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From a business perspective, data quality is: data that are "'fit for use' in their intended operational, decision-making and other roles" or that exhibits "'conformance to standards' that have been set, so that fitness for use is achieved" [6] data that "are fit for their intended uses in operations, decision making and planning" [7]
Analytics is the "extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions." It is a subset of business intelligence, which is a set of technologies and processes that uses data to understand and analyze business performance to drive decision-making .
Data analysis focuses on the process of examining past data through business understanding, data understanding, data preparation, modeling and evaluation, and deployment. [8] It is a subset of data analytics, which takes multiple data analysis processes to focus on why an event happened and what may happen in the future based on the previous data.
Business intelligence (BI) consists of strategies, methodologies, and technologies used by enterprises for data analysis and management of business information. [1] Common functions of BI technologies include reporting, online analytical processing, analytics, dashboard development, data mining, process mining, complex event processing, business performance management, benchmarking, text ...
Data collection or data gathering is the process of gathering and measuring information on targeted variables in an established system, which then enables one to answer relevant questions and evaluate outcomes. Data collection is a research component in all study fields, including physical and social sciences, humanities, [2] and business ...
This is a list of important publications in data science, generally organized by order of use in a data analysis workflow. Whole game of data science See the list of important publications in statistics for more research-based and fundamental publications; while this list is more applied, business oriented, and cross-disciplinary.
An important field in computer science, technology, and library science is the longevity of data. Scientific research generates huge amounts of data, especially in genomics and astronomy, but also in the medical sciences, e.g. in medical imaging.
Data editing is defined as the process involving the review and adjustment of collected survey data. [1] Data editing helps define guidelines that will reduce potential bias and ensure consistent estimates leading to a clear analysis of the data set by correct inconsistent data using the methods later in this article. [ 2 ]
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