Here’s a boring idea: treat knowledge like an asset and employ the “best practices” of business intelligence and rigors of data governance to gain a tremendous return on investment.

When history majors try to organize themselves and describe their work like data scientists, it doesn’t just sound boring, it sounds perilous. Knowledge Management 1.0 failed. The entire billion dollar wasteland of Knowledge Management tried to do just that – and it cost businesses a lot of money and the field just about all of its credibility. The catch is that it should have worked.

Here’s a revolutionary idea: treat knowledge like Google does.

Google’s in the knowledge business

I’m willing to concede that putting “like X does” and “revolutionary” in the same sentence is somewhat counter-intuitive and perhaps even oxymoronic. But here’s the thing – very few people actually think this and fewer act on it (and even fewer use Google’s own tools to do it).

Google’s premise is surprisingly simple: people create content for a reason, and the ones that do it better will be found, linked to, and shared by others. Furthermore, you can help people find more relevant content by tapping into the things they do most – email, create documents, share videos, and search. As Google likes to say: they don’t have better algorithms, they have better data. They probably also have better algorithms.

Knowledge works the same way – people gain expertise or organizational knowledge for a reason, some better than others, and people gravitate to those people. They get a lot of email and meeting requests from people looking to pick their brain. They host webinars, speak on panels, and write up white papers. You can probably name 3 people like this in your organization right now who could get you whatever info you need or put you in touch with someone who could.

But for the most part, Enterprise 2.0 systems aren’t geared to then tap into what the non-expert is doing in order to better help them find the expert or discover what the expert has already done. Put another way, you have to be really good about knowing what you don’t know and need to find in order to make the “prediction engines” of today useful.

It’s ironic when you think about it: the prediction tools only work well if you know what you need to find (although to be fair, they’re getting better). There’s huge potential, so if you’re reading this and it’s your job to design the algorithms and crunch the data, we’re all waiting eagerly.

A quick case

If I’m working on a grant that involves a program that was launched a year ago that involves mentoring social entrepreneurs to pitch their ideas, cultivate a board, and run a pilot I’m going to create a fair amount of email and content (docs, spreadsheets, and decks) that all relate to that. Now, wouldn’t I want a system that would say “hey, by the way, how would you like to hear a story from one of the participants or an interview from the leader of the initiative? They were on a recent interview with another program team.”

This is basically how Google’s Adwords program works but we have nothing nearly as accurate in the enterprise.

What’s broken

The root cause of why most KM or Enterprise 2.0 initiatives don’t work is because they force these experts – who are already incredibly busy being good at their job – to do something extra. This is a stark contrast to web search that relies on intelligent algorithms to do the extra for us.

If a web page had to do the same thing, it would be like recreating the web page – in slightly different forms – each time someone different clicked on it. Insanity. To make matters worse, you’d have to recreate the webpage again when someone new came into the picture. It might just look like MySpace.

Heck, even some newspapers have algorithms writing stories for them.

We need a revolution back to basics

I don’t mean technologically – I mean strategically. We need to focus on simplicity and letting people do things naturally. Will my Senior VP really care about getting a virtual sticker for writing a blog post just because? No. Are they an expert? Yes. Will they care about not answering the same question about our partnership with AmeriCorps? Yes. Will others benefit from easy access to their knowledge? Yes.

So let’s go back to when experts share in the flow of work and others can find and add on to that knowledge easily and quickly.