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PublicBI EUPP Analytics 


It is known that the EU spends approximately 2 trillion euros annually on public procurement.  But is there a way to analyze this spend to identify the top buyers, top contractors, sectors with the highest expenditures, countries that spend the most, countries that secure the most contracts, and so on?   

The PublicBI EUPP analytics solution attempts to answers all of the above questions and more. 

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ublicBI EUPP analytics solution provides information and insights in the form of easy-to-use interactive dashboards. It is based on daily cleaned & enriched EU public procurement data (TED data). In addition to answering the above questions, it can help you discover interesting trends, patterns, correlations, and anomalies in EU public procurement.  

Contact us to start your subscription or for a demo. 

Highlights of EUPP 

01

Summary and granular level analysis

02

Raw, cleaned and enriched data analysis

03

Multiple filters to slice and dice data

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Refreshed daily

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6+ years of data

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Evolved data cleaning process applied

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Simplified pricing 

Disclaimer:

PublicBI does not guarantee the accuracy, completeness, or currency of the data included in this work (PublicBI EUPP Analytics) and does not assume responsibility for any errors, omissions, discrepancies, or liability related to the use or failure to use the information. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgment on the part of PublicBI regarding the legal status of any territory or the endorsement or acceptance of such boundaries.

PublicBI does not claim that all data quality issues present in the source data (TED data) have been detected, nor that all detected issues have been fixed. Even those data quality issues that have been addressed from PublicBI's perspective are not claimed to be entirely complete and accurate. For example, in the case of missing or invalid financial values in the contracts awarded data in the source files, several rules have been applied to derive the best possible alternative value. However, validating the correctness of the derived value against the actual value (which is not available) has not been feasible.

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