What is data observability? Definitions and tools

How many times a day do you look at information on a dashboard?

How many times a day have you wondered if the data is correct or not?

As data platforms become more and more complete, crashes and breaks become more common.

Data observability will allow you to spot and predict these crashes.

What is data observability?

Data observability is the process in which, with the help of a dedicated data observability tool, you “stand” over your whole data platform to monitor its health and performance. Data observability aims to find leaks and errors before there is any real impact on revenue or workflows

Data observability allows proactive identification, resolution, and prevention of data quality and any possible pipeline issues.

A data observability platform is a system designed to analyze the health, realiability, and performance of an organization’s data ecosystem.

This tool continiously monitors data pipelines and datasets. It ensures accurate and complete data by analyzing metrics, logs, traces, and metadata. If there are any data breeches or unusual data, the platform will send a notification so data engineers can analyze exactly where the error is an follow a lineage to see where data has been affected.

Data observability tools rely on automated monitoring and machine learning to detect anomalies and learn from them to predict anomalies in the future.

Data observability tools integrate with many data sources, ETL pipelines, data warehouses, and business intelligence tools to provide a unified view of data health. This integration helps data engineers, analysts, and data scientists maintain trust in the data and make informed decisions with confidence.

Data observability aims to monitor data to quickly assess where issues might have happened to give teams the context to solve them promptly.

In essence, a data observability platform acts as a central nervous system for modern data infrastructure, ensuring data systems are resilient, scalable, and reliable. It plays a critical role in enabling data-driven organizations to move faster, reduce data downtime, and deliver high-quality insights with greater assurance.

Essentially, data observability is your guarantee that the data you are looking at is correct and  data observability tool is your weapon against unreliable data and its consequences.

Data observability vs. data quality

Although data quality and data observability tend to be discussed together, they are very different. Both aim to improve the reliability of data, but they approach the problem from different perspectives and with different tools.

Data quality refers on the condition of the data (aka, its quality).

Quality data requires accuracy, completeness, consistency, timeliness, and validity.  For example, data quality could flag missing values in a customer database, identify duplicate records, or check if dates fall within an acceptable range.

The goal of data quality efforts is to ensure that the data is fit for its intended use in reporting, analytics, and operations.

While data observability’s goal is also to keep data reliable, it is a broader and more automated approach to understand data’s behavior. Data observability is essentially continuosly monitoring the data pipeline.

In other words, data quality reacts to problems once they have happened, while data observability proacitvely detects and responds to problems before they happen or while they are happening.

Data observability vs data governance

Data governance is a discipline in data management that focuses on keeping the integrity and security of data through a framework of policies, processes, standards, roles, and responsibilities.

It involves defining frameworks to ensure that data is used properly across the organization.

Governance addresses questions such as who owns the data, who can access it, how long it should be retained, and what quality standards it must meet.

On the other hand, data observability keeps data systems healthy and reliable through continuous oversight and automation.

While data governance sets the rules, data observability ensures those rules are being followed in practice by monitoring system behavior and alerting teams when issues arise.

The 4 pillars of data

Data is composed of 4 main pillars: 

Metrics

Metrics compose the internal characteristics of data.

Data can be characterized by certain properties and metrics, which vary based on the type of data. For numeric datasets, summary statistics such as mean, standard deviation, and skewness are used to describe their distribution.

Categorical data, on the other hand, relies upon summary statistics like the number of groups and uniqueness.

Metadata

The data about the data.

Metadata can be defined as data that provides information about other data, generally a dataset.

In other words, metadata is data that has the purpose of defining and describing the data object it is linked to.

Examples of metadata include titles and descriptions, tags and categories, information on who created or modified the data, and who can access or update the data.

‍Metadata has various applications in business, including:

1. Classification
2. Findability
3. Information security

Having a strong metadata management plan in place guarantees that an organization's data is consistent, accurate, and of high quality across different systems. Companies that employ an all-encompassing metadata management approach are more likely to base their business decisions on dependable data compared to those without any metadata management solutions.

Data Lineage

The dependencies between data.

Metrics and metadata can be used to describe a single dataset sufficiently. However, datasets are not isolated.

