This week we take a look at data quality, testing, and management lore.
I don't have many moose photos so this will just have to do.
The topics in this issue of the newsletter will be wordplay on familiar song titles, lyrics, sayings, and connect back to the music industry legend of brown M&M’s, the automotive industry Moose Test, and a management paradox. So, stay with me because it’s not as random as it might sound.
Next, let’s take a quick look at a prediction reinforcement sighting from a prior Fudge Sunday.
One fun aspect of writing a newsletter is calling back to a prior issue. For example, astute readers of Fudge Sunday may have noted a cheap as chips production prediction in issue #44.
Alicia Garcia-Herrero is Senior Fellow at European think-tank BRUEGEL and Chief Economist for Asia Pacific at Natixis who recently made the argument that the current chip supply chain could go from scarcity an over abundance.
Now, let’s take a look at data quality, testing, and management lore.
Papa's got a brand new bag of M&M's
The Great Data Debate: DAMA UK's Data Quality Dimensions (awful audio version)
One of the interesting elements from the world of data science is the notion of data quality. Almost 10 years ago the so-called six attributes were captured in works from the DAMA UK in the Great Data Debate at the release of DAMA UK White Paper on Data Quality Dimensions which introduced (alphabetically):
But what is… accuracy and even if accurate is completeness complete?
Barr Moses is founder and CEO of Monte Carlo. In The Non-Engineer’s Guide to Bad Data she highlights “75 % of executives don’t trust their data”. That made me wonder if a company that specializes in Brown M&M’s as a Service (BMMaaS) that will be the next decacorn.
If “75% of executives don’t trust their data” then you can bet that 100% of the Van Halen production team in the late 1970s and 1980s didn’t trust 100% of the venue promoters to read the rider.
(Warning: This link includes a David Lee Roth YouTube video that contains NSFW language)
Of Moose and Poka-yoke
If you’ve driven across the rural USA in places closer to agriculture you might have seen cattle crossing signs or warnings horses and such. The next sign you’ll see if you keep driving towards nature is deer crossing, and in the North will likely be a very big deer crossing sign – Moose crossing sign (aka Alces americanus).
Of course, warning signs are not the same as a behavior shaping constraint. So, could there be a mitigation or way to assist when encountering a sudden obstacle?
After reading this far, the notion of ISO - ISO 3888-2:2011 just crossed my mind aka the Moose Test. So, perhaps there is a market for BMMaaS with an included Moose Test as well to help navigate safely around the data issues?
Oh Abilene, why can't you be true
I scream. You scream. We all scream for Abilene ice cream.
When I was just starting out in my career, I attended training where I learned about the perils of hot Texas weather, random suggestions, the assumed popularity of ice cream, groupthink, and the compounding effects of failures in effective planning.
In the Abilene paradox, a group of people collectively decide on a course of action that is counter to the preferences of many or all of the individuals in the group.
The Abilene Paradox (1984)
If that was a treat – you are in luck. YouTube has a collection of yarns and stagecraft from Jerry B. Harvey.
Here’s one more for the proverbial road…
~2 hours of artful and educational storytelling. Enjoy!
Dr. Jerry B. Harvey delivers 2 hours of insightful management yarns (1981)
In summary, when thinking about exponential data growth and the growing importance of that data, there is much more to consider. Perhaps one thing we need to consider is combining BMMaaS with an overlay of the Moose Test to move forward — all while acknowledging that there are those around us with boredom, temerity, conformity, and deadlines – including ourselves.
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