The latest article in my blog about future trends is pertinent to the B.I. space, as information life cycle is a vital part of B.I. and in the bigger picture feeds into trends as knowledge management.
To this day the B.I. industry has no established and integrated method to track data relevance or manage the information life cycle, to the extent the digital media industry has pioneered (to enforce DRM). A lot of the existing data life cycle concepts in B.I. come out of the top-down architected Data Warehousing space, which, if applied, are heavily human-process controlled, with a variety of tools, each component and interfacing with the rest leaving room for mistakes, ultimately leading to questionable outcomes.
The basic understanding is here today, with a practice referred to as "data quality". One of the DQ metrics is "relevance". Today this determination is a rather manual process, always open to arbitrary decisions with little bearing to the real world. The people having to determine data relevance do not have all the insight of the users of the data, and the overhead for a few specialists to find out the broader scope & usage of data is often intolerable within the typical speed of business operations. Hence the need for a crowd-sourced kind of rating model for the relevance of data. Of cource, that will be subject to distortion based on perspective. But that multi-dimensional approach is nothing well thought out software could not manage.
If we had an intrinsic system, embedded in every aspect touching data, which would maintain life cycle intention and actual status at every stage of use, the knowledge maturity and currency described in my other article above could become common place.
I suspect that business will adapt any technology that will prove itself on the broader Web. Perhaps this aspect will ultimately evolve out of Web 3.0 or the "Semantic Web" ?
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