In a recent blog post, Reinventing the Business Intelligence Process, Michael Vizard highlights the importance of adding context to data as it is collected rather than after the fact. And preferably in a reasonably automated way. So it’s easy to forget that this is something that XBRL, and indeed any taxonomy-based data tagging paradigm, can be used for.
This number - 100 – is merely data. It means whatever you want it to mean because it has no context associated with it. Tagged with a bunch of XBRL tags our ’100′ could now be clearly defined as the pre-tax profit in a specific currency for a specific company, in a specific period of a specific fiscal year. Problem is, as Michael points out, this contextual tagging is being added after the fact. By XBRL tagging software like ours, for example. Adding context to data is the hidden burden of gaining insightful information.
But what if context was being generated as the data is being generated? Say you run a simple financial statement summarizing the income and expenses of your business. And say these rollup totals are already contextualized with XBRL tags. Then whatever calculates the ‘profit’ from the net of the two can automatically tag the resulting profit or loss with the appropriate contextual tags – assuming it is XBRL-savvy, taxonomy-aware and rule-driven that is.
One of the possibilities of XBRL is precisely to facilitate the automated generation of data context so that business intelligence can become just that, without depending on burdensome ‘after-the-fact’ tagging.
