One of the reasons I found Customer Insight Leadership such a rewarding career and still find it fascinating to help such leaders, is the diversity. One day your focus may need to be on improving specific Analytics skills, or complex Data problems, the other it could be how to change culture as a leader or summarise key insights from qualitative research. It really is a polymaths’ playground.
I hope this material helps encourage you to broaden your interests and catch a vision of the breadth of knowledge and skills that could aid you as an insight leader
Here is my collection for today…
Beyond Isolated Learning to LifeLong Machine Learning
Let’s start with a technical focus, within the domain of Data Science. Having tried a few different Data Science podcasts, one that I’m finding helpful at the moment is Data Science at Home.
In this episode, host Francesco Gadaleta interviews Brett Zhiyuan Chen a software engineer from Google. Brett has recently co-authored a book “Lifelong Machine Learning” and on this podcast they discuss what that term means and how it relates to other developments in the world of Deep Learning & Data Science. It turns out to be an interesting development in research within this area, as it faces into the challenge of how humans learn compared to machines.
Rather than just optimising to solve one specific problem (using training set data that hopefully has the same distribution as future problems to be solved), humans are capable of generalizing and remembering what they have already learned to help with future questions. Lifelong Machine Learning seeks to develop the same kind of adaptive and continuously improving intelligence.
The conversation in this podcast is a useful, if technical exploration of current progress in developing trust artificial intelligence (with lots of references to alternative approaches and the challenges of using Big Data sources). Worth a listen, as the most technical content for today:
Artificial Intelligence allow machines to learn patterns from data. The way humans learn however is different and more efficient. With Lifelong Machine Learning, machines can learn the way human beings do, faster, and more efficiently…
An infographic to help you choose where to live in the US
Data Visualisation is regularly a topic of interest to our readers, so when I was approached to share an infographic based on converging different information sources, I was naturally keen to take a look.
In this infographic, published by Megan Wells on InvestmentZen blog, she shares a visual summary of the 10 best cities for young professionals based on combining data from:
- Lowest unemployment rate, based on data from US Bureau of Labour
- Highest score for enjoyment of walking around that city, based on a custom ‘walkability score‘ developed by RedFin real estate marketplace
- Most affordable average rents, based on a Rent Index score created by Zillow real estate marketplace (click link for interesting methodology explanation from their data scientist)
- Highest average wages, based on mean wage data from the US Bureau of Labour
Here is a link to the visual summary of the resulting top 10 cities in a very simple infographic:
Many young professionals are eager to begin a financially independent lifestyle. With a new paycheck, saving, spending and lifestyle habits are likely to evolve. For most people starting off a new career, it’s important to have discretionary income for enjoying life outside of work.
Despite that infographic being very basic and including the dreaded pie chart graphic, I thought it worth sharing as an example for two reasons:
- It’s very simplicity shows that value can be added just by converging data from multiple sources to share with your audience (even before insight generation)
- The transparency of methodology is laudable and simply described, an example of truly democratizing data, rather than leading audience to follow your biases
Plus, congratulations to Des Moines. Would anyone from that fair city agree that it’s a worthy winner?
How to ensure your delegation is not abdication
For my leadership reading today, I turned again to Michael Hyatt’s perennially relevant ‘Virtual Mentor‘ blog. In this post, he deals with the mistake of delegating a task and then forgetting about it, only to later discover you just abdicated a responsibility.
How can you tell the difference between avoiding being a micro-manager, to empower your analysts, verses abdicating your responsibilities as The Boss? In three practical steps, Michael highlights the issue and identifies how you could delegates more effectively. It is a worthwhile investment to take more care while delegating, so that person really is enabled to deliver what is needed and make a positive difference for both of you.
Here’s Michael’s diagnosis and recommendations:
3 Reasons Your Leadership Doesn’t Get the Results You Want Tell me you’ve had this experience. You assign a task but then forget about it. I sure have. As a leader, I am not a micromanager. That’s good news for my team. But I have to be intentional that delegation doesn’t drift into abdication.
What are you reading as topics for insight leaders?
I hope those three topics for insight leaders were interesting and helped you with different aspects of your own CPD.
It would be great to hear from you, our readers, on any content you’ve found helpful. Whether online or offline, feel free to comment below or recommend on social media. I’ll then select the most popular and share then with our community in a future post.
Now, I’m off to see my family and enjoy the rest of this Bank Holiday! Keep enjoying your development & work/life balance.