Bloomberg Launches Linked Open Data Website

Bloomberg Launches Linked Open Data Website

October 5, 2018

Bloomberg has launched a new website—Enterprise Access Point — to help firms derive value and enterprise-wide efficiencies. The website will enable enterprise clients, developers and data scientists to easily discover and act on ready-to-use datasets.

Bloomberg announced the launch of Enterprise Access Point, an online Open Data and Linked Data Platform that will provide Bloomberg’s Data License clients with normalized reference, pricing, regulatory and historical datasets. The new website will allow clients to browse quality data, examine metadata, trial sample datasets prior to acquisition, and immediately put them to use within their organization.

As users of financial data increase in sophistication, they expect a self-service model for discovering and browsing data catalogs and connecting their data tools directly to data publishers. Enterprise Access Point allows clients to connect their enterprise systems directly to Bloomberg’s publishing platform, through a RESTful API, and receive complete and comprehensive data. By delivering data in clean standardized formats through one source, integration costs for clients are reduced, if not eliminated.

For data scientists, the information provided via Enterprise Access Point is available as CSV data frames and supports multiple technologies including Jupyter and Python Pandas. For professionals leveraging artificial intelligence (AI), Linked Data is also available in a graph format. Web developers using Enterprise Access Point also benefit from Bloomberg’s RESTful Hypermedia API, which allows URL consistent data to feed directly into an enterprise’s software components, including machine-learning tools, with minimal friction or delay.

Click here to read the complete news release.

Brought to you by Scope e-Knowledge Center, an SPi Global Company, a trusted global partner for Digital Content Transformation Solutions, Knowledge Modeling (Taxonomies, Thesauri and Ontologies), Abstracting & Indexing (A&I), Metadata Enrichment and Entity Extraction.

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