Data Quality Barriers for Transparency in Public Procurement

Ahmet Soylu; Óscar Corcho; Brian Elvesæter; Carlos Badenes-Olmedo; Francisco Yedro-Martínez; Matej Kovacic; Matej Posinkovic; Mitja Medvešček; Ian Makgill; Chris Taggart; Elena Simperl; Till C. Lech; Dumitru Roman

doi:10.3390/info13020099

Data Quality Barriers for Transparency in Public Procurement

Ahmet Soylu^*, Óscar Corcho, Brian Elvesæter, Carlos Badenes-Olmedo, Francisco Yedro-Martínez, Matej Kovacic, Matej Posinkovic, Mitja Medvešček, Ian Makgill, Chris Taggart, Elena Simperl, Till C. Lech, Dumitru Roman

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

15 Citations (Scopus)

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Abstract

Governments need to be accountable and transparent for their public spending decisions in order to prevent losses through fraud and corruption as well as to build healthy and sustainable economies. Open data act as a major instrument in this respect by enabling public administrations, service providers, data journalists, transparency activists, and regular citizens to identify fraud or uncompetitive markets through connecting related, heterogeneous, and originally unconnected data sources. To this end, in this article, we present our experience in the case of Slovenia, where we successfully applied a number of anomaly detection techniques over a set of open disparate data sets integrated into a Knowledge Graph, including procurement, company, and spending data, through a linked data-based platform called TheyBuyForYou. We then report a set of guidelines for publishing high quality procurement data for better procurement analytics, since our experience has shown us that there are significant shortcomings in the quality of data being published. This article contributes to enhanced policy making by guiding public administrations at local, regional, and national levels on how to improve the way they publish and use procurement-related data; developing technologies and solutions that buyers in the public and private sectors can use and adapt to become more transparent, make markets more competitive, and reduce waste and fraud; and providing a Knowledge Graph, which is a data resource that is designed to facilitate integration across multiple data silos by showing how it adds context and domain knowledge to machine-learning-based procurement analytics.

Original language	English
Article number	99
Journal	Information (Switzerland)
Volume	13
Issue number	2
DOIs	https://doi.org/10.3390/info13020099
Publication status	Published - 20 Feb 2022

Keywords

Anomaly detection
Data integration
Fraud and corruption
Knowledge graph
Linked open data
Public procurement

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10.3390/info13020099Licence: CC BY

Data Quality Barriers for_SOYLU_Publishedonline20Feb2022_GOLD VoR (CC BY)Final published version, 2.04 MBLicence: CC BY

Cite this

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title = "Data Quality Barriers for Transparency in Public Procurement",

abstract = "Governments need to be accountable and transparent for their public spending decisions in order to prevent losses through fraud and corruption as well as to build healthy and sustainable economies. Open data act as a major instrument in this respect by enabling public administrations, service providers, data journalists, transparency activists, and regular citizens to identify fraud or uncompetitive markets through connecting related, heterogeneous, and originally unconnected data sources. To this end, in this article, we present our experience in the case of Slovenia, where we successfully applied a number of anomaly detection techniques over a set of open disparate data sets integrated into a Knowledge Graph, including procurement, company, and spending data, through a linked data-based platform called TheyBuyForYou. We then report a set of guidelines for publishing high quality procurement data for better procurement analytics, since our experience has shown us that there are significant shortcomings in the quality of data being published. This article contributes to enhanced policy making by guiding public administrations at local, regional, and national levels on how to improve the way they publish and use procurement-related data; developing technologies and solutions that buyers in the public and private sectors can use and adapt to become more transparent, make markets more competitive, and reduce waste and fraud; and providing a Knowledge Graph, which is a data resource that is designed to facilitate integration across multiple data silos by showing how it adds context and domain knowledge to machine-learning-based procurement analytics.",

keywords = "Anomaly detection, Data integration, Fraud and corruption, Knowledge graph, Linked open data, Public procurement",

author = "Ahmet Soylu and {\'O}scar Corcho and Brian Elves{\ae}ter and Carlos Badenes-Olmedo and Francisco Yedro-Mart{\'i}nez and Matej Kovacic and Matej Posinkovic and Mitja Medve{\v s}{\v c}ek and Ian Makgill and Chris Taggart and Elena Simperl and Lech, {Till C.} and Dumitru Roman",

note = "Funding Information: The Open Contracting Partnership (https://www.open-contracting.org (accessed on 19 February 2022)) is an international initiative originally emerged from the collaboration of the World Bank Institute (https://www.worldbank.org (accessed on 19 February 2022)) and the German Agency for International Cooperation and Development, GIZ (https: //www.giz.de (accessed on 19 February 2022)). It promotes increased disclosure and widespread participation in public contracting, covering the entire contracting chain from planning to finalisation of contract obligations, including tendering and performance. Open Contracting Partnership promotes high-level policies for an increased, standardised disclosure of contracting data and argues for a smarter, more strategic use of such data. The Open Contracting Data Standard (OCDS) (https://standard.open-contracting.org (accessed on 19 February 2022)) is a core product of the Open Contracting Partnership. Version 1.0 was developed for the Open Contracting Partnership by the World Wide Web Foundation (https://webfoundation.org (accessed on 19 February 2022)), through a project supported by the Omidyar Network (https://omidyar.com (accessed on 19 February 2022)) and the World Bank Institute. Ongoing development is managed by the Open Data Services Co-operative (https://opendataservices.coop (accessed on 19 February 2022)) under a contract to the Open Contracting Partnership. Funding Information: Funding: This research was funded by European Commission Horizon 2020, grant number 780247. Publisher Copyright: {\textcopyright} 2022 by the authors. Licensee MDPI, Basel, Switzerland.",

