Title: Data Quality Author: Kriko Published: May 25, 2023 Last modified: Jul 7, 2026 --- 1. [Home](https://kriko.io/) / 2. [Glossary](https://kriko.io/glossary) / 3. D Letter # Data Quality **[Data](https://kriko.io/services/data-analysis-insight) quality** refers to the extent to which [data](https://kriko.io/services/data-analysis-insight) is suitable for a defined purpose. A dataset must generally be accurate, complete, current, consistent and usable to be considered high quality. For example, displaying correct values on a KPI dashboard designed to monitor company objectives supports reliable decision-making. However, figures should not only appear correct, as they must also come from dependable sources and be produced through appropriate calculation methods. [Data](https://kriko.io/services/data-analysis-insight) profiling, monitoring and analysis play important roles in maintaining [data](https://kriko.io/services/data-analysis-insight) quality. These activities can identify missing fields, duplicate records, invalid values and inconsistencies between systems. [Data](https://kriko.io/services/data-analysis-insight) cleansing, standardisation, matching, enrichment and validation can then be used to address the problems discovered. [Data](https://kriko.io/services/data-analysis-insight) quality should therefore be treated as an ongoing management process covering the entire [data](https://kriko.io/services/data-analysis-insight) lifecycle rather than as a one-time project. Responsibility for [data](https://kriko.io/services/data-analysis-insight) quality should not belong exclusively to technical teams or a single business department. Business units define how information will be used and which quality standards are required, while [data](https://kriko.io/services/data-analysis-insight) owners and governance teams may establish relevant rules. Information technology and [data](https://kriko.io/services/data-analysis-insight) teams implement, monitor and report on these requirements within organisational systems. This shared approach helps align data quality activities with business objectives. Data quality can be assessed through dimensions such as **accuracy, completeness, consistency, timeliness, validity, uniqueness and relevance**. Accuracy indicates whether information reflects reality, while completeness determines whether all required values are available. Timeliness concerns whether data is sufficiently current and available when required, and validity measures compliance with defined formats and business rules. Relevance indicates whether the information is suitable for the analytical purpose and user requirement. Uniqueness means that records representing the same person, transaction or object are not unnecessarily duplicated. Consistency requires the same information to have compatible values across different systems and departments. For example, different customer addresses in CRM and billing systems may indicate a consistency problem. Clear definitions and understandable field names also make it easier for users to interpret information correctly. Poor-quality data can lead to inaccurate reports, unsuitable decisions and unnecessary operational costs. Incorrect sales figures, misclassified website traffic or duplicate customer records may directly affect analytical results. Employees may also waste time manually checking information when they do not trust organisational reports. High-quality data can improve productivity, accelerate decision-making and strengthen confidence in information across the organisation. In summary, data quality covers not only technical accuracy but also reliability and suitability for the intended purpose. Clear rules, responsibilities, measurement criteria and regular control mechanisms are required to maintain quality. Organisations should identify the source of data problems and improve the processes that generate the information instead of correcting only the final output. Effective **data quality management** supports more reliable decisions across marketing, operations, finance and corporate strategy. ## Discover it in the dictionary ### Behavioural Targeting Behavioural targeting is a digital marketing method that uses signals from users' behaviour across websites, mobile applications, search engines, social media or other digital… ### Prototype A prototype is an early-stage model created to test a product, service, interface, process or idea. It can also be described as an initial… ### Key Performance Indicator A Key Performance Indicator, commonly abbreviated as KPI, is a measurable performance indicator that shows how effectively a business, department, team, project or individual… ### Artificial Intelligence Artificial intelligence, commonly abbreviated as AI, is a field of technology that aims to enable computer systems to perform tasks associated with human intelligence,… ### Information Processing Cycle The information processing cycle refers to the stages through which raw data is transformed into meaningful information. During this process, data is entered into… ### Seed Keyword A seed keyword is the basic word or phrase that forms the starting point of keyword research in SEO and content strategy. It describes… ### Technical SEO Technical SEO refers to the technical optimization work that helps a website be crawled, understood, indexed and experienced more effectively by search engines and… ### Time Series Analysis Time series analysis is an analytical method used to examine data recorded over time. A time series consists of values related to a specific… ### Long-tail Keyword A long-tail keyword is a keyword type that addresses more specific search intent. It usually consists of more words, has lower search volume and… ## Track the digital heartbeat with Kriko Subscribe to receive curated insights, news, and ideas shaping the digital landscape. By checking this box, you acknowledge and accept our [privacy policy](https://kriko.io/privacy-policy).