I was invited to participate a little while back in the Collective Intelligence FOO Camp held at the Googleplex and organized by Hal Varian, Tom Malone, Tim O’Reilly, and Gary Flake. If you’re wondering what the whole thing was about, so were we attendees. Our closing session was devoted to trying to define exactly what ‘collective intelligence’ was. The most popular explanation came from Kim Rachmeler "The network knows what the nodes do not." In a piece of brilliant showmanship she also offered the near-haiku "The nodes know nothing. The nodes know all. Both are true."
I volunteered to host a discussion on "What does corporate America think of collective intelligence." We were happily belaboring the topic when Tim O’Reilly walked in, listened for a while, then essentially stated that we were barking up the wrong tree by focusing on blogs, wikis, tags, prediction markets, and the other standard tools.
He said that we should be concentrating on implicit, not explicit, user-generated information. And he offered a bet that implicit would turn out to be the more valuable of the two types. Much subsequent discussion considered whether this was a false dichotomy, but after reflection I don’t believe that it is. To explore the issue, let me start by offering my own definitions of the two:
Explicit user-generated information is information that people knowingly and deliberately generate by contributing to online platforms. Examples of explicit information include a blog post or comment, a wiki edit, a vote or rating, a trade in a prediction market, a link, and a tag.
Implicit user-generated information is information that people unknowingly generate as they work online. It’s the digital fingerprints or traces that people leave as they follow links, look at content, consider one product then buy another, etc. This data can be aggregated to show what’s popular, what’s related, who has a good reputation, etc. My impression is that the collection and analysis of implicit online information grew out of Web analytics (clickstream data) and eCommerce recommendations (customers who bought [shopped for] this also bought [ended up buying] this). I find these recommendations tremendously valuable, and they’re entirely implicit.
Another type of implicit information is the aggregation of individuals’ explicit contributions. Two of the best-known examples of this are Google’s PageRank algorithm and tag clouds like those at del.icio.us and Flickr. As I wrote earlier, people create links and tags largely out of self-interest, but these activities have substantial group-level benefits; they reveal the overall structure of online content and so help everyone navigate and find information efficiently. Tools like PageRank and tag clouds turn online content into an emergent system — one in which structure clearly exists and changes over time, but that structure can’t be inferred from examining the work of any single actor, and the actors themselves are unaware of the overall structure (just as is the case with an ant colony, one of the classic examples of an emergent system).
The concept of emergence suggests a quick ‘sniff test’ for whether a given piece of digital information should be considered explicit or implicit. If it’s shown to the people who generated it, would they say "Oh, yeah — I knew that" or would they say "I had no idea!"? If the former, it’s explicit. If the latter, it’s implicit.
I also want to emphasize a few other distinctions related to user-generated content that might be relevant for decision makers:
- Individual-level contributions (blog posts, tags, shopping cart additions) vs. group-level ones (wiki edits, trades in a prediction market). The difference here is that others are directly affected by the latter type, and so probably more likely to make their own contribution in response.
- Above-the-flow contributions vs. in-the-flow ones. Again, the latter are more likely.
- Altruistic contributions (edits to another workgroup’s wiki) vs. self-interested ones (trades in a prediction market, which are intended to increase the value of an individuals’ portfolio). Here again, the latter seem more likely.
- Deliberate actions (rate, vote, trade, post) vs. passive ones (click, browse). Same story.
- Currently private (emails) vs. invisible (clicks) vs. public (comments). Users can’t really complain about the latter being made visible, and they probably won’t complain too much about the middle category, as long as it’s anonymized. But technologies that analyze currently private information in hopes of making or suggesting connections might be trouble. I’ve heard of a few corporate efforts to analyze employees’ email traffic in order to say something like "You seem interested in protein folding / ISO 9000 certification / declining CD sales / whatever. We know of other people in the company who are interested in the same thing. Would you like an introduction to them?" I appreciate the intent behind such efforts, but wonder how they’ll be received. Many people consider their email boxes to be private (I know I do) and might not like the thought of their employer peering into them, even with the best of intentions. At the same time, though, many of us (myself included) don’t mind the thought of Google scanning our emails in order to serve us ads, so the situaion is fluid.
So was O’Reilly right that implicit is more valuable? During our discussions at the CI FOO, John Riedl pointed out that because impicit information is typically so much more voluminous it can be more valuable in aggregate. But I think that even if Tim is right, his wager is of more academic than practical interest.
This is because no matter which side of the bet you come down on, the smart move is to encourage explicit contributions. Doing so will lead to more implicit content in two ways. First, as Riedl pointed out there will be a huge amount of implicit content generated as a byproduct of the explicit content — think of all the possible ways to look at Wikipedia article creation and editors. Second, more online content of any form means more browsing and passive consumption. This browsing yields another body of clickstream-ish implicit content — for example all of Wikipedia’s page views.
So if you’re a believer in the power of explicit user-generated content, encourage it. If on the other hand you’re a believer in power of implicit information, encourage explicit user-generated content because that’s the best way to get what you really want.
