Building Data Observability: Monitoring Nulls, Drift, Freshness and Business Impact

In this article, we’ll explain why data observability is essential for reliable analytics, and how to build it into your data systems. We’ll dive into practical techniques for monitoring null values, data drift and data freshness. We’ll also discuss real-time anomaly alerting and how to tie data quality issues to downstream business impact. Whether you’re a data engineer or analyst, these strategies will help you ensure trust in your data pipelines and avoid unpleasant surprises in the boardroom. Monitoring Null Values and Missing Data One of the most common data quality issues is missing data often manifesting as NULL values in your datasets. These “pesky nulls” might occur because a source field was left blank. Nulls and missing records can wreak havoc on analysis. For example, imagine evaluating a marketing campaign’s sales lift by region, only to find the region field is blank for many records. Those rows get excluded from the analysis, potentially leading to misallocating your marketing spend because you lacked data for certain regions. In other words, incomplete data can directly translate to bad business decisions. NULL values region To monitor for missing data, teams commonly implement null value tests on critical fields. A simple but effective check is to validate that a given column has no (or acceptably few) nulls after each pipeline run. For instance, dbt (Data Build Tool) provides an out-of-the-box not_null test that will fail if any nulls are present in a specified column. Similarly, Great Expectations offers expectations like expect_column_values_to_not_be_null to ensure required fields are populated. These tests can be run as part of your ETL/ELT pipeline or CI process so that any null “explosion” (a sudden surge in missing values) is caught immediately. null value tests dbt not_null expect_column_values_to_not_be_null Beyond individual fields, it’s important to watch for missing data at the table level, i.e. data completeness. If an upstream job fails or a sensor stops sending data, you might end up with an entire partition or day of data missing. This often shows up as a drastic drop in row counts. Setting up volume threshold alerts can catch these zero-row scenarios. missing data at the table level In practice, combining field-level null checks and table-level volume checks provides robust coverage. Use automated tests to validate critical fields aren’t null, and track row count metrics over time to detect any sudden gaps. Many teams integrate these into their pipelines. The moment a null percentage exceeds a threshold or a data load is empty, an alert can be sent to the data team so they can investigate immediately. automated tests row count metrics Detecting Data Drift and Schema Changes Data drift refers to unexpected changes in your data over time. This can take two forms: schema changes (structural drift) and distribution changes (statistical drift). Both can be silent killers of data reliability. drift schema changes distribution changes Schema changes occur when the structure of data changes. A schema change can easily break downstream ETL logic or BI reports that weren’t expecting it. In our opening scenario, a new schema change in the source went undetected and caused a 20% discrepancy in a KPI precisely because no one realized a field had changed. To combat this, data observability solutions monitor schema metadata and issue schema change alerts. For instance, tools like Metaplane can send real-time notifications whenever a schema, table, or column is added, removed, or renamed in your data warehouse. Even without specialized tools, you can implement checksums or schema snapshots in your pipelines comparing the current schema to a previous version and alerting if there’s a mismatch. The key is to increase awareness for the entire data team whenever the shape of data changes unexpectedly. Schema changes schema change alerts increase awareness for the entire data team Data distribution drift focuses on the values within the data. Here, the data’s statistical properties deviate from the historical pattern. Such drift might indicate an upstream issue. It can also signal concept drift for machine learning models meaning the model’s training assumptions no longer hold as the input data has shifted, leading to degraded model performance. In fact, organizations use data observability to monitor ML model inputs and detect data drift before model accuracy suffers. Data distribution drift concept drift before To detect distribution drift, data teams employ statistical tests and anomaly detectors. A simple approach is to set acceptable ranges or validation rules for important metrics. Tools like Great Expectations and Soda allow you to define such rules to catch outliers and shifts. More advanced observability platforms use ML models to baseline your data’s normal behavior and raise alerts on any statistically significant deviation. Monte Carlo and Bigeye, for instance, apply ML-based monitoring to catch distribution anomalies and concept drift without predefined rules. For example, if 80% of your order IDs suddenly start with “TEST_” instead of