects of master data management. While data observability practices can detect quality issues, they do not guarantee decent data quality, and you may need extra effort to eliminate the problems unless you use an advanced data observability platform.
Data Observability Best Practices: How to Adopt and Use It
For a data leader, the strategic question is whether they are investing "right" on their data stack (what's the ROI they are getting from their overall data investment) and how, where they should continue to invest in the future." - Sanjay Agrawal, Сo-founder and CEO at Revefi.
To start off right and implement a data observability platform with maximum return on investment, you must follow the rules. Make sure to choose the right tools, standardize data, adopt extensive automation, and use other best practices, including:
Establish data observability standards across your organization. Define the key metrics you will track and what indicators are appropriate. These metrics may include data quality, volume, latency, number of errors, and resource use. The combination should depend on your unique business needs and how your data pipelines operate.
Analyze your data infrastructure. Understand your data sources, storage, and how the data moves through the pipelines. Once you know the schema and relationships between multiple infrastructure components, you can identify potential pitfalls and set up observability practices to mitigate the risks.
Choose compatible tools. Once you know the metrics and infrastructure, select appropriate tools for data collection, analytics, and alerts. You must check whether potential options are compatible with your current systems and can withstand the existing and potential data load.
Standardize libraries. Use standardized libraries, frameworks, and tools to align your teams around standard practices and facilitate communication across departments. It will bring interoperability and consistency, allowing you to detect and fix data issues right away.
Regularly audit and update data pipelines. Make audits a routine part of your observability practices to detect bottlenecks before they start affecting your data flows. You must also modify data pipelines as your business grows and new requirements emerge.\
Teach staff how to handle data. Train all team members who access and modify data on how to do it right to minimize the risk of human error and improve data accuracy. You should also inform technical and non-technical team members how to interpret data and derive accurate insights from it.
These tips should help you build a reliable data observability pipeline and continuously improve it for even higher accuracy. Since data quality, freshness, and proper distribution are essential for successfully operating software that heavily relies on data, you must implement observability carefully with a well-planned strategy.
Data Observability Stack: Top Tools to Consider
Fortunately, you won't have to set up all observability processes manually and keep wasting time on manual tracking. Software providers offer specialized tools that allow enterprises and smaller businesses to quickly integrate a data observability platform and automate most data-related tasks. Here are several top data management tools to consider integrating.
ETL / Data Transformation
dbt Cloud
dbt Cloud by dbt LABS enables easy management of modern data environments. This platform helps data engineering teams visualize and improve data workflows by providing recommendations and a comprehensive view of the documentation and lineage.
Google Cloud Dataflow
Dataflow is a Google Cloud service that offers unified and serverless stream and batch data processing. It’s suitable for executing Apache Beam pipelines with autoscaling, real-time data insights, and automated resource provisioning and management.
BI Tools
ThoughtSpot
ThoughtSpot offers two BI tools—ThoughtSpot Analytics for quick AI-powered insights and ThoughtSpot Embedded for embedded analytics experiences. Both solutions allow enterprises to query and analyze data from various sources for actionable data-driven insights.
Tableau
Tableau is a BI analytics platform that facilitates data analytics and management and is renowned for outstanding ease of use, even for non-technical people. It unites multiple products for different industries and applications, so the offering is truly rich. Tableau is an excellent option for companies that are just starting their data journey and want a convenient tool to make use of their data assets.
Revefi AI Data Teammate
Revefi is a comprehensive platform for augmented data observability and data operations that takes 5 minutes to integrate. It covers all data observability aspects and has some extra features like AI-driven insights on critical data issues, automated anomaly detection, root cause assessment and incident management, real-time alerts on Slack, and real-time insights into resource use and related costs.
These are just a few top data tools you should consider implementing to ensure data trustworthiness in your organization. The final choice should depend on the thorough research of your infrastructure capabilities, data management specifics, budget, and business goals.
Revefi & the Future of Data Observability
Revefi provides traditional data observability value while also intentionally extending into adjacent data-related areas, helping data teams quickly connect the dots across data quality, usage, performance, and spend.
Revefi is a must-try if you want to establish full-fledged data observability and get started immediately. It provides:
A hassle-free zero-touch installation. Revefi connects to your CDW-based data stack to ingest your metadata and provides insights within minutes. There's no POC (proof-of-concept) needed.
Automatically deployed monitors. Zero-touch copilots start monitoring your data quality, spend, performance, and usage with no configuration or manual setup. There's no need for custom coding or poring through documentation.
Proactive data issue prevention. Predictive algorithms update you on data anomalies and errors before they affect co-dependent data assets or skew future calculations.
AI-powered root cause analysis. Get to the root cause 5 times faster compared to manual debugging. Automated root cause analysis provides a holistic view of the entire data lineage.
Data Usage. Ensure the all-time usability of valuable data assets. Consistent monitoring and evaluation prompt the data team to keep data sets lean, accessible, and debris-free.
Enhanced cost-efficiency of CDW use. Studies estimate that the indirect impact of implementing a data observability solution results in a ~10% decrease in annual cloud data warehouse expenses. With Revefi, you can cut CDW vendor's bills by 30% with a higher efficiency of cloud data storage use.
Revefi transforms the idea of data observability by converging the conventional, traditional Data Observability / Data Quality with additional monitoring of Data Usage, Data Costs, and Data Performance, thus elevating it from tactical into the strategic level that impacts the ROI and performance of the whole organization. Try Revefi for free to see how simple and convenient it is to resolve data issues 5x faster and slash your CDW costs by 30%.
What is data observability and why do enterprises need it?
Data observability is the continuous monitoring of data health across pipelines, quality, freshness, volume, and schema stability. Enterprises need it because data issues silently degrade analytics, ML models, and business decisions.
How does data observability differ from traditional data monitoring?
Traditional monitoring uses predefined rules and thresholds. Data observability uses ML-based baselines that learn normal behavior and detect deviations automatically, catching issues that no manually created rule would anticipate.
What are the five pillars of enterprise data observability?
The five pillars are freshness (is data arriving on schedule), volume (is the expected amount of data present), schema (have structures changed unexpectedly), distribution (are values within normal ranges), and lineage (where did the data come from).
How does data observability reduce the cost of data quality incidents?
Observability detects issues minutes after they occur rather than days or weeks later, dramatically reducing the blast radius of data quality problems and the engineering time required for root cause analysis and remediation.
What should enterprises look for when evaluating data observability platforms?
Key evaluation criteria include deployment speed, platform coverage across cloud data warehouses, anomaly detection accuracy, false positive rate, integration with existing alerting tools, and the ability to provide root cause analysis rather than just alerts.
