The way we manage, qualify, and utilize our data is constantly tested. With the amount of information we have at our disposal, managing and ensuring data quality has become a strategic lever for companies striving for excellence. How can we ensure our data management is flawless and the data quality on which we base our decisions is optimal? This is where data observability becomes an essential component.
Effective data management relies on the ability to have complete visibility and a deep understanding of systems, applications, and processes. This is where observability takes on its full meaning, by dissecting every detail, highlighting anomalies, ensuring data quality, and strengthening trust in every decision made. In this post, we’ll explore what data observability is, see how it’s different from database observability, understand the key observability metrics, and learn how it helps organizations ensure high data quality and integrity through some real-world examples.
What Is Data Observability?
Observability in the context of data refers to an organization’s ability to understand its data systems’ health and performance through continuous monitoring and insights. In other words, data observability is how an organization obtains a clear view of what’s happening behind the scenes and in the macro context. It allows you to identify problems, make informed decisions, and optimize your processes.
If you’re wondering whether observability and data monitoring are synonymous, we can tell you they aren’t. Monitoring ensures the overall integrity of a system, while observability relates to the internal state of a system, being more structural than practical and encompassing monitoring among its pillars.
The Role of Observability
Data observability is critical for modern data operations because it ensures systems are running efficiently, detecting anomalies, finding root causes, and actively addressing data issues before they can impact business outcomes.
Unlike traditional monitoring, which focuses only on system health or performance metrics, observability provides insights into why something is wrong and allows teams to understand their systems in a more efficient way. In the digital age, where companies rely heavily on data-driven decisions, data observability isn’t only an operational concern but a critical business function.
Understanding Data Observability Metrics
1. Data Freshness
Data freshness refers to data timeliness and temporal relevance. It provides insights into the extent to which data is updated.
Outdated data can lead to wrong decisions or incorrect analyses. It’s fundamental to ensure the data used is up to date and reflects the most recent reality. Mechanisms such as real-time monitoring, automatic updating, and data synchronization are necessary to ensure data freshness.
Key aspects of data freshness
- Lag time: The difference between when the data was generated and when it was ingested or made available for analysis.
- Expected latency: The usual time interval at which new data is expected—for example, every minute or every day.
If freshness metrics show a sudden lag, it may indicate the data pipeline is failing or there’s an issue in one of the upstream systems feeding the data.
2. Data Distribution
Data distribution refers to data accessibility and availability. In an environment where different systems and platforms can store data, it’s necessary to be able to access relevant data quickly and efficiently. Implementing data distribution mechanisms, such as replication, synchronization, and caching, helps to ensure data availability when needed.
Data distribution also addresses the challenges of scaling data access in high-demand environments. As organizations grow and more users require simultaneous access to datasets, it becomes critical to ensure consistent performance. Implementing smart caching mechanisms and distributing data efficiently across regions can help reduce latency and prevent bottlenecks.
Key aspects of data distribution
- Accessibility: Ensures data can be accessed across different systems and platforms without unnecessary delays or barriers. Data should be readily available to all authorized users, regardless of location or system.
- Reliability: Monitors the consistent and stable availability of data across the organization. Uninterrupted data access is crucial for maintaining workflow continuity, especially in mission-critical systems.
- Replication and synchronization: Ensures data is accurately replicated across distributed systems and synchronized in real time to maintain consistency.
3. Data Volume
Data volume tracks the number of records or the size of the data ingested over a specific period. It’s a major challenge in the field of data observability. With the proliferation of data from multiple sources, it’s essential to be able to manage and analyze massive datasets.
Big data tools and technologies are essential to manage the growing volume of data and extract valuable insights. The ability to process huge volumes of data is essential to ensure effective data observability.
Key aspects of data volume
- Expected data volume: Compares the actual volume of data ingested with historical trends and expected norms.
- Spikes or dips: Identifies any significant anomalies in data volume, which may indicate issues such as missing data or excessive logging.
