Skip to content

College Professor uses Tried-and-True method for Encouraging Knowledge Sharing

via Slashdot a few weeks ago, and Ars Technica; at the University of Washington-Bothell, Martha Groom recently assigned her students to work on Wikipedia entries, and add to the knowledge base. An interesting approach; I found the reaction of the Wikipedia community most interesting, in that the entries were aggressively edited and commented upon – sometimes “rudely”.

It’s a common theme in many KM discussions, as early adopters enter their first Trough of Disillusionment, and see these wonderful tools languish from lack of use. Just this afternoon, I got into an impromptu meebo conversation with someone looking to implement a knowledge base for an international help desk …

jpmacl: how will you get the team to contribute their solutions?
meeboguest: that’s easy – I’m the manager, and I will make them

I sensed a type-A personality at the other end, but there is method to this madness. The Law of Large Numbers tells you that you can’t rely on idealism – in a typical working group / business organization, there aren’t enough self-motivated contributors to generate meaningful content

Some incentive is typically necessary to get folks to contribute to the knowledge base. One approach I’ve used in the past is simply requiring a certain number of wiki entries per person on the team – nothing too outrageous, averaging out to one or two entries per month. If you try this approach, I would strongly encourage folks to make their entries once every couple of weeks; this gets you into the habit of using the tool. Don’t put it all off to a marathon data entry session right before the end of the year.

But don’t stop there! Contributing knowledge is half the battle – you must develop the team’s habit of using the knowledge base.

Years ago, when object-oriented programming was in vogue, development teams in many companies worked to create object libraries for commonly used code. The trick, of course, was to get well-written, reusable code into the library – and then see it re-used. Some sharp-thinking organizations would incent coders when their code was leveraged in another area.

The same might be done with a wiki or other knowledge base, if you can define a metric that quantifies how often a piece of knowledge is reused. For example, how much traffic does the wiki page get? How many times does the article appears in search results? In the help desk scenario above, we could require each trouble ticket to note which articles in the knowledge base helped solve the problem … a simple counter at the end of the month will tell me how often my excellent solution saved the day.

Remember, it’s not enough that people are capturing knowledge – it needs to be captured in a format that is relevant and useful (else, it’s just another bag of bits).

Comments (0)

Leave a Reply

Your email address will not be published. Required fields are marked *

Related Articles
ai readiness assessment

AI Readiness Assessment: What Does Good Look Like?

Most AI readiness assessments measure technology. The AI Readiness Assessment measures your organization - across Five Building Blocks, with honest self-evaluation and real benchmarks.

Read more
unstructured data ai

Unstructured Data and AI: The Knowledge You’ve Been Sitting On

The Data Value Chain was built for structured data. But 80% of what your organization knows is unstructured - and AI just cracked it open. This is the knowledge management breakthrough we've chased for 30 years.

Read more
AI Data Value Chain

AI and the Data Value Chain: Where the Bottleneck Moved

AI compressed the technical middle of the Data Value Chain, shifting the bottleneck to the human bookends - Insight and Present. Most organizations haven't noticed.

Read more
data value chain

The Data Value Chain: Seven Skills That Turn Data Into Decisions

A seven-step framework for turning raw data into business decisions. The Data Value Chain maps the distinct skills required at each stage - and explains why you need a team, not a unicorn.

Read more