I get asked often to define this term of Collaborative Intelligence that we started using a couple of years ago. As I’ve said on my blog, I am a little wary of labels and categories. So, I’ll simply convey what I see converging in the real world: Collaborative Intelligence is a set of technologies and techniques helping groups of people apply their collective intelligence toward making good decisions together.
It’s important to remember that every hour of every day at work, every employee is making decisions. If you are a knowledge worker – actually, even if you hammer rivets or crush rock or solder wires for a paycheck – then the decisions you make are (hopefully) dependent upon something like the following Decision Stack:
- Data: the set of available facts
- Model: a theory or algorithm about how things work
- Aspiration: preferences, desires, or goals and their rationale
- Compromise: tradeoffs and diplomacy among competing interests
Every little decision we make each day involves this stack. Sometimes certain pieces of the stack are flawed; sometimes they are subconscious; but, all decisions involve these things. And, every day, all day, we are doing this at work. “Collaborative Intelligence” is just a short-hand label for what we see when we are working together to make better-informed decisions. So, it’s about how groups of people (2-n) explore data, models, aspirations, and compromises to help make things better.
Collaborative Intelligence is not just some new features added to Business Intelligence. Business Intelligence (BI) is a field of technology that has made great contributions over the last 30 years in the preparation, storage, retrieval, and distribution of facts. To a meaningful but lesser extent, it has also made contributions in the area of developing explanatory and predictive models.
Unfortunately, mainstream BI has gotten stuck in a corner in the above diagram, relying on technologies and techniques that are fundamentally centrally-driven, static, data-focused, and impersonal. This has opened a great door for more grass-roots, participative, people-centric, adaptive, edgy, conversational, and diplomatic tools and techniques, like what we see evolving in the Enterprise 2.0 space. The grass-roots conversations and activity streams that are the stock-in-trade of Enterprise 2.0 platforms are full of information-scent, Pirolli’s concept of little hints about hidden-but-interesting things. So, when using those platforms one feels naturally drawn into conversations and topics and investigations, within which one can share additional facts, models, aspirations, and compromises.
So far, the Enterprise 2.0 tools have not addressed the important BI territory of Data and Models; but, it is only a matter of time until they realize that collaboration without Data and Models is just idle chatter. It is up to the BI community to realize at the same time, however, that People are essential to the process of applying data to make things better. I have no doubt we will see the tools and techniques of BI expand along the lines indicated by the arrows in the diagram above, just as the tools and techniques of Enterprise 2.0 expand in the opposite direction. This convergence will be wonderful for users.
This is what we are working on at Lyzasoft: helping people find the information and experts they need to make better decisions.
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