Data Quality
Definition
The measure of data's fitness for its intended uses, encompassing accuracy, completeness, consistency, timeliness, and validity.
Overview
Data quality refers to how well data meets the requirements for its intended use. Key dimensions include accuracy (correctness), completeness (no missing values), consistency (same across systems), timeliness (current), and validity (conforms to rules). Poor data quality undermines automation and analytics initiatives. Data quality management involves profiling, cleansing, standardization, and ongoing monitoring.
Why It Matters
Bad data costs organizations an average of 15-25% of revenue through failed processes, incorrect decisions, and wasted effort. Every automation initiative is only as reliable as the data it processes—garbage in truly means garbage out at enterprise scale.
How New Odyssey Helps
New Odyssey includes built-in data quality monitoring with AI-driven validation rules that catch anomalies before they propagate, ensuring clean data flows through every automated workflow.
Related Solutions & Use Cases
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Migrate data between systems with AI-powered mapping and validation. Zero data loss, minimal downtime.
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Keep customer data consistent across all systems. Eliminate duplicate entries and ensure every team has accurate information.
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Create a complete, real-time view of every customer by unifying data from all touchpoints and systems.
Learn moreAutomated Reconciliation
Automate the matching and reconciliation of transactions across systems with AI-powered pattern recognition.
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