Mar 21

eXtensible Business Reporting Language. Part 5: Wrap-up

by David Harper, CFA, FRM, CIPM

CFA |

This concludes a summary of ideas from our screencast An Introduction to XBRL (The screencast earns 0.5 credit hours under the Professional Development (PD) program at CFA Institute.)

It takes two to tango (filers who supply + analysts who consume)

Because XBRL has suffered from a chicken-or-egg problem, adoption may very well be exponential (i.e., due to a tipping point phenomenon). XBRL reduces to a simple idea: tag the data. Tagged data becomes structured and highly actionable. In the screencast, we characterize this as supply/demand.

Supply refers to corporate filers who provide data in XBRL-tagged format; demand refers to the users who "consume" XBRL. Historically, it's been a chicken-or-egg problem because: analysts haven't embraced XBRL largely because only a handful of companies voluntarily adopted and the tools haven't been compelling. On the supply side, why would companies file XBRL until their constituents clamored for it?

 

But thanks to Christopher Cox, the SEC has given XBRL a boost (don't get me wrong, pervasive XBRL was inevitable, but he is speeding it along)

Conclusion

I hope this tutorial helps you understand the basic mechanics of XBRL and, more importantly, why XBRL matters. I'll conclude by recapping three ideas:

  • XBRL is an industry-specific (financial reporting) version of XML. It allows us to tag financial data items (e.g., gross revenue) with agreed-upon labels. Our agreed-upon definitions are found in a taxonomy.
  • Corporate filers need to supply their financial data; consumers (e.g., regulators, analysts) can then use one of a growing list of tools to read and manipulate the financial data.
  • The benefits are like a layered cake. At the bottom, the easy stuff: fewer errors. Over time, we will start to take value-added advantage of structured data. At first, better processing and application mashups ("what do you want to see today"). Right now, this manifests as dashboards and business intelligence. Later, the software will start to add intelligence. The human analysts will be liberated to walk up the value stack, freed from manual processing  so that he/she can focus on interpretation and insight.

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