In fact, datasets are related to each other in intricate ways. This is where lineage becomes essential. Data lineage reveals how different file types and systems are interrelated, giving you a clear understanding of where something came from and its possible destinations.

‍Knowing downstream dependencies is essential to break down silos between different teams within an organization.

For example, consider a company with separate software engineering and data teams that rarely communicate with each other. The former may not realize how their updates could affect the latter. Through data lineage, teams can access downstream dependencies and overcome communication barriers.

Logs

The interactions between data and the external world

Metrics describe the inner qualities, metadata describes its external characteristics, and lineage traces dependencies. But how does the data interact with the “outside world”? This is where logs come into play.

Logs can be machine-generated or human-generated interactions.

On the one hand, machine-generated interactions with data can include data movement, like data being replicated from external sources to a data warehouse.

Machine-generated interactions also include data transformation, like dbt transforming a source table into a derived table.

On the other hand, machine-human interactions include interactions between the data and its users, like data engineers working on new models and data scientists creating machine-learning models.

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5 pillars of data observability

Data observability has 5 main pillars that answer 5 questions:

1. Freshness: Is my data up to date?
2. Volume: Do I have all the data I need?
3. Schema: Has the structure of my data been altered?
4. Distribution: Is my data in the correct place?
5. Lineage: Where does my data go and what does it affect?

Together, these capabilities help teams proactively identify anomalies, understand their root causes, and assess their impact on downstream systems and stakeholders.

Freshness

Freshness measures how current and up-to-data your data is.

Delayed data can disrupt reporting, decision-making, and automated processes.

A data observability tool monitors ingestion schedules, pipeline runtimes, and timestamps to detect lags or stale data. A data observability tool will allow you to make sure you always have insights to the most current data.

Volume

Volume tracks the amount of data that is going through your pipeline.

Sudden spikes or drops in your data volume could indicate several issues such as duplication errors, missing records or upstream system failures.

Your data observability tool will continuously monitor row counts, file sizes, and batch loads, data to  identify unexpected changes in data volume and quickly respond to potential data loss or overflow issues before they impact downstream systems.

Schema

Schema tracks the structure of your data so it stays consistent and compatible with its use.

Your data observability tool analyzes changes in structure including added or removed columns, changes in column names,data type mismatches, table structures, etc.

Schema drifts can lead to failed queries. Your data observability tool will provide notifications when there is an anomaly so it can be solved quickly before it affects the rest of the pipeline.

Distribution

Distribution tracks where the information is landing and if it in the correct place and/or pattern.

Your data observability tool will analyze statistical properties and value patterns within the data to asses if the data is falling in the right range.

Errors in distribution could occur due to poor data quality. Monitoring data distribution allows teams to detect subtle shifts in data quality that might not trigger a schema or volume alert but could still impact the data negatively.

Lineage

Lineage provides a map showing where your data passes.

When you have large amounts of data flowing through different tools within your data platform, if there is a crash at some point, it can cascade through your pipeline.

Lineage in data observability will allow you to track exactly where the contaminated data has been and which downstream assets have been affected, so data engineers can correct it quickly and efficiently and provide root cause diagnosis.

Features in a data observability tool

In order to successfully complete all five pillars of data observability, your data observability tool should have a list of features to ensure your data is reliable and high-quality.

• Automated data monitoring

This feature takes charge of constantly analyzing your pipeline.

It tracks key metrics such as data freshness, volume, schema changes, and value distributions. These checks run in the background, detecting anomalies or performance issues in real time, so that data teams are alerted to potential problems before they affect business outcomes.

• Freshness and timeliness monitoring

This feature tracks when data was last updated and whether it is arriving on schedule.

Freshness monitoring will guarantee that downstream data is relevant and up-to-date.

Data teams will be notified if there are any delays in data ingestion or processing trigger alerts, allowing them to act before users rely on outdated information.

• Anomaly detection

Thanks to automated monitoring, your data observability tool is able to detect anomalies.

Using machine learning or statistical models, data observability tools can automatically detect unusual patterns or behaviors in datasets.

These anomalies can be, for example, sudden drops in transaction volume, unexpected increases in null values, or delays in pipelines.

