The XBRL US Data Quality Committee (DQC) has published its seventh set of Data Quality Rules for a public review and comment period to end September 10, 2018. DQC rules and guidance, which are freely available, are designed to be used by issuers to identify and correct errors in their SEC filings. The DQC, which is funded through the Center for Data Quality, 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.
Twelve new rules are included in the seventh ruleset, covering tagging under the new revenue recognition standard, and potential problem areas with tagging pension data.
Rules and guidance developed by the DQC are free and publicly available. Issuers, vendors, analysts, and investors can test out the draft rules and provide comments related to the rules through the public review page: https://xbrl.us/data-quality/rules-guidance/public-review/.
The DQC has scheduled a webinar on August 29 to review the rules and guidance for Ruleset 7. Register for this free, one-hour webinar: https://xbrl.us/events/20180829/.
Final rules are freely available for both US issuers filing in US GAAP, and for foreign private issuers filing in IFRS. Issuers should use approved rules for all filings prior to SEC submission through one of these options:
- Through software that has been certified to run with the most current ruleset: https://xbrl.us/data-quality/certification/
- By using the XBRL US checking tool: https://xbrl.us/data-quality/rules-guidance/check-filing/
- By downloading the Approved Rules (Ruleset 6) and using them with Arelle – the open source version of the SEC’s EDGAR Renderer/Previewer: https://github.com/DataQualityCommittee/dqc_us_rules/releases/tag/v6.0.1
Members of the XBRL US Center for Data Quality include Altova, the American Institute of CPAs (AICPA), Certent,DataTracks, Merrill Corporation, P3 Data Systems and Toppan Vintage. For more information on the XBRL US Data Quality Committee and the Center for Data Quality, go to: http://xbrl.us/data-quality. For information on how to support better quality XBRL data by joining the Center for Data Quality, email email@example.com.