Views on improving the integrity of global capital markets
26 October 2015

Pesky Extensions: How to Improve Financial Analysis through Structured Data

Extensions — sometimes they’re a good thing, like hair and income tax varieties. But sometimes they’re problematic. Such is the case with extensions of XBRL data, an issue discussed recently at a meeting of the XBRL US Data Quality Committee of which I’m a member.

CFA Institute has long supported the use of structured data by companies, in particular XBRL (short for eXtensible Business Reporting Language). Structured data provides a standardized, interactive, computer-based framework for financial reporting and financial statement generation. Key benefits include increased efficiency, transparency, comparability, and timeliness in the delivery of financial information to all parties in the information supply chain. In other words, it allows for the democratization of financial information.

Many challenges, however, impede the successful implementation of XBRL — one of the biggest being the quality of the data. Data quality issues that impact the automated analysis of XBRL data include: inconsistent data modeling, unnecessary use of extensions, and input errors.

Our Committee focuses on data quality issues that adversely affect data consumption and analysis by users and prioritizes issues based on input from them. We are responsible for developing guidance and validation rules that can prevent or detect inconsistencies or errors in XBRL data filed with the US Securities and Exchange Commission.

We have determined that chief amongst the data quality issues that need to be addressed is the use of extensions. The strength of the XBRL framework is in the strength of the taxonomy that companies use when completing their filings. The taxonomy is essentially the dictionary of elements, or tags, that represent the concepts/fields of reporting that regulators require in financial statement filings. A robust, well-defined, and stable taxonomy can provide for greater precision and comparability between company reports than can be found in the paper formats companies use.

Managers of filing companies can, however, extend the core dictionary of fields. If companies extend the defined fields excessively, the platform will lose the vitally important benefit of comparability. Indeed in the US, some users report that approximately 70% of data elements can be directly mapped to the US GAAP taxonomy, while 30% are extensions. Such excessive use of extensions results in the need for manual intervention by users since analysis of extensions must be manually executed, whereby analysis of a taxonomy element can be automated across companies.

We — the Committee, as well as CFA Institute — believe in a structured approach to the use of extensions. Our CFA Institute publication, eXtensible Business Reporting Language: A Guide for Investors, states:

Individual extensions should be limited to those rare situations in which an item unique to that firm exists and the information about it does not fit into any of the concepts within the standard taxonomy or extension. We strongly encourage reporting companies to look first for the appropriate tag within the existing taxonomy before turning to a custom extension. If such a tag does not exist, we believe an extension should be allowed but within a well-defined framework so that no extension corrupts other financial statement relationships. Simply put, the automated relationships required by the computer remain: When a custom tag is inserted, the relationships remain intact and the numbers continue to sum up correctly.

The Committee plans to provide guidance for the appropriate use of extensions. The challenge in developing such guidance will be balancing the need for both comparability between companies and transparency in that companies need to tell their story.

To further the discussion on the use of XBRL, CFA Institute is participating in a 4 November event, “Improving Financial Analysis Through Structured Data.” Sandra Peters, CPA, CFA, head of the financial reporting policy group for CFA Institute, is the opening keynote speaker. Other panels will address how XBRL is being used today, and the structuring of regulatory data beyond the corporate financial realm. The event closes with remarks from Hal Schroeder, a board member of the Financial Accounting Standards Board.

I hope you can join us in New York for this event. If you’re unable to attend, please send us your comments on this subject.

More on the XBRL US Data Quality Committee:

Its validation rules will be freely available for incorporation into software solutions for tagging data using XBRL and for use by public companies and others.

In addition to CFA Institute, other organizations represented on the Committee include Bloomberg, Credit Suisse, Calcbench, Standard & Poor’s Capital IQ, Vanderbilt University, and the American Institute of CPAs, as well as representatives from the five members of the XBRL US Center for Data Quality: Merrill Corporation, RDG Filings, RR Donnelley, Vintage (a Division of PR Newswire), and Workiva Inc.

Though the work of the DQC is XBRL US-based, XBRL is a global standard for digitally exchanging business information that facilitates understanding and oversight of corporate performance.

If you liked this post, consider subscribing to Market Integrity Insights.

Image credit:

About the Author(s)
Mohini Singh, ACA

Mohini Singh was director of financial reporting policy at CFA Institute. She represented membership interests regarding financial reporting and disclosure proposals issued by the FASB, the IASB, and others. Singh holds the Associate Chartered Accountant (ACA) designation.

2 thoughts on “Pesky Extensions: How to Improve Financial Analysis through Structured Data”

  1. Robert Mudra says:

    Mohini, Great piece. Problematic extensions defeat the whole purpose!

  2. Mohini Singh, ACA says:

    Thank you, Robert. I couldn’t agree more.

Leave a Reply

Your email address will not be published. Required fields are marked *

By continuing to use the site, you agree to the use of cookies. more information

The cookie settings on this website are set to "allow cookies" to give you the best browsing experience possible. If you continue to use this website without changing your cookie settings or you click "Accept" below then you are consenting to this.