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Data Blending
Data blending is a process whereby big data from multiple sources are merged into a single data warehouse or data set. Data blending allows business analysts to cope with the expansion of data that they need to make critical business decisions based on good quality business intelligence. Data blending has been described as different from data integration due to the requirements of data analysts to merge sources very quickly, too quickly for any practical intervention by data scientists. A study done by Forrester Consulting in 2015 found that 52 percent of companies are blending 50 or more data sources and 12 percent are blending over 1,000 sources. Extract, transform, load Data blending is similar to extract, transform, load (ETL). Both ETL and data blending take data from various sources and combine them. However, ETL is used to merge and structure data into a target database, often a data warehouse. Data blending differs slightly as it's about joining data for a specific use case ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Big Data
Big data primarily refers to data sets that are too large or complex to be dealt with by traditional data processing, data-processing application software, 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. Big data analysis challenges include Automatic identification and data capture, capturing data, Computer data storage, data storage, data analysis, search, Data sharing, sharing, Data transmission, transfer, Data visualization, visualization, Query language, querying, updating, information privacy, and data source. Big data was originally associated with three key concepts: ''volume'', ''variety'', and ''velocity''. The analysis of big data presents challenges in sampling, and thus previously allowing for only observations and sampling. Thus a fourth concept, ''veracity,'' refers to the quality or insightfulness of the data. Without sufficient investm ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Data Curation
Data ( , ) are a collection of discrete or continuous values that convey information, describing the quantity, quality, fact, statistics, other basic units of meaning, or simply sequences of symbols that may be further interpreted formally. A datum is an individual value in a collection of data. Data are usually organized into structures such as tables that provide additional context and meaning, and may themselves be used as data in larger structures. Data may be used as variables in a computational process. Data may represent abstract ideas or concrete measurements. Data are commonly used in scientific research, economics, and virtually every other form of human organizational activity. Examples of data sets include price indices (such as the consumer price index), unemployment rates, literacy rates, and census data. In this context, data represent the raw facts and figures from which useful information can be extracted. Data are collected using techniques suc ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Data Scraping
Data scraping is a technique where a computer program extracts data from Human-readable medium, human-readable output coming from another program. Description Normally, Data transmission, data transfer between programs is accomplished using data structures suited for Automation, automated processing by computers, not people. Such interchange File format, formats and Protocol (computing), protocols are typically rigidly structured, well-documented, easily parsing, parsed, and minimize ambiguity. Very often, these transmissions are not human-readable at all. Thus, the key element that distinguishes data scraping from regular parsing is that the data being consumed is intended for display to an End-user (computer science), end-user, rather than as an input to another program. It is therefore usually neither documented nor structured for convenient parsing. Data scraping often involves ignoring binary data (usually images or multimedia data), Display device, display formatting, red ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Data Editing
Data editing is defined as the process involving the review and adjustment of collected survey data. 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. The purpose is to control the quality of the collected data. Data editing can be performed manually, with the assistance of a computer or a combination of both. Editing methods Editing methods refer to a range of procedures and processes which are used for detecting and handling errors in data. Data editing is used with the goal to improve the quality of statistical data produced. These modifications can greatly improve the quality of analytics created by aiming to detect and correct errors. Examples of different techniques to data editing such as micro-editing, macro-editing, selective editing, or the different tools used to achieve data editing such as graphical editing a ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Data Cleansing
Data cleansing or data cleaning is the process of identifying and correcting (or removing) corrupt, inaccurate, or irrelevant records from a dataset, table, or database. It involves detecting incomplete, incorrect, or inaccurate parts of the data and then replacing, modifying, or deleting the affected data. Data cleansing can be performed interactively using data wrangling tools, or through batch processing often via scripts or a data quality firewall. After cleansing, a data set should be consistent with other similar data sets in the system. The inconsistencies detected or removed may have been originally caused by user entry errors, by corruption in transmission or storage, or by different data dictionary definitions of similar entities in different stores. Data cleaning differs from data validation in that validation almost invariably means data is rejected from the system at entry and is performed at the time of entry, rather than on batches of data. The actual process ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Data Wrangling
