Gartner’s Magic Quadrant for Enterprise Search is officially dead – something that probably should have happened years ago when search became so commoditized. Corporate users don’t want “search.” They want insights. Thankfully, there’s a new Magic Quadrant for that.

But first, a quick look back at the old Enterprise Search MQ, which was defined as technologies that “relate users’ queries to many different kinds of information in order to identify relevant, contextualized information and, in the process, perform light analysis.” No offense to my friends at Gartner who were simply summarizing the traditional market definition, but I lost interest in traditional “enterprise search” after my first Lucene implementation many years ago. And let me tell you, SOLR isn’t that much more interesting.

Thankfully, I fell in love with the much more powerful application of search-driven insights while working with a Fortune 100 telecom to proactively push information to their users based on who they were, what they were trying to do, and even what environmental factors existed (such as a known outage in the customer’s neighborhood). We didn’t even refer to it as search because nobody was clicking a search button. This was much more than the “Google experience,” and this is what I have strived for with my clients ever since.

Flash forward to this year’s Insight Engines MQ. Gartner defines the category as technologies which “apply relevancy methods to describe, discover, organize and analyze data. This allows existing or synthesized information to be delivered proactively or interactively, and in the context of digital workers, customers or constituents at timely business moment. Insight engines provide more-natural access to information for knowledge workers and other constituents in ways that enterprise search has not.” Now this is something to be excited about!

I’ve always said that with enough integration work, any search engine can index any data source. But only Intelligent Search Insight Engines help users discover data that they didn’t even know existed. This is a powerful solution to the “I don’t know what I don’t know” problem.

Organizing and analyzing that data, especially with the recent advances in machine learning, creates an even more powerful situation of having all of the data you need at your fingertips and ready for action.

Proactively pushing that insight to each user on an as-needed basis, even if they didn’t originally know what they needed…that is revolutionary. And this is exactly what I have been doing for the past 3 years at Coveo. We like to call it “the Relevance Revolution.”

Relevance Maturity Model

Coveo Relevance Maturity Model

Maybe it’s just my project management and Capability Maturity Model (CMM) background, but I love this relevance maturity model. Although it is trademarked by Coveo, the concepts apply to any enterprise search / insight engine program.

When I was in consulting, we would often replace multiple silo’d search engines (level 0) with a unified solution (level 1). Sometimes there would be faceted navigation (level 2), but it was often degraded by significant challenges in normalizing metadata across the multiple repositories.  The allure of relevancy tuning (level 3) was certainly offered by most search vendors. But I know more people who have won the lottery than I know companies who are able to successfully tune complicated platforms like Inquira by themselves, which is why the consulting company I worked at charged $350/hr for me to do this optimization work for our clients. Contextual suggestions (level 5) and truly self-learning predictions (level 6) that go beyond simple boosting of content based on click count – those were and still are quite rare in “enterprise search.”

Then after several years of following Coveo, I observed a breakthrough in how they decided to tackle these challenges. Metadata is easily ingested through productized connectors that understand the object model of the system being searched with powerful normalization capabilities within the index. Relevance tuning is handled with slider bars, not complicated and proprietary scripting language. Contextual relevance is realized through native integrations to the platforms your users engage with so that Coveo naturally knows who they are and what they are doing. Coveo long ago realized that “enterprise search was dead” and instead choose to focus on “Intelligent Search” and the insights they produce.

Gartner MQ for Insight Engines

It’s easy to see why Coveo owns the top-most and right-most position in the new Insight Engines Magic Quadrant.

So, yes, this is a biased post that mentions my current employer much more than I traditionally do on this blog. However, this philosophy of proactive insights represents why I joined Coveo, is the reason why so many of my clients think beyond search and have chosen Coveo, and I believe is what caused Gartner to rethink their Magic Quadrant definition.

And while my title will remain “Intelligent Search Evangelist” for a bit longer until “Insight Engines” is more commonly understood in the market, I look forward to the day when “search” is reserved for and companies demand nothing less than “Insight Engines.” I’d love to hear your thoughts in the comments below.