Data Visualization Lead at NGRAIN

It’s worth announcing that last month I took a position as Data Visualization Lead at NGRAIN in Vancouver, Canada.

NGRAIN’s vision is to see beyond reality, and to help people accelerate decisions by interacting with the world’s data in 3D. To make that concrete, we are currently developing cutting edge Augmented Reality applications for the industrial enterprise. 3D data visualization is notoriously difficult to execute, but in Augmented Reality we will no longer have a choice but to confront it, as our reality is 3D. Bringing my expertise to this problem will be an exciting challenge and will require great care.

What you see here will continue to be my own views and not those of NGRAIN. I will, however, likely be publishing on NGRAIN’s blog and cross-promoting here.

Visual analytics, democratization of analysis, and visual literacy

Regarding: SAP Visual Intelligence: Big Data Example on YouTube

First I had better say the positive things. I appreciate that this is a feature demonstration and not a case study. The presenter goes through the feature set of their product in an admirable and straightforward way. It is an impressive feature set and an impressive product. They are not trying to tell a story, so it is unfair to criticize their story.

Then I can say the “but”. But! I was struck by the badness of the visualizations. ALL of them! Some highlights are below, but you can watch the video for yourself and see not one well purposed chart.

Sales and quantity sold by product line and quarter at 2:12

sap_video_1

Sales and quantity sold by product line and quarter at 3:03

 

sap_video_4

3D Bar Pies (is that even a thing?) at 3:33

 

sap_video_5

Variably-sized pies on a map at 3:57

sap_video_6

Sales revenue by city by quarter at 4:52

sap_video_7

Sales revenue by city at 6:27

sap_video_8

The Visual Analytics movement seeks to democratize data analysis by making it easy to use and highly visual. They want to push analysis up towards senior management and outwards away from the analysts. Which is a laudable goal, but when we get there and people are creating stuff like we see in this video, we will have failed.

This illustrates two major calls to action:

  1. Designers: How do you design a Visual Analytics product that guides its users to make good data viz design decisions?
  2. Trainers: Go out there and spread the good word. Time to get Andy Kirk in to train your organization!

 

 

Joys of R

I’ve been coding a lot in R lately. Just thought I’d share one example of the insanity.

myList = list(“a”,”b”)
myList[1] == “a”
class(myList[1]) == class(“a”)
myList[[1]] == “a”
class(myList[[1]]) == class(“a”)
class(myList)
class(myList[1])
class(myList[[1]])
class(“a”)

Returns…

> myList = list(“a”,”b”)
> myList[1] == “a”
[1] TRUE
> class(myList[1]) == class(“a”)
[1] FALSE
> myList[[1]] == “a”
[1] TRUE
> class(myList[[1]]) == class(“a”)
[1] TRUE
> class(myList)
[1] “list”
> class(myList[1])
[1] “list”
> class(myList[[1]])
[1] “character”
> class(“a”)
[1] “character”

Generally weird but reasonable, except  that myList[1] == “a” is TRUE even though they are not of the same class.