In the classic book, The Innovator’s Dilemma, Clayton Christensen concludes that a key reason leading companies fail is because they spend too much energy working on sustaining innovations that continuously improve their products for their existing customers. Seemingly paradoxically, he points out that these sustaining innovations can involve very advanced and very expensive technology. That is, it’s not the nature of the technology used (e.g., advanced or simple) that causes innovation to be sustaining or disruptive — it’s who the technology is designed to serve and in what uses.
I think search vendors need to dust off their copies of The Innovator’s Dilemma. Why? Because, for the most part, they seemed wedged in the following paradigm, which I’d call the relevancy quest:
- Search is about grunting a few keywords
- The answer is a list of links
- The quest is then magically inducing the most relevant links given a few grunts
And it’s not a bad paradigm. Heck, it made Google worth $140B and bought Larry and Sergey a nice 767. But can we do better?
Some folks, like the much-hyped Powerset, think so. They’re challenging the grunting part of the equation, arguing that “keyword-ese” is the problem and the solution is natural language. They seem unphased both by Ask Jeeves’ failure to dominate search and by the more than 20 years of failed attempts to provide natural language interfaces to database data, used for business intelligence (BI). As I often say, if natural language were the key to BI user interfaces, then Business Objects would have been purchased by Microsoft years ago for a pittance and Natural Language Inc.’s DataTalker would rule BI. (Instead of the other way around.)
But I respect Powerset because at least they’re challenging the paradigm and taking a different approach to the problem. And, while I sure don’t understand the cost model, I also respect guys like ChaCha because they’re challenging the paradigm, too. In ChaCha’s case, they’re delivering human-powered search where you can literally chat with a live guide who helps you refine your search.
I can also respect the social search guys, including the recently launched Mahalo, because they’re challenging the paradigm as well — using Wisdom of Crowds / Web 2.0 / Wikipedia style collaboration to created “hand-written results pages” for topics, such as the always searchable “Paris Hilton.”
The folks I have trouble understanding are those on the algorithmic relevancy quest, companies like Hakia, a semantic search vendor (interviewed here by Read/Write Web) whose schtick is meaning-based search, and who comes complete with a PageRank ™ rip-off-name algorithm called SemanticRank ™. Or Ask who recently launched a $100M advertising campaign about “the algorithm“. These people remind me of the disk drive manufacturers who invested millions in very advanced technologies for improved 8″ disk drives (to serve their existing customers) all the while missing the market for 5.25” disk drives required by different customers (i.e., PC manufacturers).
Are the Hakias of the world answering the right question? Should we be grunting keywords into search boxes and relying on SomethingRank ™ to do the best job of determining relevancy? Is the search battle of the future really about “my rank’s better than you rank” or equivalently, “my PhD’s smarter than your PhD”? Aren’t these guys fighting the last war?
As usual, I think there are separate answers for Internet and enterprise search.
On the Internet side, sure I think search engines can certainly use more “magic” to improve search relevancy. For example, they can use recent queries and a user profile to impute intent. They can use dynamic clustering and iterative query refinement (e.g., faceted navigation) to help users incrementally improve the precision of their queries.
More practically, I think vertical search and community sites are a great way of improving search results. The context of the site you’re on provides a great clue to what you’re looking for. Typing “Paris Hilton” into Expedia means you’re probably looking for a hotel, where typing it EOnLine means you’re looking for information on the jailed debutante.
Of course, there are a host of Web 2.0 style techniques to improve search like diggs and wikis which can be put to work as well.
Increasingly, our publishing and media customers are going well beyond “improving search” and changing the paradigm to “content applications” — systems that combine software and content to help specific users accomplish specific tasks. See Elsevier’s PathConsult as a concrete example.
On the enterprise search side, I think the answer is different. As I’ve often mentioned, on the enterprise side you lack the rich link structure of the web, effectively lobotomizing PageRank and robbing Google of its once-special (and now increasingly gamed and hacked) sauce.
When I look for the answer of how to improve search in an enterprise context, I look back to BI, where we have decades of history to guide us about the quest to enable end-user access to corporate data.
- Typing SQL (once seriously considered as the answer) failed. Too complex. While SQL itself was the great enabler of the BI industry, end users could never code it.
- Creating reports in 4GL languages failed. Too complex.
- Having other people create reports and deliver them to end users was a begrudging success. While this created a report treadmill/backlog for IT and buried end-users in too much information, it was probably the most widely used paradigm.
- Natural language interfaces failed. Too hard to express what you really want. Too much precision required. Too much iteration required.
- End users using graphical tools linked directly to the database schema failed. While these tools hid the complexities of SQL, they failed to hide the complexity of the database schema.
It was only when Business Objects invented a graphical, SQL-generating tool that hid all underlying database complexity and enabled users to compose an arbitrary query that the BI market took off. Simply put, there were two keys:
1. The ability to phrase an arbitrary query of arbitrary complexity (not a highly constrained search).
2. The ability to hide the complexity of the database from the underlying user
While no one has yet built a such a tool for an arbitrary XML contentbase (and while I think building one will be hard given the lack of requirement for a defined schema), MarkLogic customers use our product every day to build content applications that generate complex queries against large contentbases, and completely hide XQuery from the end-user.
Simply put, it’s not about improving search. It’
s about delivering query. That’s the game-changer.
BI itself is poised to bring a lot of value to enterprise search. In the process of implementing BI, writing reports, and creating dashboards and scorecards, organizations have created a lot of the metadata needed to make enterprise search more meaningful and useful…