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RUMS – how to compare structures of enterprise groups?

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Title: RUMS – how to compare structures of enterprise groups?
Authors: Rommelspacher, Simon1 (AUTHOR) Simon.Rommelspacher@destatis.de, Urban, Adrian1 (AUTHOR)
Source: Statistical Journal of the IAOS. Nov2024, Vol. 40 Issue 4, p941-948. 8p.
Subjects: Commercial statistics, Data structures, Data quality, Rum, Statistics
Abstract: In a globalised and interconnected world, enterprise groups play an increasingly important role in the economy. At the same time, it is becoming more difficult for official statistics to map these structures correctly to be able to analyse them for the various statistical areas. Enterprise groups have complex data structures that often come from different data sources of varying quality and need to be compared at different points in time. Comparing these complex and growing entities is not trivial and has therefore rarely been done in the past across countries and sources. To fill this gap, inspired by other similarity metrics, a new similarity metric has been developed in the German statistical business register: RUMS – Rommelspacher-Urban Metric for Similarity of enterprise groups. RUMS enables the comparison of enterprise groups within and between statistical business registers. This allows the quantification of changes over time or deviations in different business registers, which can help to detect data inconsistencies to increase data quality. RUMS is defined in a way that allows flexible adjustments to the weightings of the properties that are considered in the calculation based on the specific needs of the use case. [ABSTRACT FROM AUTHOR]
Copyright of Statistical Journal of the IAOS is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="DE" term="%22Commercial+statistics%22">Commercial statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Data+structures%22">Data structures</searchLink><br /><searchLink fieldCode="DE" term="%22Data+quality%22">Data quality</searchLink><br /><searchLink fieldCode="DE" term="%22Rum%22">Rum</searchLink><br /><searchLink fieldCode="DE" term="%22Statistics%22">Statistics</searchLink>
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  Data: In a globalised and interconnected world, enterprise groups play an increasingly important role in the economy. At the same time, it is becoming more difficult for official statistics to map these structures correctly to be able to analyse them for the various statistical areas. Enterprise groups have complex data structures that often come from different data sources of varying quality and need to be compared at different points in time. Comparing these complex and growing entities is not trivial and has therefore rarely been done in the past across countries and sources. To fill this gap, inspired by other similarity metrics, a new similarity metric has been developed in the German statistical business register: RUMS – Rommelspacher-Urban Metric for Similarity of enterprise groups. RUMS enables the comparison of enterprise groups within and between statistical business registers. This allows the quantification of changes over time or deviations in different business registers, which can help to detect data inconsistencies to increase data quality. RUMS is defined in a way that allows flexible adjustments to the weightings of the properties that are considered in the calculation based on the specific needs of the use case. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Statistical Journal of the IAOS is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – SubjectFull: Data quality
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              Text: Nov2024
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