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| 001 | 00006725 | ||
| 003 | ES-MaONT | ||
| 005 | 20241218012046.0 | ||
| 008 | 210927s2021 xxkar||| ||||i00| 0 eng d | ||
| 020 | _a9789264805118 (HTML) | ||
| 020 | _a9789264503939 (PDF) | ||
| 020 | _a9789264713093 (EPUB) | ||
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_a10.1787/8f99ec8c-en _2doi |
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| 040 | _aES-MaONT | ||
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_aOrganización de Cooperación y Desarrollo Económico _92843 |
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| 245 | 1 | 0 |
_aData-driven, information-enabled regulatory delivery _c/ OECD |
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_aParis : _bOECD Publishing, _c25 September 2021 |
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_a37 p. : _bgráf., tablas ; _c1 documento PDF |
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_atexto _2isbdcontent |
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_ainformático _2isbdmedia |
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_arecurso en línea _2rdacarrier |
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| 520 | _aThis report draws upon the results of three projects in different regions in Italy, covering distinct regulatory areas, to assess the increasingly important role played by data analytics in applying and enforcing rules. The importance of risk-based approaches to regulatory inspections and enforcement is well known. However, regulators seeking to incorporate risk-based approaches still encounter roadblocks in terms of insufficient data generally and inadequate data management tools specifically. While using some risk analysis is more efficient than no risk analysis at all, data-related roadblocks have made it more difficult to identify risk factors. This problem has become more pronounced during the COVID-19 pandemic, where regulatory inspections and enforcement activities and related interventions had to be prioritised in order to balance safety concerns. However, recent developments have shown that data management can help improve inspection systems quite quickly, in an easier and cheaper way than in the past, because costs for equipment are lower, less specialist staff time is required, and computing power has increased. Developments in machine learning have also made the analysis of large volumes of data faster. | ||
| 650 | 0 |
_aEmpresas _92189 |
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| 650 | 7 |
_92345 _aIndustrias |
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| 651 | 0 |
_aItalia _92751 |
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| 653 | _aherramientas basadas en datos | ||
| 653 | _aherramientas digitales | ||
| 653 | _amodelos predictivos | ||
| 653 | _ariesgos | ||
| 653 | _asistemas regulatorios | ||
| 856 | 4 |
_uhttps://www.oecd-ilibrary.org/sites/8f99ec8c-en/index.html?itemId=/content/publication/8f99ec8c-en _x0 _yAcceso al documento |
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| 856 | 4 |
_uhttps://www.oecd-ilibrary.org/governance/data-driven-information-enabled-regulatory-delivery_8f99ec8c-en _x0 _yMás información |
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_2z _cELIB |
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