a numeric pattern, that’s a red flag a basic test might miss. Anomaly detectors would flag this kind of pattern change immediately, signaling a possible upstream test data leak or schema issue. statistical tests and anomaly detectors distribution anomalies concept drift In practice, guarding against data drift means monitoring both structural changes and data quality metrics continuously. Set up checks for schema consistency at integration points (or use a tool that hooks into your warehouse’s information schema). Simultaneously, track key data distributions: volumes, averages, unique values, categorical counts, etc. A good observability system will alert you if, say, Column email became null for 90% of records or if daily transactions are 5σ above normal. By catching drift early, you prevent bad data from seeping into analytics and ML models unnoticed. In practice structural changes and data quality metrics Column email daily transactions are 5σ above normal Ensuring Data Freshness and Timeliness Data freshness is all about whether your data is up-to-date. Even perfectly clean and correctly formatted data can be useless if it’s stale. In dynamic businesses, there is an expectation of how recent data should be. freshness Monitoring freshness involves checking that data pipelines are running on time and that new data is arriving within defined latency SLAs. It’s often not enough to monitor pipeline jobs (since a job could run successfully but produce no new data). Data observability focuses on the data outputs themselves. A common approach is to track the timestamp of the latest record or the last update time of each table. latency SLAs jobs data outputs For a more custom approach, you can even schedule SQL queries or scripts that run at specific times to ensure data has landed. One example (using Snowflake) is to create a daily task that counts new rows and sends an alert if no rows were added since the previous day. In other words, if the data hasn’t been updated by a certain cutoff, raise a red flag. This can catch situations where an upstream feed is stalled or a pipeline job quietly didn’t run. Modern data observability platforms automate much of this by automatically tracking freshness as a metric. They will notify you of stale data, for instance “Dataset X has not been updated in > 2 hours (beyond its SLA of 1 hour)”. if the data hasn’t been updated by a certain cutoff, raise a red flag “Dataset X has not been updated in > 2 hours (beyond its SLA of 1 hour)” It’s worth defining different freshness requirements for different datasets based on business needs. Not all data needs to be real-time, but if a dashboard or model expects fresh data, treat its latency as a first-class metric. Some teams formalize this as freshness SLAs/SLIs. And as always, when a freshness breach is detected, alert the team or even the downstream consumers. There’s nothing worse than an executive discovering a dashboard is still showing last week’s numbers – your observability should catch that automatically before the meeting starts. freshness requirements freshness SLAs/SLIs before Real-Time Anomaly Detection and Alerting Observability isn’t just about detecting problems; it’s about alerting the right people in time to fix them. Real-time anomaly detection means your system is continuously watching data events and metrics as they happen and raising an alarm the moment something looks off. The faster you can respond to data issues, the less likely they are to impact end users or decision-makers. Real-time anomaly detection A robust data observability setup will include automated alerts for different types of anomalies: automated alerts If a scheduled ETL job fails or doesn’t run on time, an alert should fire (this is basic pipeline monitoring, often handled by orchestrators like Airflow or Dagster). If a data quality test fails (e.g. null check, schema validation), that should trigger an alert. If an anomaly is detected by a monitoring system (say, a drastic drop in volume or a distribution change beyond tolerance), an alert is critical. Many teams integrate anomaly detectors with communication channels: for instance, send a Slack message if a table’s row count falls below 10% of its usual value, or send a PagerDuty page if a mission-critical data source hasn’t updated in 6 hours. If a scheduled ETL job fails or doesn’t run on time, an alert should fire (this is basic pipeline monitoring, often handled by orchestrators like Airflow or Dagster). If a data quality test fails (e.g. null check, schema validation), that should trigger an alert. If an anomaly is detected by a monitoring system (say, a drastic drop in volume or a distribution change beyond tolerance), an alert is critical. Many teams integrate anomaly detectors with communication channels: for instance, send a Slack message if a table’s row count falls below 10% of its usual value, or send a PagerDuty page if a mission-critical data source hasn’t updated in 6 hours. anomaly Modern tools provide a variety of integration options for real-time notifications. Out of the box, popular orchestrators and monitoring platforms support Slack, email, SMS or incident management tools for alerts. The key is to wire up your