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• W145346803 abstract "ion Levels: Financial data sources provide data at incompatible levels of abstraction or classify their data according to taxonomies pertinent to a certain sector. For example, the SEC provides a taxonomic classification of sectors which is incompatible with other commonly industry sector classification schemes. Since data is being published at different levels of abstraction (e.g. person, company, country, or sector), data aggregated for the individual viewpoint may not match data e.g. from statistical offices. Also, there are differences in geographic aggregation (e.g. region data from one source and country-level data from another). A related issue is the use of local currencies (USD vs. EUR) which have to be reconciled in order to make data from disparate sources comparable and amenable for analysis. Finally, countries have sometimes vast differences in legislation on book-keeping thus, indicators from e.g. regulators from the Euro region may not be directly comparable with indicators from US-based regulators. The lack of agreement on even a common EU GAAP standard will hold up the publishing of XBRL in a standardized way and its multilingual accounting term translation. This will also be a problem for other legislators and jurisdictions when it comes to transcending national boundaries. Data Quality: Data quality is a general challenge when automatically integrating data from autonomous sources. In an open environment the data aggregator has little to no influence on the data publisher. Data is often erroneous, and combining data often aggravates the problem. Especially when performing reasoning (automatically inferring new data from existing data), erroneous data has potentially devastating impact on the overall quality of the resulting dataset. Hence, a challenge is how data publishers can coordinate in order to fix problems in the data or blacklist sites which do not provide reliable data. Methods and techniques are needed to  Check integrity, accuracy, highlight, identify and sanity check, corroborating evidence  Asses the probability that a given statement is true, equate weight differences between market sectors or companies  Clearinghouses for raising and settling disputes between competing (and possibly conflicting) data providers  Methods for interacting with messy erroneous web data of potentially dubious provenance and quality. In summary, errors in signage, amounts, labeling, and classification can seriously impede the utility of systems operating over such data [Ncsu09]." @default.
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• W145346803 date "2009-01-01" @default.
• W145346803 modified "2023-09-23" @default.
• W145346803 title "Challenges Ahead for Converging Financial Data" @default.
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