Last night the estimable Martin White, intranet and enterprise search expert and author of many books on the subject, flagged up two surprising articles from Forrester who have declared that Cognitive Search (we’ll define this using their own terms in a little while) is ‘overshadowing’ the ‘outmoded’ Enterprise Search, with a final dig at how much better commercial options are compared to open source.
Let’s start with the definition, helpfully provided in another post from Forrester. Apparently ‘Cognitive search solutions are different because they: Scale to handle a multitude of data sources and types’. Every enterprise search engine promises to index a multiplicity of content both structured and unstructured, so I can’t see why this is anything new. Next we have ‘Employ artificial intelligence technologies….natural language processing (NLP) and machine learning’. Again, NLP has been a feature of closed and open source enterprise search systems for years, be it for entity extraction, sentiment analysis or sentence parsing. Machine learning is a rising star but not always easy to apply to search problems. However I’m not convinced either of these are really ‘artificial intelligence’. Astonishingly, the last point is that Cognitive solutions ‘Enable developers to build search applications…provide SDKs, APIs, and/or visual design tools’. Every search engine needs user applications on top and has APIs of some kind, so this makes little sense to me.
Returning to the first article, we hear that indexing is ‘old fashioned’ (try building a search application without indexing – I’d love to know you’d manage that!) but luckily a group of closed-source search vendors have managed to ‘out-innovate’ the open source folks. We have the usual hackneyed ‘XX% of knowledge workers can’t find what they need’ phrases plus a sprinkling of ‘wouldn’t it be nice if everything worked like Siri or Amazon or Google’ (yes, it would, but comparing systems built on multi-billion-page Web indexes by Internet giants to enterprise search over at most a few million, non-curated, non-hyperlinked business documents is just silly – these are entirely different sets of problems). Again, we have mentions of basic NLP techniques like they’re something new and amazing.
The article mentions a group of closed source vendors who appear in Forrester’s Wave report, which like Gartner’s Magic Quadrant attempts to boil down what is in reality a very complex field into some overly simplistic graphics. Finishing with a quick dig at two open source companies (Elastic, who don’t really sell an enterprise search engine anyway, and Lucidworks whose Fusion 3 product really is a serious contender in this field, integrating Apache Spark for machine learning) it ignores the fact that open source search is developing at a furious rate – and there are machine learning features that actually work in practise being built and used by companies such as Bloomberg – and because they’re open source, these are available for anyone else to use.
To be honest It’s very difficult, if not impossible, to out-innovate thousands of developers across the world working in a collaborative manner. What we see in articles like the above is not analysis but marketing – a promise that shiny magic AI robots will solve your search problems, even if you don’t have a clear specification, an effective search team, clean and up-to-date content and all the many other things that are necessary to make search work well (to research this further read Martin’s books or the one I’m co-authoring at present – out later this year!). One should also bear in mind that marketing has to be paid for – and I’m pretty sure that the various closed-source vendors now providing downloads of Forrester’s report (because of course, they’re mentioned positively in it) don’t get to do so for free.
UPDATE: Martin has written three blog posts in response to both Gartner and Forrester’s recent reports which I urge you (and them) to read if you really want to know how new (or not) Cognitive Search is.