Source – Journalism Code, Context & Community
Source: What We’ve Learned About Sharing Our Data Analysis – Features – Source: An OpenNews project
Good reference on ensuring transparency in data analysis
Category Archives: Analytics
Foot Traffic Ahead | Smart Growth America
Great piece of research out of GWU on walkability of cities. I particularly find interesting the correlation between higher walkability and higher GDP. In many ways it makes sense, but also wonder if it’s not just a product of overall density first (i.e. density is caused by lots of people/businesses being close together – thus higher GDP), and as a result these are more walkable cities. I recognize the fallacy in my point, in such that two dense areas are not the same from the stand point of walkability.
Foot Traffic Ahead | Smart Growth America.
Six Myths about Data-Driven Design | UX Magazine
Very much appreciate the effort from this article. The myths (especially 2-6), resonate very strongly with me, and something that I’ve seen over and over in management consulting, client side management, and ad agencies alike. It’s really time that we move beyond silos of comprehension, and towards a more holistic view of how data can support design and planning.
Six Myths about Data-Driven Design | UX Magazine.
Machine-Learning Maestro Michael Jordan on the Delusions of Big Data and Other Huge Engineering Efforts – IEEE Spectrum
Interesting interview with Michael Jordan (no, not that Michael Jordan) about the the challenges with Big Data. It reminds me of a conversation that I had with a good friend of mine just the other day. We discussed how Big Data is meaningless in the absence of context. Likewise, Michael Jordan’s point touches on the other side of the issues with Big Data and BD Analytics in such that meaning can be obtained from spurious connections, and that confidence can be over assumed.
A quote:
“If I have no principles, and I build thousands of bridges without any actual science, lots of them will fall down, and great disasters will occur.
Similarly here, if people use data and inferences they can make with the data without any concern about error bars, about heterogeneity, about noisy data, about the sampling pattern, about all the kinds of things that you have to be serious about if you’re an engineer and a statistician—then you will make lots of predictions, and there’s a good chance that you will occasionally solve some real interesting problems. But you will occasionally have some disastrously bad decisions. And you won’t know the difference a priori. You will just produce these outputs and hope for the best.”
The Rebirth of the CMO – Peter Dahlstrom, Chris Davis, Fabian Hieronimus, and Marc Singer – Harvard Business Review
Fascinating article from HBR on the role of the CMO. This is something that I keep on harping about. Now more than ever the CMO is at the center of the organization, and really drives it.