Remove Data Warehouse Remove Publishing Remove Statistics Remove Unstructured Data
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Edmunds sets stage for AI with data infrastructure consolidation

CIO Business Intelligence

His role now encompasses responsibility for data engineering, analytics development, and the vehicle inventory and statistics & pricing teams. The company was born as a series of print buying guides in 1966 and began making its data available via CD-ROM in the 1990s. The shift to online started not long after.

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Business Intelligence vs Data Science vs Data Analytics

FineReport

Business Intelligence describes the process of using modern data warehouse technology, data analysis and processing technology, data mining, and data display technology for visualizing, analyzing data, and delivering insightful information. Typical tools for data science: SAS, Python, R.

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How a Discovery Data Warehouse, the next evolution of augmented analytics, accelerates treatments and delivers medicines safely to patients in need

Cloudera

Sample and treatment history data is mostly structured, using analytics engines that use well-known, standard SQL. Interview notes, patient information, and treatment history is a mixed set of semi-structured and unstructured data, often only accessed using proprietary, or less known, techniques and languages.

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Data Visualization and Visual Analytics: Seeing the World of Data

Sisense

Data is usually visualized in a pictorial or graphical form such as charts, graphs, lists, maps, and comprehensive dashboards that combine these multiple formats. Data visualization is used to make the consuming, interpreting, and understanding data as simple as possible, and to make it easier to derive insights from data.

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What is a Data Pipeline?

Jet Global

The key components of a data pipeline are typically: Data Sources : The origin of the data, such as a relational database , data warehouse, data lake , file, API, or other data store. This can include tasks such as data ingestion, cleansing, filtering, aggregation, or standardization.