Data observability breakdown

Data observability capabilities and principles can be a powerful accelerator to business success, offering significant benefits, including:

Quality and explainability: Ensure that high-quality data is maintained across the organization, enabling clear understanding and use of data for decision-making
Compliance and trust: Ensure compliance with relevant laws and regulations, avoiding legal consequences and reputational damage
ROI tracking: Fully understand the ROI of data quality investments while identifying and mitigating risks associated with poor data quality

Data observability defined

Data observability is software and processes for profiling, monitoring, and notifying stakeholders about data quality in sources, pipelines, AI, and analytics. It automatically collects metadata to determine data structure and content, suggest data quality rules, build historical baselines, detect anomalies, determine root cause and impact, and respond to issues to ensure reliable data.

¹Source: https://www.idc.com/getdoc.jsp?containerId=US51397423

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Ready for data observability?

There’s no better time than right now. Start a data observability initiative by asking questions about these key topics:

Understand data quality events: Are data quality issues recognized and how often do they occur?
Identify success metrics: What key metrics (accuracy, completeness, freshness, etc.) reflect data quality in your organization?
Ensure stakeholder visibility: Who needs access to data quality insights (data engineers, data stewards, business analysts, etc.)?
Evaluate technical expertise: Do team members involved in data validation have the necessary technical backgrounds?
Assess automation level: How manual are your data quality processes? Can automation improve efficiency and accuracy?

These questions will help you get ready for implementing a robust data observability program, guiding you to evaluate your current readiness and pinpoint areas for improvement.