Friday, June 30, 2017

Books Series Blog Challenge and Experiment

For the month of July, I challenge myself to blog every day with a series of posts around Knowledge Management books.  Rather than come up with a list of top recommended readings in KM, I'm simply going to pick books on my bookshelves (hard copies and electronic). I anticipate that each, regardless of how much of a classic (or not) it is, will be a trigger for some valuable reflections.  On top of that, I'm hoping to gain some insights into learning through aggregation, reflection and synthesis.  Here's the question I'm asking myself: Can you get more out of 30-40 books by revisiting them all at once compared to what you get by reading them over a decade?

At the end of the month, I will construct a synthesis knowledge map based on these reflections, highlighting key insights.  I don't know what it will look like.  I don't even know if the exercise is worthwhile.  My final post in the series will be precisely about that.  What was useful about the exercise?  What didn't work well?  What did I learn from this challenge?  Would I recommend it to others?

I don't want to put too much thought into the order in which I will pick the books but one stands out in my mind and I will start the series with it on July 1.

Learn or Die: Using Science to Build a Leading-Edge Learning Organization, by Edward D. Hess.

Friday, June 23, 2017

Metrics Anecdote

Here is a short, very simple fictional (adapted from reality) anecdote about metrics in the realm of knowledge management.

"Barbara, how many workshops have we done in the past 8 years?"
"Let me check.... 25."
"We've done 25 workshops in the past 8 years?"

And the number 25, whatever it means, is now a data point for someone in management who needed to know how many workshops we have implemented.  Someone needed numbers.  To say "we conduct knowledge sharing workshops" isn't the same as "we have conducted 25 knowledge sharing workshops in the past 8 years."  And yet, that number is meaningless.  Here's why.

"Barbara, how many workshops have we done in the past 8 years?"
"Probably more than 20. Why do you ask?"
"I need to document our activities with some quantitative metrics."
"Oh, then the total number of workshops isn't that relevant or useful because we used to do full day workshops and now we do half-day workshops and sometimes they're just 90-minute workshops, so workshops aren't all the same."
"Is there a better way to show that we have done a lot of good work with these workshops?"
"How about counting the individual sessions rather than the full workshops?  In those 20+ workshops, we've addressed 115 distinct topics in sessions lasting anywhere between 30 minutes to 2 hours."
"Thanks.  That's useful."
"You're welcome.  We also have attendance data and ....."
"Thanks.  I think the number of topics is good enough for now."
"Well... it would be nice to know in advance what metrics are of interest to management.  I keep metrics that are relevant to me for the purpose of improving the workshops."


  • When asked for metrics, understand the rationale for the request.  Ask questions (without sounding defensive or overprotective of data).
  • Avoid surprise requests for data by pro-actively engaging management in the determination of valuable metrics to be collected.
  • Don't just answer a question with data that you know is going to be misleading without further explanation, but do make an effort to be responsive and answer the question honestly. 

Tuesday, June 20, 2017

Stalling at the Top of the Learning Curve

I like the concept of the learning curve and in the context of a conversation about learning to learn, being aware of our individual learning curve around a particular topic can be valuable.

For example, I feel as if I have reached a point on the learning curve around this theme of "learning to learn" where the returns have become minimal.  I read these types of articles (previous posts) but I've come to the point where it's really a scanning process rather than deep reading.  I'm looking for something new, a new concept, a new idea, and 90% of the time, I'm a little disappointed because it's not new (to me).  It's almost become boring.

What should/could I do about it?

1. Look at it with different lenses
It would be arrogant to think that I know everything there is to know about "learning to learn".  Let's assume for a moment that I've been looking in the wrong places for additional knowledge.  I need a new direction, a new angle.  Perhaps I should revisit another recurring interest, neuroscience, and see if there are useful connections with "learning to learn" that I have yet to explore.

2. Look away for a while
This is just a symptom of being bored with too much of the same thing.  I just need a break from "learning to learn."  I should consciously avoid the topic for a while (perhaps a year) and then get back to it with fresh eyes.  For example, my recent interest in permaculture has created a nice break from standard knowledge management and related topics which I constantly read about.

Monday, June 19, 2017

Learning to Learn - what's new?

A recurring theme:  Learning to Learn.

  • "Talking to Yourself (Out Loud) Can Help You Learn," by Ulrich Boser, May 05, 2017.
  • "If You're Not Outside Your Comfort Zone, You Won't Learn Anything," by Andy Molinsky, July 29, 2016.
  • "Learning to Learn," by Erika Anderson, March 2016.
  • "You Can Learn and Get Work Done at the Same Time," by Liane Davey, January 11, 2016.
  • "4 Ways to Become a Better Learner," by Monique Valcour, December 31, 2015. 
All in the Harvard Business Review.  I don't care how much AI and machine learning are going to transform our world, I'm ready to bet that learning to learn with our own little human brains is never going to be obsolete. In fact, critical thinking and rapid learning are going to be at a premium.

Too funny:  As I was ready to publish this little item, I came across the following:

  • "In the AI Age, "Being Smart" Will Mean Something Completely Different," by Ed Hess, June 19, 2017, Harvard Business Review.