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| dc.title | Infrastructure, Public Policy and the Challenge of Big Data |
| dc.contributor.author | Sosa Escudero, Walter |
| dc.contributor.orgunit | Infrastructure and Energy Sector |
| dc.date.available | 2020-01-14T00:00:00 |
| dc.date.issue | 2020-01-14T00:00:00 |
| dc.description.abstract | This article discusses the possibilities of using big data and machine learning strategies to improve the provision of public services along many dimensions, including better access or quality and cost reduction. Several examples are presented for the case of water, electricity and transportation. The note also highlights the challenges faced by the infrastructure sector and, in general, by the public sector in relation to the possibilities and limitations of big data. |
| dc.format.extent | 16 |
| dc.identifier.doi | http://dx.doi.org/10.18235/0002139 |
| dc.identifier.url | https://publications.iadb.org/publications/english/document/Infrastructure_Public_Policy_and_the_Challenge_of_Big_Data_en.pdf |
| dc.language.iso | en |
| dc.medium | Adobe PDF |
| dc.publisher | Inter-American Development Bank |
| dc.subject | Public Policy |
| dc.subject | Infrastructure Development |
| dc.subject | Big Data |
| dc.subject | Public Utility |
| dc.subject.jelcode | L95 - Gas Utilities • Pipelines • Water Utilities |
| dc.subject.jelcode | L91 - Transportation: General |
| dc.subject.jelcode | L94 - Electric Utilities |
| dc.subject.jelcode | C80 - Data Collection and Data Estimation Methodology • Computer Programs: General |
| dc.subject.keywords | Infrastructure; Public Policy; Big Data; Machine Learning |
| dc.type | Technical Notes |
| idb.identifier.pubnumber | IDB-TN-01847 |
| idb.operation | RG-T3175 |