The XBRL data has been available for some years now, such as the FFIEC in the US has XBRL data for more than a decade, the SEC has some 7 years’ worth data for large accelerated filers and at least 5 years’ worth data for all companies within its purview. And here is the true test of XBRL. The ultimate fate of all data is to be distilled into information, and then into intelligent insights. The funnel is quite simple really and is one that we’ve all seen very often.
XBRL data is not very tough to extract. It is available from websites belonging to regulators (when this data is public, of course). It only requires a plug-in that can read the XBRL data and fill up a standardized presentation format, using that data. The trouble, though, comes next in the form of two challenges – Data Analytics and Data Visualization.
The sheer vastness of data makes it difficult to rely completely on a particular analysis tool for arriving at a decision. XBRL aids in fundamental analysis by capturing the focal points for decision making and helps in comparing different parameters for inferential analysis. Using XBRL data, fundamental analysis of companies becomes very easy as XBRL lends some degree of comparability to the fundamentals of a number of businesses (in spite of high extension rates). This should be effective at least for the XBRL data pertaining to the primary financial statements like the Balance Sheet, Income, Cash Flow and Equity statements. An analyst may need some categorization of data based on some filtering criteria. So for an analysis package, it is essential to do a thorough use-case analysis to ensure that useful data analysis can be performed.
Another question that arises is, “Do you restrict the input to only quantitative data, or do you want to use text block items for some qualitative analysis also?” A related query here is, “Do you use other sources of data at all, such as HTML submissions where available (like for the SEC) and RSS news feeds?” These are deep questions, because the answer could change the way the world looks at the usefulness of XBRL data.
Data Visualization lends us the inferential ability to make sense of the analysis. It helps us dig deep into complex models and arms us with graphical representations making interpretations easy. Humans are visual creatures. We love visual representations because we process them faster (or we think we process them faster). However, data visualization is much more fraught with danger than taking a photograph or other visible creations. This is primarily due to the difficulty of finding the perfect balance between aesthetics and effectiveness. These two problems are compounded by the variability of effectiveness and aesthetics from person to person. Consider the following example:
For the data in Table 1, some people may prefer Visualization 1 (Figure 1) while some may prefer Visualization 2 (Figure 2).
|Continent||Sales for 2013 (in mn USD)|
As far as aesthetics are concerned, some visualization does look unquestionably beautiful, while others do not. For example, a bump chart with color gradations may look much more beautiful than a bump chart with thin lines joining data points, but do the color gradations bring any extra information? Is the heavier load on the graphics worth it?
XBRL data is available. How we use it will depend on both regulators making comparability a factor in deciding XBRL quality, and on the tools that the XBRL ecosystem develops. Only when we have a thriving environment of XBRL data use, can we confidently say that XBRL is an effective data standard.
DataTracks US is part of DataTracks Services Limited, leaders worldwide in preparation of financial statements in EDGAR HTML, XBRL and iXBRL formats for filing with regulators. DataTracks prepares more than 12,000 XBRL statements annually for filing with regulators such as SEC in the United States, HMRC in the United Kingdom and MCA in India.
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