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Enterprise search: What is it and where is it headed

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Docurated, a document management platform provider, published a remarkable compilation of essays. The collection of industry experts’ views appeared in May 2014 as “Enterprise Search: 14 Industry Experts Predict the Future of Search.”  What I found interesting is the role or rather the lack of it for enterprise knowledge management. The phrase appears three times in the analysis.

The first reference is made by Steve Nicolaou, principal consultant at Microsoft. A former Fast Search & Transfer professional, Nicolaou “now architects global solutions with SharePoint Search.” He says, “Elements of knowledge management add meaning to queries and results.”

The second reference comes from Seth Redmore, VP of product management and marketing at Lexalytics, a company engaged in sentiment analysis solutions. Redmore, the media and marketing contact for the firm, points out that enterprise search has to provide functionality beyond typical “search,” extending to facets, true knowledge management and multimedia search.

The third reference appears in the essay by Alex Gorbansky, the CEO of Docurated, who asserts, “Docurated [is] a next-generation visual knowledge management platform that solves the information retrieval problem for leading companies.”

Off its rails?

What struck me in this compilation of experts’ opinions is that knowledge management boils down functions like facets and “meaning.” Have these experts sidestepped knowledge management as a relevant component of search and a core feature of knowledge management?

The question I want to consider is, “What is enterprise search?” The Docurated interviews provide a number of interesting ideas. With mainstream vendors thumping on knowledge management and nudging it to the corners of a utility function, has enterprise search started to come off its rails? Search has been an enterprise application for decades. One of the more startling comments in the Docurated compilation makes clear that enterprise search has some built-in friction.

“Enterprise search is a developing industry,” Nicolaou states. “Until recently, much of the core technology remained unchanged since the 1970s and innovation was fairly limited to niche markets. The general purpose enterprise search offerings were fairly similar in technology and scope.”

The innovations he cites include big data, Google and cloud computing. The future is being invented now. He says, “With all the major software houses directing serious R&D toward enterprise search now, the future will bring shorter innovation cycles, continuous user experience improvements, deeper integration with first- and third-party applications and more ETL-like [extract, transform and load] functionality to handle poor quality content.”

Now and in the future

John Challis, CEO/CTO of Concept Searching, a company providing indexing solutions, takes a different approach. He points out that 15 years ago “enterprise search was ready to move beyond simple keyword and Boolean searching.”

He adds, “The future of enterprise search seems destined to continue with simple keyword and Boolean searching, augmented by faceted navigation based on metadata. The main driver for this is the World Wide Web. Virtually every e-commerce website today offers guided navigation based on metadata. When you enter a simple text query into Amazon or eBay or virtually any other shopping site, you see filters for ‘vendor,’ ‘price range,’ ‘color,’ ‘size,’ etc. This ubiquitous model now appears in most of the leading enterprise search products and users immediately understand how a simple text query can quickly be focused to a specific domain by clicking on a metadata filter.”

Lexalytics’ Redmore points to the impact of open source search solutions such as Lucene. He sees an opportunity in relieving the user of the need to formulate a query. He says, “Why should you have to ask first? Search has been traditionally driven by the searcher (duh), but interesting projects that allow for integrated understanding of where/what the user is doing allows for proactive intervention. Why wait for the slow brain to catch up to the fast machine, when the machine can push out what the user needs right then? Yes, this is functionality you’re starting to see with Google and other companies, but there are certainly interesting use cases for the larger enterprises, particularly internally to start, and then helping customers as they grow in their relationship with the company.”

I would characterize this view as “search without search,” an approach that I interpret as the Google self-driving automobile method applied to enterprise information retrieval.

Jim Jackson, product manager at search appliance producer MaxxCAT, highlights vertical applications of search. The devices allow for rapid deployment and easy scalability. His view is that of a professional who wants a user to have access to needed information. His view of the future is aligned toward systems that are capable of “returning finer-grained results that are not documents, but the exact sentence, the exact spreadsheet cell or exact information the user is looking for.”

He adds, “To facilitate our vision of implicit, contextual search, MaxxCAT is continuing to enhance our API to allow people and machines to leverage the tremendous search and performance capabilities of our information platform so that solutions can be built that connect people to the information they want.”

Incentives

David Murgatroyd, VP of engineering at Basis Technology, provides language components to search vendors. He perceives the enterprise search sector as “spurred on by both commercial needs and pressing governmental security challenges.” Analytics play an ever-larger role. Addressing the future, he notes, “Search will be increasingly entity-centric and collaborative.”

Murgatroyd says, “The user and the system collaborate best when they do so around a shared inventory of real-world entities. Analyzing those sometimes ambiguous entities accurately is best done with the added information provided by rich collaboration.”

Nick De Toustain, director of sales at LTU Technologies, points out, “Clearly there’s value to be had in being able to sort through a company’s disparate data sources and making them accessible so as to deliver actionable business intelligence. The questions for an enterprise search provider then become: How easy is your solution to implement?”

LTU Technologies, a vendor of image matching systems, takes a different posture toward enterprise search, specifically: “Whether it’s websites or social media, everything is becoming increasingly visual. There’s a corresponding need to ‘make sense’ of all that online imagery, which is where image recognition technology comes in. Future enterprise search tools will need to include image recognition capability to keep up with the massive amount of imagery being tweeted and posted every second.”

Blurring lines

Otis Gospodnetic, founder of Sematext, places enterprise search in the context of “search of one enterprise’s content ... or large-scale search.” He singles out Elasticsearch as an open source search vendor that can solve big data problems. For him, the future is “not really about pure search any more.”

Gospodnetic says, “We’ll see full-text search embedded in more applications and devices. We’ll see the line between non-search and search software and servers blurred even more. I suspect we may see people putting query languages with familiar syntax, such as SQL, on top of search engines to enable people to write powerful queries more easily while hiding the original query syntax people typically use with search engines today.”

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