data checks to these channels so that silent failures become loud. As one engineer put it, you’re not just monitoring pipelines you’re monitoring trust, so you want to know immediately when that trust might be compromised. silent failures become loud you’re monitoring trust It’s also important to implement contextual alerting to avoid alert fatigue. Not every anomaly is equally urgent, and bombarding on-call engineers with dozens of minor alerts can be counterproductive. Observability best practices include enriching alerts with context. contextual alerting In practice, achieving real-time alerting might involve a combination of systems: your pipeline orchestrator for catching job failures, a data quality framework for rule-based checks and an anomaly detection tool for statistical anomalies. By layering these, you get comprehensive coverage and timely alerts. Once alerts are in place, test them! Do fire drills or seed a fake anomaly to ensure the alerts go to the right channels and people, and that your team knows how to respond when the real ones hit. Connecting Data Issues to Downstream Business Impact Data observability isn’t just about tech metrics ultimately, it’s about protecting the business from data catastrophes. When something breaks in the data, there is often a direct downstream impact: business Dashboards and reports can break or mislead. If a critical dashboard is showing wrong figures due to bad data, executives might make decisions on false premises. Or, as in our opening story, a broken dashboard in a meeting can erode leadership’s trust in the analytics. Data downtime has real costs in lost credibility and delayed decisions. Machine learning models can degrade. ML models are only as good as the data fed into them. If an input data feed drifts or contains a surge of nulls, model predictions can become inaccurate, harming user experiences or business outcomes. For example, an e-commerce recommendation model that wasn’t updated with the latest inventory data might suggest out-of-stock products, hurting sales. Observability helps catch those issues (like stale or weird input data) before the model’s performance tanks. Operational processes and compliance can be at risk. In industries like finance or healthcare, a data quality issue might mean incorrect regulatory reports or audit failures. One case study described a fintech company that caught schema/freshness anomalies in regulatory reporting data just in time, avoiding compliance penalties. The business impact of undetected errors here could be fines or legal trouble. Dashboards and reports can break or mislead. If a critical dashboard is showing wrong figures due to bad data, executives might make decisions on false premises. Or, as in our opening story, a broken dashboard in a meeting can erode leadership’s trust in the analytics. Data downtime has real costs in lost credibility and delayed decisions. Dashboards and reports can break or mislead. Machine learning models can degrade. ML models are only as good as the data fed into them. If an input data feed drifts or contains a surge of nulls, model predictions can become inaccurate, harming user experiences or business outcomes. For example, an e-commerce recommendation model that wasn’t updated with the latest inventory data might suggest out-of-stock products, hurting sales. Observability helps catch those issues (like stale or weird input data) before the model’s performance tanks. Machine learning models can degrade. Operational processes and compliance can be at risk. In industries like finance or healthcare, a data quality issue might mean incorrect regulatory reports or audit failures. One case study described a fintech company that caught schema/freshness anomalies in regulatory reporting data just in time, avoiding compliance penalties. The business impact of undetected errors here could be fines or legal trouble. Operational processes and compliance can be at risk. To effectively tie data issues to business impact, leverage the lineage and impact analysis pillar of observability. When an anomaly is detected, lineage metadata can reveal which downstream assets depend on the affected data. This means you can quickly identify. lineage and impact analysis Another best practice is to quantify issues in business terms whenever possible. Instead of reporting “Null rate in Column ABC > 5%”, you might say “5% of customer records missing region data, impacting 3 dashboards.” This framing makes it clear which business areas are impacted (e.g. sales dashboard for Region performance) and prompts faster action. It also helps in post-incident review to estimate the cost of data incidents, reinforcing why investment in observability is needed (for example, X hours of data downtime might equate to Y dollars in missed opportunities). 