Anomaly detection will spot the issue and alert your data team so they can quickly correct the issue and prioritize them by importance.

• Schema change detection

Aside from anomaly detection, data observability tools can also detect changes in schema, for example, alterations in columns in a table structure or changes in data types.

Schema change alerts help teams adapt quickly and maintain compatibility across systems.

• Data lineage tracking

Data lineage tracking will allow you to see a detailed map of how and where data flows through your organization. Since its raw source until its final use, in reports or dashboards.

This feature allows users to understand dependencies between datasets, trace the root cause of errors, and assess the downstream impact of changes. It’s especially useful for debugging, change management, and auditing purposes.

• Root cause analysis tools

Advanced data observability platforms provide tools to assist with root cause analysis, helping teams identify where an issue originated and what caused it.

By analyzing lineage, metadata, and historical anomalies, these tools help reduce data downtime.

• Volume tracking

Data observability platforms monitor data volume to detect missing or duplicate records.

Unusual spikes or drops in row counts, file sizes, or event logs can indicate problems like pipeline failures or upstream integration issues.

• Alerting and notifications

Data observability tools include customizable alerting mechanisms that notify the right teams.

These alerts are typically enriched with context, such as affected datasets, timing, and severity.

• Data quality metrics

While observability extends beyond data quality, many tools incorporate built-in quality metrics such as null percentages, uniqueness, consistency, and value range checks.

These metrics are tracked over time to identify trends and spot degradation.

• Data trends

Observability platforms often include dashboards that show historical trends across key data health metrics. More advanced data observability tools will use these trends to “learn” from prior anomalies in order to predict them in the future and provide context.

Dashboards also provide a centralized view of the status of pipelines and datasets, useful for executives and stakeholders.

• Integrations with other tools in data platforms

In order for a data observability tool to work effectively, it should integrate with other tools in tour data platforms, for example, data warehouses, ETL/ELT tools, data lakes, and BI platforms.

Why do you need a data observability tool?

In short, you need to know your data platform is running smoothly and if, at any point, there is a failure.

You’re capturing data from many different sources. Large infrastructures break more often and chances are, it’ll take a long time to find where the crash is or even know there is one.

If there is an error at some point, it is most likely you won’t know exactly when it happened and it can take days (in the best case scenario) to track down each dashboard that has been affected by the error.

Don’t forget that data isn’t just words and numbers on a dashboard, it is the foundation of every decision within a company.

A data observability tool will bring your whole data platform together to avoid losing time, revenue and reputation.

Data observability will proactively detect issues while identifying data quality and pipeline issues before they can negatively impact the business, and offer root cause analysis.

Benefits of data observability

Evidently, your data observability tool has one major benefit: keeping the quality of your data.

With continuous monitoring and automated checks, data observability ensures that issues such as missing values, schema changes, or anomalies are caught early.

This leads to more reliable datasets, reduces data-related errors in reports, and builds trust across stakeholders.

However, observability encompasses many more benefits to your business.

• Increased revenue and reputation

When decisions are made with accurate and timely data, business strategies become more effective.

Observability helps prevent data downtime that can lead to missed opportunities, flawed forecasts, or customer dissatisfaction.

In other words, fewer losses due to inaccurate data, equals more gains.

Additionally, data leaks and incorrect information can damage your reputation. By providing consistent and high-quality data, you reputation can remain strong.

• Increased efficiency

By automating checks and reducing manual work, data observability eliminates repetitive tasks and rework.

Data teams spend less time sifting through endless reports to spot leaks and lineage, freeing up time to build new models to improve infrastructure.

• Faster Problem Resolution

Aside from freeing up time for new and more important tasks, data observability allows data team to solve problems in a fraction of the time.

With real-time alerts, root cause analysis, and data lineage, issues can be detected and resolved quickly and effectively.

Data teams are able to minimize the impact on downstream systems and users by prioritizing problems based on their severity and impact.

• Proactive data management

Rather than reacting to broken dashboards or frustrated users, observability allows teams to anticipate problems and take preventive action. This proactive stance significantly improves reliability and supports a culture of operational excellence.