Volume metrics are particularly important for systems depending on a steady flow of data, such as streaming platforms, Internet of Things applications, or financial transactions.
4. Data Schema and Consistency
A data schema defines the structure and logic of data, making it easier to analyze and interpret when the organization of data and its interconnections are understood. Data consistency ensures uniformity across systems and reliability in how the data is stored. Inconsistent data occurs when systems have conflicting records or formats, leading to discrepancies in analysis or reporting—especially in environments where data is integrated from multiple sources, such as different databases or platforms.
Beyond maintaining structure, data schema and consistency are pivotal for fostering collaboration across teams. When multiple departments rely on shared datasets, any inconsistency or schema drift can lead to fragmented insights or operational delays. By enforcing strict schema validation and applying automated checks, organizations can ensure their data remains uniform as it travels between different systems. This consistency is especially important in regulated industries, where discrepancies in data can lead to compliance risks and financial penalties.
Key aspects of data schema
- Cross-system consistency: Ensures data from different systems matches and doesn’t conflict.
- Schema consistency: Monitors whether data adheres to expected schema definitions—for example, correct data types are used and field names and formats remain consistent.
- Duplicate records: Detects duplicate records within or across systems, which can lead to inflated numbers or inaccurate analysis.
5. Data Lineage
Data lineage is the process of tracing the origin and trajectory of data from its creation to its final use. It helps organizations understand the origin of their data, the modifications it’s undergone, and its final destination. Lineage metrics are critical for ensuring transparency, accountability, and compliance with data governance standards.
Lineage plays a vital role in improving operational efficiency. When teams see how data is transformed across systems, they can more easily identify inefficiencies or redundant processes in the data pipeline. This transparency enables faster troubleshooting, as teams can trace errors back to their source instead of sifting through complex systems.
Key aspects of data lineage
- Upstream/downstream tracking: Allows teams to trace data flow across multiple systems, including where data originated and how it’s been transformed.
- Transformation history: Logs all the modifications and transformations applied to the data over time.
- Auditing and compliance: Provides audit trails to show who accessed or modified data and when.
Data Observability Tools and Platforms
Data observability relies on continuous telemetry tools. These tools monitor and collect data on systems and applications in real time. Continuous telemetry tools allow you to capture metrics, events, and logs from different sources, and then analyze and visualize them to gain a deeper understanding of system behavior.
To keep track of data observability components in modern systems, dashboards and alerting mechanisms play a key role in surfacing these metrics to the appropriate stakeholders. Dashboards visualize metrics in a centralized location, making it easier for teams to monitor the health of their data pipelines and systems. Alerts can be configured to notify teams whenever metrics exceed predefined thresholds or deviate from expected norms.
For instance, a dashboard may show real-time data on freshness, volume, and completeness, providing a complete view of the pipeline. Alerts can notify the data team if a data source stops sending records, query response times spike, or data freshness falls below an acceptable threshold.
Choosing data observability tools or a platform will depend on various factors, including the size of your infrastructure, the technologies you use, your budget, and your specific monitoring needs. It’s important to carefully plan your observability strategy to ensure you have the right tools and are collecting the right data for analysis and visualization.
Data Observability vs. Database Observability
When we talk about data observability, we’re focusing on monitoring the data that flows through systems. This includes ensuring data integrity, reliability, and freshness across the lifecycle of the data. It’s distinct from database observability, which focuses more on the health and performance of the databases themselves.
From ingestion to transformation, storage, and consumption across different systems, data observability focuses on the entire data lifecycle. It provides visibility into the quality, reliability, and integrity of data as it flows through data pipelines, ensuring the data remains accurate, complete, and timely throughout its journey.
On the other hand, database observability is specifically concerned with monitoring the performance, health, and operations of a database system—for example, an SQL or MongoDB server. This includes monitoring query performance, connection pools, memory usage, disk I/O, and other technical aspects, ensuring the database is running optimally and serving requests efficiently.