5% of customer records missing region data, impacting 3 dashboards X Y In summary, always connect the dots from data quality to business quality. Data observability’s value is ultimately measured by preventing bad data from causing bad business outcomes. By reducing data downtime and catching errors early, you lower operational costs and protect revenue while maintaining stakeholder trust. Dashboards stay reliable, models stay accurate, and teams can confidently make data-driven decisions. lower operational costs and protect revenue Tools and Techniques for Data Observability Fortunately, you don’t have to build all of this from scratch. A growing ecosystem of tools and frameworks can help implement data observability practices: Data Observability Platforms: Full-stack solutions like Monte Carlo, Bigeye, Acceldata, and Sifflet provide end-to-end observability. They connect to your data stack and automatically monitor freshness, volume, schema, and distribution metrics using machine learning and rules. These platforms often include anomaly detection, data lineage, and automated root cause analysis out-of-the-box. Data Quality Testing Frameworks: Great Expectations (GX) is a popular open-source library for writing expectations (tests) on data. It allows data teams to declaratively define what “good data” looks like (e.g. no nulls, ranges of values, distribution constraints) and then validate your data against those expectations on a schedule or in a pipeline. Great Expectations integrates with many orchestrators and even notebooks, producing data docs and reports for test results. It’s like a unit testing framework for data, helping you catch issues early in the pipeline. dbt tests serve a similar role in the context of data transformation dbt comes with built-in tests for not_null, uniqueness, accepted values, etc., and the community has extended this (with packages like dbt-expectations) to cover more complex checks. By including tests in your dbt models, you essentially add quality gates that will fail the pipeline if data doesn’t meet expectations. These frameworks are excellent for known failure modes and business rules (for example, “if any order has a negative quantity, fail the test”). They are highly customizable and are code-first, which appeals to engineering-savvy teams. Anomaly Detection and Monitoring Tools: Tools like Soda (open-source Soda SQL) and Datadog’s monitoring (which now owns Metaplane) provide SQL-based and ML-based anomaly detection. Soda, for instance, lets you write checks in YAML/SQL to detect anomalies in data (e.g. “row count deviates from average by >3σ” or “column value distribution has changed”) and can send alerts to Slack on failures. Similarly, Metaplane (now part of Datadog) focuses on data observability with minimal setup it can monitor table metrics, detect drift, and send alerts, as well as track schema changes and even monitor data pipeline execution. These tools are useful if you want a managed solution but with more focus on anomaly detection rather than fully custom tests. Open-Source Metadata and Observability Platforms: OpenMetadata is an open-source platform that combines data cataloging with data quality and observability features. With OpenMetadata, you can define table- and column-level tests (including custom SQL or Great Expectations-based tests), run data profilers, and set up alerts on test failures across your data stack. It provides a central UI and dashboard to monitor data health in real time, plus it tracks lineage and usage of data assets. OpenMetadata essentially lets you implement observability in a unified way without a commercial license, integrating with tools like Great Expectations and dbt under the hood. This is a great option for teams that want an open-source, metadata-driven approach. Custom Metrics and DIY Monitoring: For some organizations, especially those with unique needs, a custom observability setup may be built using general monitoring tools. For example, you can instrument your pipelines to emit metrics (row counts, processing latency, error counts) to a time-series database like Prometheus, and then use Grafana to visualize thresholds and anomalies. Alerts can be set up in Grafana or via custom scripts to Slack/PagerDuty. Cloud providers also offer building blocks. Data Observability Platforms: Full-stack solutions like Monte Carlo, Bigeye, Acceldata, and Sifflet provide end-to-end observability. They connect to your data stack and automatically monitor freshness, volume, schema, and distribution metrics using machine learning and rules. These platforms often include anomaly detection, data lineage, and automated root cause analysis out-of-the-box. Data Observability Platforms: Monte Carlo Bigeye Acceldata Sifflet Data Quality Testing Frameworks: Great Expectations (GX) is a popular open-source library for writing expectations (tests) on data. It allows data teams to declaratively define what “good data” looks like (e.g. no nulls, ranges of values, distribution constraints) and then validate your data against those expectations on a schedule or in a pipeline. Great Expectations integrates with many orchestrators and even notebooks, producing data docs and reports for test results. It’s like a unit testing framework for data, helping you catch issues early in the pipeline. dbt tests serve a similar role in the context of data transformation dbt comes with built-in tests for not_null, uniqueness, accepted values, etc., and the community has extended this (with packages like dbt-expectations) to cover more complex checks. By including tests in your dbt models, you