By making data behavior transparent, actionable, and reliable, data observability becomes a key enabler of scalable, data-driven operations.

• Team collaboration

In any organization, each team has their own set of tasks that revolve around the data managed in the data pipeline.

Data observability provides a unified view of the data platform and a common source of truth to allow teams to work closer together and understand the impact of data throughout the entire organization. By aligning around alerts, dashboards, and data lineage, teams can solve issues much more efficiently.

Data observability provides shared visibility into the data life cycle, enabling engineering, analytics, and business teams to work from a common source of truth.

How to implement a data observability tool

Implementing a data observability tool should be done thoughtfully and carefully.

A data observability tool should align with your current infrastructure and your goals.

You can implement your data observability tool with 5 simple steps.

1. Assess your data ecosystem

Start by evaluating your current data stack.

Identify your data sources and your upstream data, data transformation tools, data warehouses, and BI tools. You should also consider your current data platform (if you have one) and what tools are involved in your pipeline.

This will help you decipher the quality and volume of your data, and your needs for downstream data.

When assessing your ecosystem, its important to consider where most issues occur in your pipeline and what solutions you’re looking for and who will consume the data,

2. Define goals and use cases

Before selecting a tool, clearly define what success looks like. Identify key use cases for observability such as:

• Catching schema changes before they break dashboards
• Detecting data delays or freshness issues
• Improving incident response time
• Ensuring data quality for regulatory compliance

3. Select the right observability tool

When choosing a data observability tool, consider 4 important features:

• Integration support
• Machine learning–based anomaly detection
• Alerting mechanisms
• Lineage and root cause analysis capabilities

4. Configure monitors and alerts

Define clearly what you wish to monitor through your data pipeline, taking into consideration the 5 pillars or observability: Freshness, volume, schema, distribution, and lineage.

You can also customize the channels where you wish to receive alerts and notifications.

5. Train and prepare your team

Data engineers, analysts, and data stewards should all be on the same page and understand the new tool and what it covers. At the end of the day, they will be the ones making the most out of your data observability tool.

Show them how to interpret metrics, investigate lineage, and respond to alerts. The more fluent your team is, the faster you’ll see results.

Additionally, plan who will be in charge of tasks and solutions to make the implementation into the team seamlessly and efficiently-

When choosing a data observability tool, make sure it offers a strong customer support team that can guide you through the more intricate parts of implementation or features with personalized demos.

Once your data observability tool has been integrated in your data stack, make sure to track its progress and make any adjustments that you need along the way to make the most out of it.

Data observability tools

1. Sifflet

Ratings ⭐⭐⭐⭐⭐

G2 4.4/5 | Gartner 4.5/5

Overview

Sifflet is a modern data observability platform that centres around the quality of your data.

With Sifflet, data teams with full-stack visibility into the health and reliability of their data systems.

Sifflet is the only data observability solution that incorporates context-aware intelligence, prioritizing anomalies by technical metrics and their potential impact on downstream systems and business use cases. Additionally, the platform will register past anomalies to build patterns and anticipate future spikes.

With the help of AI agents, Sifflet reads the signals in your metadata to monitor lineage, freshness, schema drift and usage throughout your stack to recommend strategic monitoring.

Sifflet offers 3 integrated technologies: a data catalog, data monitoring, and data lineage.

The data catalog ensures that all assets are documented, discoverable, and enriched with metadata, giving users a clear understanding of their data landscape.

Data monitoring continuously tracks the freshness, accuracy, completeness, and volume of datasets, using adaptive machine learning models to detect anomalies in real time.

Lineage tracking maps data flows across systems, allowing users to trace errors back to their source and assess the downstream impact instantly.

✅ Pros

• AI data observability agents
• Automated root cause analysis
• Smart alerting system to limit noise and highlight relevant incidents

❌ Cons

• Is better suited for larger enterprises
• Requires onboarding

2. Monte Carlo Data

Ratings ⭐⭐⭐⭐

G 4.4/5 | Gartner 4/5

Overview

Monte Carlo is one of the first data observability platforms, born in 2019.

