From bureaucracy to agility

Last year, I referred to a post by Olivier Amprimo, who was then at Headshift. He is now working at the National Library Board in Singapore, and is still sharing really interesting thoughts. The latest is a presentation he gave to the Information and Knowledge Management Society in Singapore on “The Adaptation of Organisations to a Knowledge Economy and the Contribution of Social Computing“. I have embedded it below.

For me, the interesting facet of what Olivier describes is the transition from bureaucratic organisations to agile ones, and what that means for KM. Traditional KM reflects what Olivier isolates in the bureaucratic organisation, especially the problem he describes as the confusion between administrative work and intellectual work. In doing traditional KM (repositories of knowledge, backed up with metrics based on volume) we run the risk that administrative work is enshrined as the only work of value. However, it is the intellectual work where agility can be generated, and where real value resides.

Olivier describes the agile organisation as one where the focus is on rationalisation of design.

What is important is how the individual forms and is conditioned by work. The work is the facilitator. This is the first time that the individual has been in this position. This is where the knowledge economy really starts.

I found an example of the kind of agility that Olivier refers to in an unexpected place: a short account of the work of Jeff Jonas, who is the chief scientist of IBM’s Entity Analytics group. His work with data means that he is an expert in manipulating it and getting answers to security-related questions for governmental agencies and Las Vegas casinos. For example, he describes how he discussed data needs with a US intelligence analyst:

“What do you wish you could have if you could have anything?” Jonas asked her. Answers to my questions faster, she said. “It sounds reasonable,” Jonas told the audience, “but then I realized it was insane.” Insane, because “What if the question was not a smart question today, but it’s a smart question on Thursday?” Jonas says.

The point is, we cannot assume that data needed to answer the query existed and been recorded before the query was asked. In other words, it’s a timing problem.

Jonas works with data and technology, but what he says resonates for people too. When we store documents and information in big repositories and point search engines at them, we don’t create the possibility of intelligent knowledge use. The only thing we get is faster access to old (and possibly dead) information.

According to Jonas, organizations need to be asking questions constantly if they want to get smarter. If you don’t query your data and test your previous assumptions with each new piece of data that you get, then you’re not getting smarter.

Jonas related an example of a financial scam at a bank. An outside perpetrator is arrested, but investigators suspect he may have been working with somebody inside the bank. Six months later, one of the employees changes their home address in payroll system to the same address as in the case. How would they know that occurred, Jonas asked. “They wouldn’t know. There’s not a company out there that would have known, unless they’re playing the game of data finds data and the relevance finds the user.”

This led Jonas to expound his first principle. “If you do not treat new data in your enterprise as part of a question, you will never know the patterns, unless someone asks.”

Constantly asking questions and evaluating new pieces of data can help an organization overcome what Jonas calls enterprise amnesia. “The smartest your organization can be is the net sum of its perceptions,” Jonas told COMMON attendees.

And:

Getting smarter by asking questions with every new piece of data is the same as putting a picture puzzle together, Jonas said. This is something that Jonas calls persistent context. “You find one piece that’s simply blades of grass, but this is the piece that connects the windmill scene to the alligator scene,” he says. “Without this one piece that you asked about, you’d have no way of knowing these two scenes are connected.”

Sometimes, new pieces reverse earlier assertions. “The moment you process a new transaction (a new puzzle piece) it has the chance of changing the shape of the puzzle, and right before you go to the next piece, you ask yourself, ‘Did I learn something that matters?'” he asks. “The smartest your organization is going to be is considering the importance right when the data is being stitched together.”

Very like humans, then? A characteristic of what we do in making sense of the world around us is drawing analogies between events and situations: finding matching patterns. This can only be done if we have a constant awareness of what we already know coupled with a desire to use new information to create a new perspective on that. That sounds like an intellectual exercise to me.