Introduction:
EU spends ~2 trillion euros per annum on public procurement. All EU procurement data above EU threshold and many contracts awarded below threshold is available in TED (Tenders electronic daily) data. However, the data quality issues hinder meaningful analysis and deriving of information and insights. 

PublicBI EUPP Analytics solution is aimed to provide information and insights currently in the form of easy to use interactive dashboards based on daily cleaned & enriched* EU public procurement data (TED data). Apart from answering basic questions such as total contract award value, number of contract awards, top buyers, top suppliers, etc., it can support you in finding many interesting trends, patterns, correlations, and anomalies in EU public procurement. For a sample of the findings using EUPP Analytics see the published data storiesContact us to start your subscription or for a demo. For those who already have access and have accepted the terms of use, please read the disclaimer and known limitations provided below before use. 

Highlights:

  • Possibility to analyze data at both summary and granular levels (each contract award).

  • Possibility to analyze raw (original), cleaned and enriched data.

  • Easy to use interactive dashboards format.

  • Based on TED EU procurement data.

  • Powerful filters to slice and dice data.

  • Data updated daily (business days). 

  • Almost 5 years of data (data from January 2018 until previous business day) available.

  • Data cleansing process evolved based on multiple years of work.

  • Regular updates and further improvements in data quality on ongoing basis.

  • The first version of the solution won prize at EU Datathon 2018, Brussels.

  • Simplified pricing. Monthly or yearly subscription plans.  

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Contracts Awarded

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Upcoming

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Upcoming

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, or discrepancies in the information, or liability with respect to the use of 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 concerning the legal status of any territory or the endorsement or acceptance of such boundaries. 

PublicBI does not claim that all of the data quality issues present in the source data (TED data) have been detected and does not claim that all of the detected data quality issues have been fixed. Even those data quality issues that have been fixed from PublicBI's perspective, is not claimed to be complete and accurate. For example, in the case of financial values missing or presence of invalid value in the contracts awarded data in the source files (TED XML files), several rules have been applied to derive the best possible alternate value, however, validating the correctness of the derived value against actual value (not available) has not been feasible.

Known Limitations:

  • Currently, the public procurement data used as source data in the EUPP analytics solution is based on TED data. Not all of the contract awards below EU threshold is available in this set of data.

  • Currently, TED XML files published from January 2018 are considered. Some of the files published in 2018 or later may contain contracts awarded in 2017 or earlier, as the data for 2017 and earlier is incomplete, it is best to simply disregard the information for years earlier than 2018. 

*Cleaned and enriched: The EU procurement data in its original form (raw data) has several data quality issues (more than 300K data quality issues in files of a single month files) to the extent that it cannot be used for any meaningful analysis. By cleaning and enriching the data, it has been brought up to a state where meaningful analysis is possible. However, it should be noted that, cleaning is an ongoing process and not all of the data quality issues has been fixed yet. In some cases, it is impossible to fix the data quality issue, for example, where the contract award value is missing in the raw data, and it is not possible to derive from any of the value derivation rules, then the contract award value is not enriched but cleaned (set to 0 from null).