What have you and your organization learned from explicit and/or implicit information that you would not have known otherwise? Leave a comment, please, and let us know.
{ 24 comments… read them below or add one }
Andrew,
Explicit and implicit user-generated information are both important. When combined, they offer a powerful way to create knowledge.
In our case, we track both and combine them to accomplish what we want. For example, in order to determine how much a group values a specific discussion or document, we give users the chance to rate the content i.e. explicit voting. However, our experience has shown that users in the enterprise often don’t bother to vote for a variety of reasons. Therefore, we measure their implicit behavior as well. We track information like how many people read something, if they put the content on their watch list, if they recommend it to others, etc. We then combine the explicit and implicit information to create a dynamic score for every piece of content.
This score is also important as it helps us generate inferred expertise. In many enterprise organizations, it can be difficult to find experts on specific topics. Most of the time, it requires explicit information like a recommendation from someone or some kind of list to identify them. However, implicit user-generated information is equally important, and perhaps even more accurate as OÂ’Reilly is claiming. We combine the groupÂ’s reaction to peopleÂ’s contributions, and correlate the scores with the tags the experts use or we assign to infer how much expertise they have and on what topics. We would not be able do this if we relied on explicit user-generated information by itself.
I agree explicit and implicit user-generated information does not present a false dichotomy. However, since implicit user-generated content is dependent upon explicit user-generated content, rather than say “implicit would turn out to be the more valuable of the two types,” a better statement may be that implict user-generated information is the greater of the two when it comes to value generation — it’s what adds value to the explicit user-generated content.
I work for the U.S. government and use enterprise 2.0 tools on our intranets extensively, which were launched by the DNI in 2006. Nearly everyday the implicit information we generate helps me find the explicit information I need — whether it is a report, a discussion on a blog or Intellipedia page, or discovering people working similar issues via Intelink bookmarks. Even our Google search of the intranet has improved from the robust hyperlinking these tools have enabled and the promotion and interlinking of relevant content. As an explicit user-generated example, I likely would not have discovered this Harvard blog as soon as I did (last month) if it were not for a blog on our intranet and an associated trackback.
Nice article. I guess what i learned is that in simpler terms explicit information is a type of user generated data. For example,when someone comments on a you tube video or this blog for example. However,explicit is the type of data that marketers like myself like to collect. for example, like the pages of a site that users visit and stay on the most. It seems like implicit is more private data while explicit is more public data?
Thanks Andrew. Knowing both implicit and explicit information can drive higher levels of value. For example…
In our collaboration work, we’ve been able to provide large digital ‘jam’ sessions that allow 1000′s of people to explicitly collaborate and solve a common problem. Undoubtedly, we get some interesting ideas when we go to a very large crowd of people. However, The value goes beyond the explicit content and suggestions provided.
We also have implicit insight into who is passionate in resolving any given issue. And we know other interesting demographic information around the top contributors. It’s not just the content but the activity patterns and levels that we’re interested in.
This knowledge allows us to take the explicit input from 1000′s of people and invite a smaller group, based on implicit data, of really driven, passionate, and smart people to do an even deeper analysis and formulate & execute on specific strategies, recommendations and plans.
We’ve found the best teams aren’t just smart teams. They are smart and driven and combines both explicit and implicit knowledge.
This is a great discussion topic. I am in sales in the eLearning space and more and more of my customers are looking towards this particular topic to augment eLearning and other training materials. By leveraging explicit and implicit data folks within training and education communities would be looking at a completely new way of learning. Instructional design philosophies could be impacted significantly when you take these options into consideration. It is my thought that this concept can and will change education/training forever.
In reading this blog, I learned the real differences between implicit and explicit data collection and why they are both very important. In my opinion, you must have both. Many people, like myself, think about specific topics but would never think to write about them. Many people are very intimidated by putting anything in writing especially if they donÂ’t consider themselves the expert in that particular topic. They would never think to publish it on a blog or some other form of media however, those same folks could be compelled to comment on, revisit or share information they read about. Implicit data then becomes the silent person’s way of contributing without risk.
My fear within this web 2.0 realm is always the potential for abuse and misinformation. I don’t worry too much about abuse or misinformation within an enterprise, but I do wonder more about the potential for corruption or abuse within the public domain. Google makes money off of charging companies for a higher ranking on their search engine. That is their business model and it works for them. What if it is not about money? Will truth prevail? What would happen if a company, political party or religion wanted to influence data within the web 2.0 realm so that their information is it the highest ranked/rated? Is there potential for these ghost organizations to appear and begin influencing this model in a negative way? Would companies form to influence the convergence of web 2.0? Would there be enough good people out there that think otherwise to counter act these potential malicious organizations? Wasn’t there a story out there in the cosmos about companies that wanted to redefine the definitions in Wiki? WerenÂ’t they successful? How do influences like this affect this model?
Hi Andrew,
In our projects (for example the introduction of an intranet workspace) we use implicit data to manage features. The more people using a feature the more attention it deserves. When only a small number of people are using it you can try to boost usage in a number of ways. Another way to use implicit data is as an implementation guide. Places in organizations that are using the platform or specific features more or less than others can need attention.