essentially add quality gates that will fail the pipeline if data doesn’t meet expectations. These frameworks are excellent for known failure modes and business rules (for example, “if any order has a negative quantity, fail the test”). They are highly customizable and are code-first, which appeals to engineering-savvy teams. Data Quality Testing Frameworks: Great Expectations (GX) expectations dbt tests dbt-expectations Anomaly Detection and Monitoring Tools: Tools like Soda (open-source Soda SQL) and Datadog’s monitoring (which now owns Metaplane) provide SQL-based and ML-based anomaly detection. Soda, for instance, lets you write checks in YAML/SQL to detect anomalies in data (e.g. “row count deviates from average by >3σ” or “column value distribution has changed”) and can send alerts to Slack on failures. Similarly, Metaplane (now part of Datadog) focuses on data observability with minimal setup it can monitor table metrics, detect drift, and send alerts, as well as track schema changes and even monitor data pipeline execution. These tools are useful if you want a managed solution but with more focus on anomaly detection rather than fully custom tests. Anomaly Detection and Monitoring Tools: Soda Datadog Open-Source Metadata and Observability Platforms: OpenMetadata is an open-source platform that combines data cataloging with data quality and observability features. With OpenMetadata, you can define table- and column-level tests (including custom SQL or Great Expectations-based tests), run data profilers, and set up alerts on test failures across your data stack. It provides a central UI and dashboard to monitor data health in real time, plus it tracks lineage and usage of data assets. OpenMetadata essentially lets you implement observability in a unified way without a commercial license, integrating with tools like Great Expectations and dbt under the hood. This is a great option for teams that want an open-source, metadata-driven approach. Open-Source Metadata and Observability Platforms: OpenMetadata alerts on test failures Custom Metrics and DIY Monitoring: For some organizations, especially those with unique needs, a custom observability setup may be built using general monitoring tools. For example, you can instrument your pipelines to emit metrics (row counts, processing latency, error counts) to a time-series database like Prometheus, and then use Grafana to visualize thresholds and anomalies. Alerts can be set up in Grafana or via custom scripts to Slack/PagerDuty. Cloud providers also offer building blocks. Custom Metrics and DIY Monitoring: custom observability setup Each of these approaches or tools can contribute to a holistic observability solution. In fact, many teams use a combination: perhaps dbt tests and Great Expectations for known data quality checks during pipeline development, and a platform like Monte Carlo or OpenMetadata for continuous monitoring and alerting in production. The tools are increasingly interoperable (for example, Great Expectations can be invoked within Airflow or Dagster, and dbt test results can feed into other monitoring dashboards). The best choice depends on your stack and requirements for scale, but the bottom line is that investing in data observability tooling greatly accelerates your ability to find and fix data issues. dbt tests Great Expectations Monte Carlo OpenMetadata bottom line Best Practices and Conclusion Building data observability into your data pipelines is a journey, but here are some best practices to keep in mind: Start with critical data assets: Identify the tables, feeds, or reports that are most important to the business (e.g. revenue data, key product analytics) and implement observability there first. This ensures that if anything goes wrong, you’re covering the highest impact areas and demonstrating value quickly. Define clear data SLAs: Work with stakeholders to decide freshness and quality requirements for each dataset. Document what “timely” and “accurate” mean in each case (e.g. daily sales data must be updated by 8am, no more than 1% nulls in critical fields, etc.). Use these as the basis for your tests and alerts. Integrate into existing workflows: Treat data observability as part of your pipeline development and operations, not an afterthought. For example, incorporate dbt tests or Great Expectations in your CI/CD process so that pipelines fail fast if data is bad. Integrate monitoring with your orchestrator (Airflow, etc.) and notification systems. This way, data checks run automatically and issues are flagged just like code issues would be. Assign ownership and triage processes: Decide who gets alerted for data incidents and how they should respond. Maybe on-call data engineers handle pipeline failures, whereas data analysts might handle content issues. Establish runbooks for common data issues. Essentially, bring incident management discipline to data problems. Balance rules and anomaly detection: Use a mix of explicit tests (for known expectations and business rules) and anomaly detection. This layered approach prevents both obvious errors and subtle drift from slipping by. It also helps avoid too many false alarms rules handle clear-cut validity issues, while