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doi = "10.3390/info13020099",

language = "English",

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AU - Soylu, Ahmet

AU - Corcho, Óscar

AU - Elvesæter, Brian

AU - Badenes-Olmedo, Carlos

AU - Yedro-Martínez, Francisco

AU - Kovacic, Matej

AU - Posinkovic, Matej

AU - Medvešček, Mitja

AU - Makgill, Ian

AU - Taggart, Chris

AU - Simperl, Elena

AU - Lech, Till C.

AU - Roman, Dumitru

N1 - Funding Information: The Open Contracting Partnership (https://www.open-contracting.org (accessed on 19 February 2022)) is an international initiative originally emerged from the collaboration of the World Bank Institute (https://www.worldbank.org (accessed on 19 February 2022)) and the German Agency for International Cooperation and Development, GIZ (https: //www.giz.de (accessed on 19 February 2022)). It promotes increased disclosure and widespread participation in public contracting, covering the entire contracting chain from planning to finalisation of contract obligations, including tendering and performance. Open Contracting Partnership promotes high-level policies for an increased, standardised disclosure of contracting data and argues for a smarter, more strategic use of such data. The Open Contracting Data Standard (OCDS) (https://standard.open-contracting.org (accessed on 19 February 2022)) is a core product of the Open Contracting Partnership. Version 1.0 was developed for the Open Contracting Partnership by the World Wide Web Foundation (https://webfoundation.org (accessed on 19 February 2022)), through a project supported by the Omidyar Network (https://omidyar.com (accessed on 19 February 2022)) and the World Bank Institute. Ongoing development is managed by the Open Data Services Co-operative (https://opendataservices.coop (accessed on 19 February 2022)) under a contract to the Open Contracting Partnership. Funding Information: Funding: This research was funded by European Commission Horizon 2020, grant number 780247. Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

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N2 - Governments need to be accountable and transparent for their public spending decisions in order to prevent losses through fraud and corruption as well as to build healthy and sustainable economies. Open data act as a major instrument in this respect by enabling public administrations, service providers, data journalists, transparency activists, and regular citizens to identify fraud or uncompetitive markets through connecting related, heterogeneous, and originally unconnected data sources. To this end, in this article, we present our experience in the case of Slovenia, where we successfully applied a number of anomaly detection techniques over a set of open disparate data sets integrated into a Knowledge Graph, including procurement, company, and spending data, through a linked data-based platform called TheyBuyForYou. We then report a set of guidelines for publishing high quality procurement data for better procurement analytics, since our experience has shown us that there are significant shortcomings in the quality of data being published. This article contributes to enhanced policy making by guiding public administrations at local, regional, and national levels on how to improve the way they publish and use procurement-related data; developing technologies and solutions that buyers in the public and private sectors can use and adapt to become more transparent, make markets more competitive, and reduce waste and fraud; and providing a Knowledge Graph, which is a data resource that is designed to facilitate integration across multiple data silos by showing how it adds context and domain knowledge to machine-learning-based procurement analytics.

AB - Governments need to be accountable and transparent for their public spending decisions in order to prevent losses through fraud and corruption as well as to build healthy and sustainable economies. Open data act as a major instrument in this respect by enabling public administrations, service providers, data journalists, transparency activists, and regular citizens to identify fraud or uncompetitive markets through connecting related, heterogeneous, and originally unconnected data sources. To this end, in this article, we present our experience in the case of Slovenia, where we successfully applied a number of anomaly detection techniques over a set of open disparate data sets integrated into a Knowledge Graph, including procurement, company, and spending data, through a linked data-based platform called TheyBuyForYou. We then report a set of guidelines for publishing high quality procurement data for better procurement analytics, since our experience has shown us that there are significant shortcomings in the quality of data being published. This article contributes to enhanced policy making by guiding public administrations at local, regional, and national levels on how to improve the way they publish and use procurement-related data; developing technologies and solutions that buyers in the public and private sectors can use and adapt to become more transparent, make markets more competitive, and reduce waste and fraud; and providing a Knowledge Graph, which is a data resource that is designed to facilitate integration across multiple data silos by showing how it adds context and domain knowledge to machine-learning-based procurement analytics.

KW - Anomaly detection

KW - Data integration

KW - Fraud and corruption

KW - Knowledge graph

KW - Linked open data

KW - Public procurement

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DO - 10.3390/info13020099

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JO - Information (Switzerland)

JF - Information (Switzerland)

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ER -

Data Quality Barriers for Transparency in Public Procurement

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