XBRL 2.0

Digital financial reporting that actually works


It’s instructive to view how publicly-traded compliance software companies prepare their own XBRL filings. After all, these are the XBRL experts. There are two such companies – Workiva (WK) and Donnelley Financial (DFIN). This blog compares their most recently reported income statements.


In this statement, the components of [Net Sales] are correctly shown on the [ProductOrServiceAxis]. Then, Donnelley inexplicably abandons dimensions in reporting [Cost of Sales]. [Net Sales] and [Cost of Sales] should have a consistent taxonomy, sharing the same domain elements. Why is this important? Analysts care about margins. In this case, calculating  gross margin by sales type requires manually aligning domain elements with primary elements. While this XBRL data may be machine-readable, it’s no longer machine-understandable.


Then Donnelley determines that both [Cost of Sales] and [SG&A] should be custom items to highlight the exclusion of [D&A]. [SG&A] in the standard taxonomy doesn’t include [D&A], so that extension is unnecessary. And while [Cost of Sales] in the standard taxonomy does include [D&A], very few companies explicitly include or exclude the item. And [D&A] can always be reconciled against the cash flow statements disclosure of total [D&A]. In any case, the [D&A] clarification can be made with custom labels without defeating the statement’s comparability.


This statement constitutes a well-formed, compliant reporting model. No extensions, no block errors, no taxonomy errors, no validation errors, no missing or erroneous relations. Revenue and Cost of Revenue are described using the same structure and domain members.


Judging solely from this example, Workiva gets it. Donnelley doesn’t. The purpose of XBRL is to achieve comparability by converting statements to a standard taxonomy. Donnelley’s extended taxonomy model is both non-standard and inconsistent, inhibiting users’ ability to easily digest the data. Workiva’s presentation is more user-friendly, producing statements that can be both read and analyzed.