No Such Thing as Scientific Debt.
What is technical debt and why the metaphor cannot be extended to data science.
How to Organize the Citizen Data Scientists
Leverage citizen data scientists to be efficient in their primary roles while promoting a data informed culture using Kotter’s dual operating system.
How NOT To Lie With Statistics “On Average”
If you drove to work at 20 mph and returned from work at 30 mph, what was your average speed for the round trip? If you answered 25 mph, read on to learn why that is incorrect.
Data Literacy and the Japanese Way of Tea
Data literacy is a pre-requisite for a data-informed culture. Here is how you can develop a data literacy program using the Dreyfus model of skill acquisition and the concept of Shu Ha Ri for a diverse and evolving workforce.
Importance of Pursuing Ignorance in Data Science
Why do 80% of data science projects fail? Why is pursuit of ignorance critical for their success? Why “bad” data is actually an asset? How can the Johari Window help?
When NOT to use Data Visualization
To visualize or not to visualize data? When are tables better than graphs? What is cognitive fit and why it matters?
Trough of Disillusionment and the Pythagorean Cup
This post extends the application of the Hype Cycle to an organization’s data and analytics initiatives by integrating a data maturity model into it. It also visualizes the drastic onset of negative hype using pythagorean cup.
Data Literacy: Using the Socratic Method
According to Gartner, by next year, 50% of organizations will lack sufficient AI and data literacy skills to achieve business value. Here is how you can apply the time-tested method of Socratic Questioning to promote Data Literacy.
Good Strategic Dashboard, Bad Strategic Dashboard
Adapting Richard Rumelt’s model of a good strategy to discuss the elements of a good strategic dashboard. An alternative to the audience centered approach to dashboard design… a strategy centered approach.
The Data Thinker
“The essence of the independent mind lies not in what it thinks, but in how it thinks.” – Christopher Hitchens.
Here is why organizations need data thinkers.