Table of Contents
Author
Nihar Rout
Managing Partner 4DAlert
What is Data Observability?
Lets understand Data Observability and Why it is Important.
Data observability tools are an organization’s ability to fully quantify the health of the data, proactively detect issues, and quickly apply a mitigation plan before issues impact revenue, ROI, or brand reputation.
Data is the lifeline of modern enterprises. Organizations invest significant money and time in developing data analytics platforms and decision support systems to make timely business decisions.
Why is Data Observability Important?
In today’s data-driven world, data observability tools have emerged as a critical strategy for ensuring data reliability. Availability and quality of data directly influence a company’s decision-making and operational success. High-quality data can transform everything—from customer experience and profitability to internal workflows.
The practice of measuring and monitoring data systems to ensure their dependability, integrity, and accuracy is known as data quality monitoring. By applying the best practices of data observability, organizations can deploy remedial measures to resolve issues and prevent future occurrences. This helps them make stronger decisions, refine processes, improve marketing strategies, and optimize products and services
Data Observability Frameworks
Health Of Data Operation
Monitoring and measuring the health of data operations is crucial for maintaining reliable and high-quality data systems. Collecting metadata, runtime, frequency, and performance of data pipelines assists organizations in identifying:
1. Bottlenecks
2. Inefficiencies
3. Areas for improvement in data pipelines
4. Abnormalities in execution
This results in optimized data processing, increased operational efficiency, and agile data warehouse management.
Together, data observability and operations help organizations meet regulatory requirements by ensuring:
1. Accuracy
2. Consistency
3. Traceability
Data Flow Monitoring
The Data Flow Monitoring framework delivers metrics critical to objects in the data flow and provides insights into:
1. Data completeness — Was the data fully received for each object?
2. Data timeliness — Did the data arrive on schedule?
a. Freshness
This metric tells us how old the data is and when it was last updated. Without this visibility, business stakeholders can’t gauge the accuracy of their analysis. The older the data, the more questionable the results.
b. Volume
Monitoring data volume at every hop of a pipeline provides confidence in data completeness. If data volume varies unexpectedly, it may indicate pipeline issues. Real-time alerting can notify the right team members before poor data drives poor decisions.
c. Schema Drift
In well-managed environments, schema changes undergo approval and impact testing. But often, schema changes slip through without due process. Data observability tools should flag unauthorized changes and proactively assess their impact on pipeline execution.
Data Profiling
Even when pipelines run successfully and meet criteria for volume and timeliness, column- and row-level data profiling reveals insights into data quality rules. These help assess data health at a granular level and make insights actionable.
Data observability solutions should allow checks such as:
⦿ Actual vs expected column value range
⦿ Master data validation compliance
⦿ Invalid characters
⦿ Invalid emails, phone numbers, or ZIP codes
⦿ Uniqueness criteria compliance
⦿ User-defined custom rules
⦿ Data field length
⦿ Null or blank values
Organizations need a defined set of metrics aligned with business goals to assess and monitor data health. By automating compliance checks and validations, AI-powered data observability tools can reduce the risk of non-compliance.
Data Reconciliation
As data flows through multiple pipelines, it can break for countless reasons. With limited teams and numerous priorities, engineers can’t reconcile all data daily. Often, users report issues before engineers are even aware, which erodes trust.
Data observability platforms should offer automated data reconciliation to validate cell-level accuracy and consistency.
Reconcile Data Between Source and Target
Solutions must connect to diverse sources and reconcile source-to-target data automatically. AI-powered engines should detect mismatches and alert stakeholders via email, SMS, or Slack.
Reconciliation Within Analytics Platforms
In cases where source access isn’t possible, solutions should compare new data against historical trends to detect anomalies and reconciliation failures.
Implementing a Data Observability Framework
To bring it all together: data observability tools combine technologies and practices to give visibility into data health. As an extension of the DataOps movement, observability enables agile, iterative improvements to data systems.
But tools alone don’t solve everything. You might have the best dashboards and automation, but without company-wide adoption, observability remains siloed. Likewise, a team aligned with DataOps goals won’t succeed without supportive tech.
Key Components of a Data Observability Solution
To deliver value, any data observability platform must include:
⦿ Rule catalog customization — Predefined, customizable rules for various use cases
⦿ Anomaly detection — Automatic detection of data patterns that don’t fit context
⦿ Real-time alerting — Immediate alerts for fast corrective action
⦿ AI trend analysis — Use AI/ML to compare new data with historical trends
⦿ Issue tracking — Log, assign, and track issues through to resolution
⦿ Dashboards and metrics — Actionable reporting on identified problems
⦿ Central repository — Unified view across teams and systems to break silos
There may be standardized logging policies for one team, but not for another, and there’s no way for other teams to easily access them. Some teams may run algorithms on datasets to ensure they are meeting business rules. But the team that builds the pipelines doesn’t have a way to monitor how the data is transforming within that pipeline and whether it will be delivered in a form the consumers expect. The list can go on and on.
Without the ability to standardize and centralize these activities, teams can’t have the level of awareness they need to proactively iterate their data platform. A downstream data team can’t trace the source of their issues upstream, and an upstream data team can’t improve their processes without visibility into downstream dependencies.
What Does the Future of Data Observability Look Like?
As data volumes continue to grow and organizations’ dependency on data grows, data observability tools will become even more essential for organizations of every size. More and more businesses are realizing the benefits of data-driven decision-making, but they won’t be able to use that data effectively unless data conforms to data quality standards. Increasingly, organizations will see that manually reconciling, monitoring, and managing data across multiple data sources manually requires large resources and time and therefore is not feasible.
Data observability functions and tools such as 4DAlert will take over as the predominant method to automate monitoring pipelines, reconcile huge volumes of data, reduce siloed data monitoring, and improve collaboration across the organization.
Observability tools will continue to improve by supporting more data sources, automating more capabilities like governance and data standardization, and delivering rapid insights in real-time. These enhancements will help organizations support growth and leverage revenue-generating opportunities with fewer manual processes.
Transform Your Organization’s Monitoring Capabilities with 4DAlert’s AI/ML-enabled Data Observability Solution
Organizations handle and integrate multiple systems, pull data from varieties of sources, and load a huge volume of valuable data every day. But without the right tools, managing, monitoring, and finding data quality issues manually can take a huge amount of time and resources. Growing data volumes make it more important than ever for companies to find a solution that streamlines and automates end-to-end data management for analytics, compliance, and monitoring needs.
4DAlert Data House Platform
4DAlert’s Data House platform provides Data Observability, Data Governance, Data Catalog, Data Modelling, and CI/CD modules that support you manage your data platform. Specifically, the Data Observability module within the data house enables you to:
⦿ Monitor pipelines built across various integration points
⦿ Get alerted in real time when there is abnormality in pipeline execution
⦿ Automate data reconciliation needs
⦿ Detect data quality issues
⦿ Deliver a set of predefined dashboards that helps manage your data platforms