IT Glossary



data quality tools

…s are continuing to emerge and grow in popularity. The tools are increasingly implemented in support of general data quality improvement initiatives, as well as within critical applications, such as ERP, CRM and BI. As data quality becomes increasingly pervasive, many data integration tools now include data quality management functionality. Also see: Gartner’s Data & Analytics Summit – The explosion of data sources, big data analytics u…

Search-Based Data Discovery Tools

…g users to explore data without much training. However, as well as having a broader scope (visualization-driven data discovery tools focus exclusively on quantitative data) they differ at the user interface layer, with search-based data discovery tools using text search input and results to guide users to the information they need. Also see: Gartner’s Data & Analytics Summit – The explosion of data sources, big data analytics use cases, techn…

Operational Data Store

…d propagate those updates back to the source operational system from which the data elements were obtained. The data warehouse, on the other hand, provides an architecture for decision makers to access data to perform strategic analysis, which often involves historical and cross-functional data and the need to support many applications. Also see: Gartner’s Data & Analytics Summit – The explosion of data sources, big data analytics use cases,…

Big Data

Big data is high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation. (Related: Master Data Management  – MDM) Also see: Gartner’s Data & Analytics Summit Gartner’s free research and webinars on Data & Analytics.      …

Data Broker

…y is not “sold” (i.e., its ownership transferred), but rather it is licensed for particular or limited uses. (A data broker is also sometimes known as an information broker, syndicated data broker, or information product company.) Also see: Gartner’s Data & Analytics Summit – This inaugural summit will offer valuable insight and help redefine the role of data and analytics in digital business. Free research and webinars from Gartner on Big Da…

Data Center

…include aging data center infrastructures that are at risk for not meeting future business requirements, an ongoing cost-consciousness, and the need to be more energy-efficient. Many enterprises are looking to virtualization, fabric-based infrastructure, modular designs and cloud computing as they explore how best to optimize their resources. Also see: Free research and webinars from Gartner on Big Data Analytics. MDM (master data management)…

Dark Data

…formation assets. Thus, organizations often retain dark data for compliance purposes only. Storing and securing data typically incurs more expense (and sometimes greater risk) than value. Also see: Gartner’s Data & Analytics Summit – This inaugural summit will offer valuable insight and help redefine the role of data and analytics in digital business. Free research and webinars from Gartner on Big Data Analytics. Master Data Management (MDM)…

Analytics

…m business and IT professionals looking to exploit huge mounds of internally generated and externally available data. Also see: Gartner’s Data & Analytics Summit – The explosion of data sources, big data analytics use cases, technology and analytics roles is yielding a wealth of new business opportunities. Learn more about how to leverage these opportunities at Gartner’s Data & Analytics Summit Free research and webinars from Gartner on B…

Advanced Analytics

…per insights, make predictions, or generate recommendations. Advanced analytic techniques include those such as data/text mining, machine learning, pattern matching, forecasting, visualization, semantic analysis, sentiment analysis, network and cluster analysis, multivariate statistics, graph analysis, simulation, complex event processing, neural networks. Also see: Gartner’s Data & Analytics Summit – The explosion of data sources, big data a…

Data Scientist

The data scientist role is critical for organizations looking to extract insight from information assets for “big data” initiatives and requires a broad combination of skills that may be fulfilled better as a team, for example: Collaboration and team work is required for working with business stakeholders to understand business issues. Analytical and decision modeling skills are required for discovering relationships within data and…

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