Data vs. Database Observability: Example
Imagine a company aiming to track real-time sales data from multiple sources, including its website, point-of-sale systems, and mobile app. With data observability, the company monitors:
- Whether the data from each source arrives in a timely manner (data freshness)
- Whether all fields in the data, such as product IDs, prices, and timestamps, are complete and match the expected schema (data completeness and schema consistency)
- Whether any anomalies arise, such as a sudden drop in sales data from one source
The company ensures the accuracy and reliability of its sales data as it flows into its central data warehouse, where they use it for analytics and reporting.
In the same company, their e-commerce platform relies heavily on an SQL Server database for storing and managing customer orders. With database observability, the company focuses on:
- Monitoring the performance of key SQL queries that retrieve order details or update inventory records
- Identifying queries that are consuming excessive resources and may need optimization
- Tracking memory and disk usage to ensure the SQL Server isn’t overloaded
- Managing connections to the database to prevent bottlenecks
In this case, SQL Server observability ensures the database remains performant and can handle the high volume of transactions efficiently.
MySQL Observability
MySQL observability focuses on monitoring the performance, health, and operations of MySQL databases. This includes monitoring query performance, connection pools, memory usage, disk I/O, and other technical aspects to ensure the MySQL database is running optimally and serving requests efficiently.
By implementing MySQL observability, organizations can gain insights into the following:
- Query performance: Identifying slow-running queries, which may be impacting overall database performance
- Resource usage: Monitoring the utilization of key resources, such as CPU, memory, and disk I/O, to identify potential bottlenecks
- Connection pooling: Ensuring connections to the database are managed efficiently to prevent contention and performance issues
- Replication and clustering: Monitoring the health and performance of MySQL replication and clustering setups to ensure high availability and data consistency
Case Studies of the Effectiveness of Data Observability
Data observability isn’t only an operational necessity but a source of multiple benefits. Decisions based on reliable data, proactive problem-solving, and cost optimization are a few of the benefits originating from this practice.
Here are three case studies showing the effectiveness of data observability.
1. Tesla Auto Insurance—Real-Time Driving-Based Pricing
Tesla has implemented a car insurance strategy in several U.S. states based entirely on real-time analysis of observable driving behavior from its customers. The sensors and autopilot software built into Tesla vehicles collect data to enable this evaluation. Tesla then uses this data to generate a monthly safety score.
According to Tesla, drivers with intermediate driving levels could save between 20% and 40% on their insurance premiums. Those with the highest safety scores could benefit from savings of 40% to 60%.
2. Optimizing Data Incident Resolution With Observability—The BlaBlaCar Experience
Accelerating the resolution of data-related incidents has become essential with the growing complexity of modern data stacks and pipelines. Data observability platforms integrate functionalities such as data traceability, query modification detection, and information correlation to precisely locate points of failure, whether at the system, code, or data level.
Data observability has enabled BlaBlaCar, the world’s largest carpooling network, to halve the time required to resolve issues. Kineret Kimhi, head of business intelligence and data engineering at BlaBlaCar, says they’ve nearly halved the 200 hours once needed to investigate root causes. With the root cause clearly identified, data engineers can now focus solely on the problem.
3. Fintech Success—Data Observability at Checkout.com
To make data useful, it must be available in a timely manner. Stale data can negatively impact organizations that rely on it. Although setting up freshness alerts for data isn’t technically complex, managing them on a large scale can be a nightmare. Data observability automates and scales these monitoring activities across an organization.
In the fintech sector, Checkout.com monitors over 4,600 datasets, supports more than 300 active data platform users each day, and handles more than 1,200 data build tool models executing various transformations daily.
According to Martynas Matimaitis, senior data engineer at Checkout.com, data observability is a crucial element of their daily operations.
Final Words
Data observability is a foundational practice for ensuring the health and reliability of modern data ecosystems. In an environment where data is the backbone of innovation and efficiency, adopting a comprehensive data observability strategy ensures organizations can maintain the reliability and quality of their data while staying ahead of potential issues that could impact business outcomes.