Posted on Thursday, June 30, 2016

Investors, analysts, data providers, public companies, XBRL software and service providers and other stakeholders invited to comment

XBRL US announced the start of its second public exposure period for guidance and rules developed by the Data Quality Committee (DQC), which is funded through the Center for Data Quality. The Committee is responsible for developing guidance and validation rules that can prevent or detect inconsistencies or errors in XBRL data filed with the SEC and focuses on data quality issues that adversely affect data analysis.

The public review that launched today contains guidance and five new proposed rules, as well as proposed additions to the DQC’s previously issued rule to detect inappropriate negative values. The proposed guidance and rules will help filers identify errors in the XBRL data due to inappropriate combinations of concepts, concepts that are no longer supported by the US GAAP Taxonomy and inappropriate positive or negative values. The DQC’s analysis shows that the use of these proposed rules would have identified potential errors in over 800,000 data points submitted to the SEC in the last year.

Comments received during the 60-day public review period will be evaluated for incorporation into final rules that will be made freely available and will be contributed to the open source Arelle XBRL platform. The public will be able to review a plain English description of the rules and guidance and be able to submit comments. Developers can download the open source code to incorporate into their own software, to use as a reference implementation against their software and to provide feedback.

To participate in the public review, go to:http://publicreview.xbrl.us.

Members of the XBRL US Center for Data Quality include Altova, the American Institute of CPAs (AICPA), Certent, DataTracks, DisclosureNet, Merrill Corporation, P3 Data Systems, Vintage, a division of PR Newswire, and Workiva. For more information on the XBRL US Data Quality Committee and the Center for Data Quality, go to:  http://xbrl.us/data-quality



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