Huff Po and a Search Vendor Debunk Big Data Myths
September 1, 2014
I suppose I am narrow minded. I don’t associate the Huffington Post with high technology analyses. My ignorance is understandable because I don’t read the Web site’s content.
However, a reader sent me a link to “Top Three Big Data Myths: Debunked”, authored by a search vendor’s employee at Recommind. Now Recommind is hardly a household word. I spoke with a Recommind PR person about my perception that Recommind is a variant of the technology embodied in Autonomy IDOL. Yep, that company making headlines because of the minor dust up with Hewlett Packard. Recommind provides a probabilistic search system to customers that were originally involved in the legal market. The company has positioned its technology to other markets and added a touch of predictive magic as well. At its core, Recommind indexes content and makes the indexes available to users and other services. The company in 2010 formed a partnership with the Solcara search folks. Solcara is now the go to search engine for Thomson Reuters. I have lost track of the other deals in which Recommind has engaged.
The write up reveals quite a bit about the need for search vendors to reach a broader market in order to gain visibility to make the cost of sales bearable. This write up is a good example of content marketing and the malleability of outfits like Huffington Post. The idea strikes me as something that looks interesting may get a shot at building the click traffic for Ms. Huffington’s properties.
So what does the article debunk? Fasten your seat belt and take your blood pressure medicine. The content of the write up may jolt you. Ready?
First, the article reveals that “all” data are not valuable. The way the write up expresses it takes this form, “Myth #1—All Data Is Valuable.” Set aside the subject verb agreement error. Data is the plural and datum is the singular. But in this remarkable content marketing essay, grammar is not my or the author’s concern. The notion of categorical propositions applied to data is interesting and raises many questions; for example, what data? So the first my is that if one if able to gather “all data”, it therefore follows that some is not germane. My goodness, I had a heart palpitation with this revelation.
Second, the next myth is that “with Big Data the more information the better.” I must admit this puzzles me. I am troubled by the statistical methods used to filter smaller, yet statistically valid, subsets of data. Obviously the predictive Bayesian methods of Recommind can address this issue. The challenges Autonomy like syst4ems face are well known to some Autonomy licensees and, I assume, to the experts at Hewlett Packard. The point is that if the training information is off base by a smidge and the flow of content does not conform to the training set, the outputs are often off point. Now with “more information” the sampling purists point to sampling theory and the value of carefully crafted training sets. No problem on my end, but aren’t we emphasizing that certain non Bayesian methods are just not a wonderful as Recommind’s methods? I think so.
The third myth that the write up “debunks” is “Big Data opportunities come with no costs.” I think this is a convoluted way of saying that get ready to spend a lot of money to embrace Big Data. When I flip this debunking on its head, and I get this hypothesis, “The Recommind method is less expensive than the Big Data methods that other hype artists are pitching as the best thing since sliced bread.
The fix is “information governance.” I musty admit that like knowledge management, I have zero idea what the phrase means. Invoking a trade association anchored in document scanning does not give me confidence that an explanation will illuminate the shadows.
Net net: The myths debunked just set up myths for systems based on aging technology. Does anyone notice? Doubt it.
Stephen E Arnold, September 1, 2014
Comments
-
- Subscribe to Beyond Search
Feature archive
News archive
-
