Data Observability is essential for organizations that rely on accurate, timely, and consistent data regardless of the reason. Digital Transformation, Data Product Management, Data Governance, Business Intelligence (BI), Advanced Analytics, Artificial Intelligence (AI), and Machine Learning (ML) all depend on Data Observability.
Data observability refers to the process(es) of monitoring and understanding data assets' quality, reliability, and performance throughout the data lifecycle. Data observability is essential for data-driven organizations that want to ensure the trustworthiness and usability of their data products and services. For many companies today, this work is the sideline occupation of a few diligent, often overworked, employees working in the bowels of IT.
This blog post will explore data observability, why it matters, and how to achieve it using best practices and tools. We will also share some examples of how data observability can help you solve common data challenges and improve your data outcomes.
Data observability is a concept derived from software observability, which is the practice of monitoring and analyzing the internal state of a system or application using external outputs such as logs, metrics, and traces. Software observability helps developers and engineers identify and troubleshoot issues, optimize performance, and ensure the reliability and availability of their software.
Similarly, Data Observability is the practice of monitoring and analyzing the internal state of data assets using external outputs such as metadata, lineage, quality metrics, and alerts. Data observability helps data professionals and stakeholders identify and troubleshoot data issues, optimize data pipelines, and ensure data quality and reliability.
Data assets include any data sources, transformations, storage, processing, consumption, or delivery components involved in the data lifecycle.
Why does data observability matter?
Data observability matters because it enables data-driven organizations to:
Build trust in their data: Data observability helps ensure that the data is accurate, complete, fresh, and compliant, which increases the confidence and credibility of the data consumers and users. Trust in data is crucial for making informed decisions, delivering value to customers, and achieving business goals.
Reduce data debt: Data observability helps prevent and reduce data debt, which is the accumulated cost of poor data quality and management over time. Data debt can result in wasted resources, lost opportunities, increased risks, and reduced competitiveness. Data observability helps identify and resolve data issues early before they become costly and complex.
Accelerate data innovation: Data observability helps speed up the development and delivery of new data products and services by enabling faster feedback loops, easier collaboration, and more efficient debugging. Data observability also helps foster a culture of experimentation and learning by allowing data teams to test hypotheses, measure outcomes, and iterate quickly.
Three Dimensions of Data Observability?
Data freshness measures the timeliness of data (i.e., how up-to-date it is). Freshness also measures whether data reflects the current state of the real-world phenomena it represents. Data freshness can be affected by data ingestion frequency, data processing latency, and data retention policies. Data freshness can be monitored by tracking age, staleness, and lag metrics.
Data Availability measures accessibility and readiness for data. It is measured from the perspective of the apps, services, and users who need the data. Freshness and availability are closely related concepts in data product management.
Data lineage tracks data's origin, transformation, and destination (i.e., consumption) throughout the organization and across different systems, business units, and data products. Data lineage provides visibility into the data flow and dependencies, as well as the provenance and context of the data. Data lineage can help with Data Governance, Compliance, quality and issue management, and impact analysis (e.g., Data Privacy Impact Assessments). Data lineage can be captured by using metadata management tools, data catalogs, and data lineage graphs.
Data Lineage significantly benefits an organization in many ways. It is often used to improve Transparency and Compliance.
Transparency is an aspect of data that refers to how easily a process or user can trace data from source to consumption. It also considers the historical traceability of data as it changes over time.
Data compliance measures how well the data meets regulatory and ethical standards.
Data quality: This aspect evaluates the accuracy, completeness, consistency, validity, and usability of the data. Data quality can be influenced by factors such as data sources, data schemas, data transformations, and data consumption. Data quality can be assessed by using data validation rules, data quality indicators, and data quality dashboards.
Data accuracy measures whether the data is correct and consistent with the expected values.
Data completeness measures the degree to which the data is missing any records or fields.
Data usability identifies how well-documented the data is and whether or not it is easy to understand.