statistical monitors catch only significant deviations. Continuously refine and expand: After covering the basics, iterate on your observability. Add new tests when past incidents reveal a gap. Tune alert thresholds to minimize noise. As your data landscape evolves, ensure your monitoring keeps up. Also, periodically review alert logs to see if certain checks are too sensitive or not sensitive enough. Foster a data quality culture: Encourage your team to view data quality issues with the same seriousness as application downtime. Celebrate catches of issues before they hit production. Provide business context in alerts so everyone internalizes the importance. Over time, a culture of proactive data observability will form, where issues are caught early and trust in data remains high. Start with critical data assets: Identify the tables, feeds, or reports that are most important to the business (e.g. revenue data, key product analytics) and implement observability there first. This ensures that if anything goes wrong, you’re covering the highest impact areas and demonstrating value quickly. Start with critical data assets: Define clear data SLAs: Work with stakeholders to decide freshness and quality requirements for each dataset. Document what “timely” and “accurate” mean in each case (e.g. daily sales data must be updated by 8am, no more than 1% nulls in critical fields, etc.). Use these as the basis for your tests and alerts. Define clear data SLAs: daily sales data must be updated by 8am, no more than 1% nulls in critical fields, etc. Integrate into existing workflows: Treat data observability as part of your pipeline development and operations, not an afterthought. For example, incorporate dbt tests or Great Expectations in your CI/CD process so that pipelines fail fast if data is bad. Integrate monitoring with your orchestrator (Airflow, etc.) and notification systems. This way, data checks run automatically and issues are flagged just like code issues would be. Integrate into existing workflows: Assign ownership and triage processes: Decide who gets alerted for data incidents and how they should respond. Maybe on-call data engineers handle pipeline failures, whereas data analysts might handle content issues. Establish runbooks for common data issues. Essentially, bring incident management discipline to data problems. Assign ownership and triage processes: incident management discipline Balance rules and anomaly detection: Use a mix of explicit tests (for known expectations and business rules) and anomaly detection. This layered approach prevents both obvious errors and subtle drift from slipping by. It also helps avoid too many false alarms rules handle clear-cut validity issues, while statistical monitors catch only significant deviations. Balance rules and anomaly detection: explicit tests anomaly detection Continuously refine and expand: After covering the basics, iterate on your observability. Add new tests when past incidents reveal a gap. Tune alert thresholds to minimize noise. As your data landscape evolves, ensure your monitoring keeps up. Also, periodically review alert logs to see if certain checks are too sensitive or not sensitive enough. Continuously refine and expand: Foster a data quality culture: Encourage your team to view data quality issues with the same seriousness as application downtime. Celebrate catches of issues before they hit production. Provide business context in alerts so everyone internalizes the importance. Over time, a culture of proactive data observability will form, where issues are caught early and trust in data remains high. Foster a data quality culture: In conclusion, achieving strong data observability is a game-changer for data-driven organizations. It shifts your operations from reactive firefighting to proactive assurance. By monitoring nulls, drift, and freshness in real time, and by alerting on anomalies with context, you can drastically reduce data downtime and prevent costly business mistakes. Analytics failures are more than technical glitches they are strategic risks and observability is your insurance against them. game-changer The call to action is clear: don’t wait for the next dashboard disaster or surprise ML glitch. Start small: pick one critical pipeline or dataset and implement observability checks and alerts around it. Many teams find that once they have visibility into one part of their data, scaling it out to others is much easier. Invest in the right tools and practices that fit your team, and make observability an integral part of your data engineering lifecycle. The reward is confidence confidence that you’ll catch issues early, that your data is reliable, and that your business can trust every dashboard, report, and model. In today’s competitive, data-driven landscape, visibility isn’t optional; it’s essential for survival. So roll out that first data quality test or anomaly monitor, and begin building a truly observable (and resilient) data pipeline. don’t wait for the next dashboard disaster or surprise ML glitch visibility isn’t optional; it’s essential for survival Happy monitoring, and here’s to no more data blind spots! Happy monitoring, and here’s to no more data blind spots!