Future of Work, Sense Making

Half life of information and necessary rate of learning

I’ve just recently completed a series of certifications and learning modules for work. This is in addition to the training we have to do every year. It’s a lot of learning and pretty challenging alongside your day job.

That’s the point. It’s part of Microsoft’s (where I work) emphasis on a growth mindset since it focuses on the way you relate your sense of self to a challenge. My sense of self has risen immeasurably since taking on the challenge I can tell you and I am relishing it even more as I move forward.

The growth mindset theory popularised by Carol Dweck encompasses many aspects in addition to how you tackle challenges. One of them is the belief that your abilities can be developed, through learning for example (as long as you have a growth mindset).

Continuous learning is especially critical in an age of tech intensity which Microsoft CEO Satya Nadella also believes we are in, as I wrote about here: Tech Intensity and the Adaptive Organisation.

Learning refresh cycles

So coming full circle to my doodle. If continuous learning is the order of the day, how long before your past learning becomes redundant?

I caught myself in self congratulatory mode after finishing my recent certification thinking I couldn’t get too comfortable in the knowledge my learning was done for that subject.

A great piece on the theory of the half life of information ponders what the current rate of decay of knowledge is.

With my recent certification which was on Azure Fundamentals, I was pretty sure that things were changing so fast, I’d have to relearn things in less than a year.

My doodle posits that a year is the average time it takes for knowledge to be made redundant and then new learning needs to kick in. Of course this will vary by subject and industry.

What do you think?

As a Service, Customer Success, Sense Making

The product customer success cycle

This DanelDoodle pretty much speaks for itself but just a few added notes. The feedback loop is the critical element for success (aside from the obvious one – the customer/user being at the centre of everything).

A good feedback loop is not an easy thing to build so the simplicity of the diagram belies the effort. Feedback loops should incorporate many things, the most impart being, in summary:

  1. A good reporting interface into how customers and users are using the product that both product development and customer success teams have access to and share insights from in terms of how outcomes can be improved. This should include both quantitative data as well as qualitative, e.g. survey responses.
  2. A feedback loop between customer success teams and product development teams where the former bring field insights to the latter and these influence new feature development. Conversely, new feature ideas can be shared by product development teams and discussed with customer success teams before they are developed further. A good collaboration system will help with this.
  3. A similar reporting interface as above for the customer (those responsible for end users) so they gain insights into how the product/s are being used. This should include an element that allows the customer to build their own reports and feedback loops which I have hacked solutions around (covered here and here).
Sense Making

Internet Trends and the impact on As a Service

Mary Meeker is famous for the insight of her Internet Trend Reports so of course I read them. Two slides stood out in relation to the trend I am tracking and the report I am working on. I captured my views with some annotations.