Investors, analysts, data providers, public companies, XBRL software and service providers invited to comment
XBRL US announced the start of its third 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 over 3,000 proposed additions to the DQC’s previously issued rule to detect inappropriate negative values. The rules include concepts reported in company face financials as well as footnote disclosures. A webinar will be held on Wednesday, October 19 at 3PM ET to provide details about the proposed rules and explain how to provide comments.
“Facts that are incorrectly reported as negative values in corporate financials reduce the ability to automate data processing and can delay the availability of data to investors,” said Pranav Ghai, co-Founder, Calcbench, “We continue to see incorrect negative signage as one of the bigger problems in XBRL-formatted financials.”
Comments received during the 60-day public review period will be evaluated for incorporation into final rules that will be made freely available and contributed to the open source Arelle XBRL platform. The public will be able to review a plain English description of the rule and a listing of the proposed elements to be added 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.
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