Data wrangling, sometimes referred to as data munging, is the process of transforming and mapping data from one " raw" data form into another format with the intent of making it more appropriate and valuable for a variety of downstream purposes such as analytics. The goal of data wrangling is to assure quality and useful data. Data analysts typically spend the majority of their time in the process of data wrangling compared to the actual analysis of the data. The process of data wrangling may include further munging, data visualization, data aggregation, training a statistical model, as well as many other potential uses. Data wrangling typically follows a set of general steps which begin with extracting the data in a raw form from the data source, "munging" the raw data (e.g. sorting) or parsing the data into predefined data structures, and finally depositing the resulting content into a data sink for storage and future use. It is closely aligned with the ETL process. Backg ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Data Fusion
Data fusion is the process of integrating multiple data sources to produce more consistent, accurate, and useful information than that provided by any individual data source. Data fusion processes are often categorized as low, intermediate, or high, depending on the processing stage at which fusion takes place. Low-level data fusion combines several sources of raw data to produce new raw data. The expectation is that fused data is more informative and synthetic than the original inputs. For example, sensor fusion is also known as (multi-sensor) data fusion and is a subset of information fusion. The concept of data fusion has origins in the evolved capacity of humans and animals to incorporate information from multiple senses to improve their ability to survive. For example, a combination of sight, touch, smell, and taste may indicate whether a substance is edible. The JDL/DFIG model In the mid-1980s, the Joint Directors of Laboratories formed the Data Fusion Subpanel (whic ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Data Preparation
Data preparation is the act of manipulating (or pre-processing) raw data (which may come from disparate data sources) into a form that can be readily and accurately analysed, e.g. for business purposes. Data preparation is the first step in data analytics projects and can include many discrete tasks such as loading data or data ingestion, data fusion, data cleaning, data augmentation, and data delivery. The issues to be dealt with fall into two main categories: * systematic errors involving large numbers of data records, probably because they have come from different sources; * individual errors affecting small numbers of data records, probably due to errors in the original data entry. Data specification The first step is to set out a full and detailed specification of the format of each data field and what the entries mean. This should take careful account of: * most importantly, consultation with the users of the data * any available specification of the system which wil ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Join (SQL)
A join clause in the Structured Query Language (SQL) combines column (database), columns from one or more table (database), tables into a new table. The operation corresponds to a Join (relational algebra), join operation in relational algebra. Informally, a join stitches two tables and puts on the same row records with matching fields : INNER, LEFT OUTER, RIGHT OUTER, FULL OUTER and CROSS. Example tables To explain join types, the rest of this article uses the following tables: Department.DepartmentID is the primary key of the Department table, whereas Employee.DepartmentID is a foreign key. Note that in Employee, "Williams" has not yet been assigned to a department. Also, no employees have been assigned to the "Marketing" department. These are the SQL statements to create the above tables: CREATE TABLE department( DepartmentID INT PRIMARY KEY NOT NULL, DepartmentName VARCHAR(20) ); CREATE TABLE employee ( LastName VARCHAR(20), DepartmentID INT REFERENCE ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Data Visualization
Data and information visualization (data viz/vis or info viz/vis) is the practice of designing and creating Graphics, graphic or visual Representation (arts), representations of a large amount of complex quantitative and qualitative data and information with the help of static, dynamic or interactive visual items. Typically based on data and information collected from a certain domain of expertise, these visualizations are intended for a broader audience to help them visually explore and discover, quickly understand, interpret and gain important insights into otherwise difficult-to-identify structures, relationships, correlations, local and global patterns, trends, variations, constancy, clusters, outliers and unusual groupings within data (''exploratory visualization''). When intended for the general public (mass communication) to convey a concise version of known, specific information in a clear and engaging manner (''presentational'' or ''explanatory visualization''), it is t ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
