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Data quality refers to the state of qualitative or quantitative pieces of information. There are many definitions of data quality, but data is generally considered high quality if it is "fit for [its] intended uses in operations, decision making and planning ". [ 1][ 2][ 3] Moreover, data is deemed of high quality if it correctly represents the ...
Data analysis is the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. [ 1] Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science ...
Big data primarily refers to data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many entries (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate. [ 2]
ISO 8000 is the global standard for Data Quality and Enterprise Master Data. It describes the features and defines the requirements for standard exchange of Master Data among business partners. It establishes the concept of Portability as a requirement for Enterprise Master Data, and the concept that true Enterprise Master Data is unique to ...
Data profiling is the process of examining the data available from an existing information source (e.g. a database or a file) and collecting statistics or informative summaries about that data. [ 1] The purpose of these statistics may be to: Understanding data challenges early in any data intensive project, so that late project surprises are ...
Information quality. Information quality (IQ) is the quality of the content of information systems. It is often pragmatically defined as: "The fitness for use of the information provided". IQ frameworks also provides a tangible approach to assess and measure DQ/IQ in a robust and rigorous manner. [1]
The Data Quality Campaign was founded in 2005, by Aimee Guidera and 10 founding partners as a national collaborative effort to encourage states to improve the collection and use of high-quality education data, and implement state longitudinal data systems to measure student achievement. [1] [2] It focused on its 10 Essential Elements of ...
Data cleansing. Data cleansing or data cleaning is the process of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table, or database and refers to identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or deleting the dirty or coarse data. [ 1]