Realizing the Potential of Structured Data
Editor’s Note: This post was originally published on www.xbrl.org on 7 September 2017.
Despite its presence and use for several years now, XBRL has not achieved its full potential for either investors or companies in the United States. One reason is that companies continue to see structured data as a compliance and cost burden, and they have shared these views with regulators. Securities regulators worldwide are examining costs and some of what has been learned may be particularly relevant to them.
The CFA Institute paper, “The Cost Of Structured Data: Myth vs. Reality,” summarized here, examines the costs that large and small companies bear in preparing and filing their financial information in a structured format and what can be done to mitigate those costs. CFA Institute, an investor organization, seeks to address this issue so that all parties — preparers, regulators, and users — can avail themselves of the various benefits of structured data.
We began our study by examining what companies are saying about the costs associated with their XBRL filings. We then went through several case studies on large and small publicly traded companies as well as non-profit organizations. One of our major takeaways is that the way a company implements XBRL reporting — that is, whether the work is outsourced to a vendor or done in-house — directly affects its costs and that cost reductions can be achieved by bringing the structured reporting process in-house.
The current manual processes used by companies to assemble and review reports requires both time and money. These processes can be streamlined if companies standardize their data, which may be housed across disparate data sources in-house, early in the reporting process. When data are standardized, disclosure management applications can pull information from disparate data sources to write automated reports, enabling the streamlining of current labor-intensive processes. Such standardization not only saves companies time and resources but also reduces data errors because of less manual intervention.
However, companies continue to view structured reporting as a compliance exercise. As a result, most companies do not structure their data into a machine-readable format at the source early in the financial reporting process. Instead, they follow a two-tier process whereby filers outsource the tagging of their data as an additional step, after their financial statements have been prepared, simply to fulfill their regulatory filing requirements. Consequently, structured reporting is not producing the intended results, which are (1) increasing the speed and frequency with which financial information is prepared, reported, analyzed, and used and (2) reducing costs. The benefits of in-house implementation are illustrated in the paper through a case study of the Wacoal Corporation, a global women’s apparel manufacturer. That the Wacoal example is “vintage” demonstrates just how advanced the company’s implementation really was at the time. Companies today are using disclosure management tools that do all the things the Wacoal Corporation was able to accomplish. In what might be called an innovation vacuum, software developers in other parts of the world are stepping into the void with innovative offerings.
Furthermore, a case study of United Technologies Corporation’s adoption of XBRL (by bringing it in-house) shows that concerns regarding resources required, cost, and technical proficiency were without basis. In addition, by applying XBRL technology and automating many of the manual assembly and review processes eliminated 150–200 hours of labor from the quarterly reporting process.
It is not just large, global multinational companies who can benefit. A small non-profit used software tools to transform internal accounting financial data into XBRL and then repurposed it in multiple formats for reporting, analysis, and publication — all in-house. When the project was complete, the non-profit filed its financial statements in a structured format. This use of XBRL could also allow non-profits to file their Form 990, Return of Organization Exempt from Income Tax, via XBRL, turning a lengthy and arduous process into a very quick one.
In addition to bringing the XBRL initiative in house, we believe two additional “best practices” include implementing the use of Inline XBRL (iXBRL) and curtailing the use of extensions.
Under iXBRL, all XBRL data are contained in ordinary, human-readable files. Because a single iXBRL report can be viewed on a screen and analyzed by software, no viewer is required to convert an XBRL filing into a human-readable form — resulting in cost savings.
Our belief is that regulators should curtail the current excessive use of extensions or custom tags that are not easily machine comparable and therefore hamper investment analysis. However, investors also need information that is entity specific in order for it to be meaningful to their financial analysis. We thus believe that it is necessary to allow for the use of company-specific extensions within a framework that restricts their use to rare circumstances. As noted in eXtensible Business Reporting Language: A Guide for Investors, “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.” This is a key issue around the world, so critical in fact that XBRL International formed the Entity Specific Disclosures Task Force to improve the handling of entity-specific disclosures, including defining when best to use extensions and to improve the comparability of extensions and the filings that use them.
Only proper implementation will enable organizations, large and small, to realize the benefits of structured data, namely reducing costs and creating efficiencies. Key elements of successful implementation include the following:
- Bringing structured data initiatives in-house instead of using outside vendors to prepare their regulatory filings
- Implementing Inline XBRL (iXBRL) — a form of XBRL that is both human and machine readable
- Curtailing the use of company-specific tags or “extensions”
By following this prescription, filers and other organizations increase the likelihood that their structured data initiatives will provide the cost and efficiency benefits while also offering greater transparency for regulators, investors, and other users.
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