The platform provides automated monitoring which allows data teams to catch anomalies early and reduce the operational cost of broken pipelines. Monte Carlo’s approach is heavily engineering-centric, aiming to be embedded into the DevOps workflows.

Monte Carlo does not offer a built-in data catalog, which makes it less context-aware compared to competitors like Sifflet. This limits users’ ability to automatically understand the business importance of a dataset or prioritize alerts based on how data is used downstream.

While Monte Carlo does include lineage and monitoring, it often lacks the business-layer perspective, which can make alert triage and resolution more labor-intensive.

Source: G2

✅ Pros

• Strong anomaly detection
• Lineage tracking
• Real-time alerts

❌ Cons

• Lacks native data catalog
• Limited business context
• Lacks prioritization in alerts

3. Acceldata

Ratings ⭐⭐⭐⭐

G2 4.4/5 | Gartner 4/5

Overview

Acceldata is a comprehensive data observability platform that focuses on the performance and operational health of data systems, providing insights into the entire data pipeline.

Acceldata allows organizations to optimize both data reliability and data platform efficiency, making it particularly valuable for enterprises struggling with complex data operations across hybrid and multi-cloud environments.

Acceldata supports performance observability, which helps teams detect slow queries, underutilized resources, and cost inefficiencies.

It also features built-in integrations with big data systems like Spark, Hive, Snowflake, and Databricks.

However, Acceldata does not offer a native data catalog or lineage that connects technical events with business outcomes.

Acceldata is ideal for enterprises where system performance, cost control, and infrastructure reliability are top priorities.

Source: G2

✅ Pros

• Monitors system health, query performance, and infrastructure bottlenecks
• Pipeline-level monitoring
• Strong integrations

❌ Cons

• Lacks metadata management
• Limited business context
• Less suited for analysts

4. Metaplane

Ratings ⭐⭐⭐⭐⭐

G2 4.8/5 | Capterra 5/5

Overview

Metaplane is a data observability tool designed for startups and mid-sized companies.

Metaplane focuses on schema monitoring, freshness, volume checks, and anomaly detection with minimal setup.

It integrates with modern data stacks like Snowflake, BigQuery, Redshift, dbt, and Looker, without managing a complex observability infrastructure.

Metaplane offers a "plug-and-play" approach, meaning that teams can start monitoring datasets without a long onboarding process.

Metaplane prioritizes developer experience and workflow integration, providing Slack-based alerting and GitHub-driven configuration.

It also uses machine learning models to detect statistical anomalies in data behavior, though its contextual understanding of business impact is more limited compared to platforms like Sifflet.

While Metaplane does offer basic lineage visuals and integrations with tools like dbt for metadata, it does not feature a full-fledged data catalog, this can make it harder for teams to understand how anomalies affect downstream business operations.

Source: G2

✅ Pros

• Fast onboarding
• Developer-centric UX
• ML-powered anomaly detection

❌ Cons

• Limited business context
• Basic lineage support
• Not scalable

5. Splunk

Ratings ⭐⭐⭐⭐

G2 4.3/5 | Gartner 4.4/5

Overview

Splunk is a data platform originally designed for machine data and log analytics, which has evolved into a broader observability and security operations tool.

Splunk offers strong capabilities in infrastructure monitoring, application performance management (APM), and log-based anomaly detection. It ingests massive volumes of machine data in real time, allowing enterprises to monitor system performance, detect incidents, and investigate failures across distributed environments.

Organizations use Splunk primarily for IT operations, security, and DevOps observability.

Splunk can ingest data quality signals from logs or metrics emitted by data platforms, but lacks native support for.

✅ Pros

• Scalable log ingestion
• Flexible dashboards and alerting
• Real-time monitoring

❌ Cons

• Not purpose-built for data observability
• No business context
• Designed for DevOps and IT users

Final thoughts…

Businesses are built on data and data platforms are getting more complex each day.

So it is crucial to make sure that your data platform runs smoothly and not waste time finding where the breech is and what it may have affected.

A data observability tool can:

• Detect anomalies
• Monitor pipeline health
• Trace data lineage
• Alert in real time
• Reduce data downtime

Ready to stop reacting to data issues and start preventing them? Try Sifflet today.

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