I think this is common for internetsites and should be common as well for internal use of webbased platforms!
Robbert
It’s the tie of implicit in real-time (or near-time) that adds the value in modern systems. We’ve had implicit data lying around for years, which are looked at purely in hindsight, and perhaps never acted on once an analysis has been done.
For emergence to really come about, tighter feedback loops are the key, and a healthy mix of both easily collected (and useful) implicit data, low-hanging (and again, useful) explicitly provided tagging, voting, rating, etc., along with more overt feedback (lengthy Amazon.com reviews for example, or thoughtful blog commentary), adds up to quite a bit of fire power, both for algorithmic improvement, and human oversight.
So I’ll echo Rex on this one, and I hear a distinct KM background rumbling around, where these distinctions between the implicit and explicit have been discussed for some time.
We’re calling specific attention to this particular coin in the Enterprise 2.0 training module I’m finishing up right now. Timing is everything, eh?
I enjoy this blog and wonder how can i join your Blog team
can you answer please
Isn’t implicit information harder to quantify, and also rely on the impartiality of people. Personally I value explicit content more, it is more tangible and quantifiable.
“Google makes money off of charging companies for a higher ranking on their search engine. That is their business model and it works for them.”
Julie, for starters it must be clarified that Google and other SEs do not “charge” for ranking. Rather, they charge for advertising in a weighted bid-model that is separately placed alongside their “natural” results, which are determined by as Andrew noted, their proprietary Page Rank Algorithm .
“The nodes know nothing. The nodes know all. Both are true.”
Indeed this is a beautifully summarized idea of the complex dichotomy that is group speak collected in data form. Inherently as Google’s PR algo attempted to utilize was the relationship between implicit data in the form of links or “votes” asserting the relative authority of a given piece of information.
However, as Julie mentioned the practice of optimization for the sole purpose of influencing page rank is a form of explicit user-generated information masquerading as implicit. What’s most interesting is the fact that Google and other databases have “learned” for way to discount these thinly veiled attempts at manipulating the realm of collective intelligence (see: Consumer Electronic’s post above)and in most cases offers information “vital” (to use a term from Google’s own leaked doc) to the users original search query.
In addition, the user who finds you site via a query in Google inadvertently provides you with implicit information on the language of the common user. By examining this data we as organizations have an amazingly powerful glimpse of the collective intelligence of our audience. This user-generated information is the focus group of the 2.0 world. This implicit information allows for more responsiveness to address the specific needs of employees, colleagues, and/or customers.
Very thoughtful and engaging article, Andrew! it’s nice to sometimes take a step away from scrutinizing data/information and view the entire model of emergence from above.
concentrating on implicit information is not as much harder as people think. We already have softwares for tracking mouse cursor of user and then comes personalizations. But implicit is related to explicit in one or other way. Lets say if a person comment on a blog, he is reading or visiting other sites too of the same nature. Thats why we can safely assume that implicit or explicit can be related to each other.
“#
This is a great discussion topic. I am in sales in the eLearning space and more and more of my customers are looking towards this particular topic to augment eLearning and other training materials. By leveraging explicit and implicit data folks within training and education communities would be looking at a completely new way of learning. Instructional design philosophies could be impacted significantly when you take these options into consideration. It is my thought that this concept can and will change education/training forever.
In reading this blog, I learned the real differences between implicit and explicit data collection and why they are both very important. In my opinion, you must have both. Many people, like myself, think about specific topics but would never think to write about them. Many people are very intimidated by putting anything in writing especially if they donÂ’t consider themselves the expert in that particular topic. They would never think to publish it on a blog or some other form of media however, those same folks could be compelled to comment on, revisit or share information they read about. Implicit data then becomes the silent personÂ’s way of contributing without risk.
My fear within this web 2.0 realm is always the potential for abuse and misinformation. I donÂ’t worry too much about abuse or misinformation within an enterprise, but I do wonder more about the potential for corruption or abuse within the public domain. Google makes money off of charging companies for a higher ranking on their search engine. That is their business model and it works for them. What if it is not about money? Will truth prevail? What would happen if a company, political party or religion wanted to influence data within the web 2.0 realm so that their information is it the highest ranked/rated? Is there potential for these ghost organizations to appear and begin influencing this model in a negative way? Would companies form to influence the convergence of web 2.0? Would there be enough good people out there that think otherwise to counter act these potential malicious organizations? WasnÂ’t there a story out there in the cosmos about companies that wanted to redefine the definitions in Wiki? WerenÂ’t they successful? How do influences like this affect this model?
Posted by Julie White on 03/17 at 06:47 PM”
As well as me same opinion
Very good article I myself have learned alot from the bloggers and of course Andrew, Im on the side of internet usage i think the internet has become a phenomenal experiment that works
Nice post. thanks for sharing. I like the way you share the ideas.
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Hi,
Nice work, thanks again for sharing such an informative ideas.
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