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|The correctness of data, ensuring it reflects the real-world entities it represents.
|The genuineness and trustworthiness of data, indicating that it is original, unaltered, and from a reliable source.
|Identifying, correcting, or removing errors, inconsistencies, and inaccuracies within a dataset to improve data quality.
|The extent to which data contains all necessary information for a particular record.
|The uniformity of data across various systems and sources.
|Customer Data Integration (CDI)
|The process of integrating and managing customer data from multiple sources to create a unified and accurate view.
|Customer Identity Resolution
|The process of identifying and linking multiple records related to a single customer across different data sources.
|Unusual or unexpected patterns, behaviours, or values within a dataset that deviate from the norm.
|The process of correcting or removing inaccuracies and inconsistencies in data.
|Identifying and eliminating duplicate records within a dataset.
|Enhancing existing data with additional information from external sources.
|The framework and processes for managing data quality, security, and compliance.
|Data Governance Framework
|A structured approach to managing, organising, and controlling data to ensure quality, security, and compliance.
|Combining data from different sources to create a unified and comprehensive dataset.
|Data Integration Tools
|Software or tools designed to combine and unify data from different sources, facilitating seamless data flow and analysis.
|The process of obscuring or anonymizing sensitive information within a dataset to protect privacy.
|Data Matching Software
|Software designed to identify and match similar or identical records within a dataset, supporting tasks like deduplication.
|The assignment of responsibility for the accuracy, security, and overall quality of specific datasets.
|The protection of sensitive and personal information to ensure compliance with privacy regulations.
|The process of analysing data to understand its structure, quality, and relationships.
|Data Profiling Software
|Software that automates the process of analysing and understanding the characteristics, quality, and relationships within a dataset.
|Data Profiling Tools
|Software or tools designed to analyse and understand the characteristics, quality, and structure of data within a dataset.
|Data Quality Dashboard
|A visual representation or interface displaying key data quality metrics and insights for monitoring data health.
|Data Quality Improvement
|The ongoing process of enhancing data quality through measures like cleansing, validation, and standardisation.
|Data Quality Metrics
|Quantifiable measures used to assess the quality of data, focusing on aspects like accuracy, completeness, and consistency.
|Data Quality Score
|A quantitative measure indicating the overall quality of a dataset, often calculated based on various data quality metrics.
|Data Quality Tools
|Software or applications specifically designed to assess, improve, and maintain the quality of data within an organisation.
|Another term for data cleansing, involving the identification and correction of errors and inconsistencies.
|The process of transforming data into a common format, ensuring consistency and uniformity.
|The responsible management and oversight of data to ensure its quality, security, and compliance.
|Checking data against predefined standards or rules to ensure compliance.
|Master Data Management (MDM)
|A method of managing and organising core business entities to ensure consistency and accuracy across an organisation.
|The process of creating and maintaining a master or authoritative version of data, ensuring a unified and consistent view.
|The process of comparing and identifying similarities or commonalities between two sets of data. Used in tasks like deduplication and record linkage.
|Combining two or more sets of data into a single dataset, typically from different sources, to create a comprehensive view.
|The trustworthiness of data, indicating its accuracy and consistency over time.
|Single Customer View (SCV)
|The comprehensive and unified representation of a customer’s data from various touchpoints and interactions.
|The degree to which data accurately represents the real-world concept it is intended to measure or describe. Valid data is relevant, meaningful, and correctly reflects the intended attributes or characteristics.
|The process of checking whether data is accurate and truthful by cross-referencing it with a reliable source or using a standardised method.
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