Do you know all you need to know about the people of web analytics? Probably – but I’m here to share more anyway.
Today, it’s time to talk to Michael D Healy (can’t believe I forgot to ask him what the D stands for).
Michael, tell us something about yourself – include something about baseball that I won’t understand.
When I was a just a kid at a baseball game, I tried to figure out the exchange rate should the team at bat want to return a run off the board in exchange for the team on the field returning an out. Now we could potentially calculate this meaningless metric, if someone asked for it.
The method by which people make decisions and communicate the different phases of decision making is what I am working in as a web analytics consultant in the San Francisco Bay Area. Clients sometimes really want to know what the trade-off between outs and runs might be . . .
My wife works here in SF as well; we live in an urban environment bordering several very awesome parks which are somehow largely devoid of transients. Our daughter enjoys those parks almost as much as ‘Yo Gabba Gabba!‘ . . . DJ Lance Rock email me if you need some help dude!
We don’t cook as much as everyone else in the community apparently does, a walkscore of 100 gets us up and out too often to be in the kitchen cooking this and that. If you are coming to eMetrics in SF here are a few of my recommendations:
- The Monk’s Kettle – Aweseome, but BUSY!
- La Meizi – Spicy Sichuan Cuisine, only for those who like spicy food
- The French Laundry – Hope you have your reservations already
What book are you reading at the moment?
After my Analysis Exchange project with the New Zealand Drug Foundation, an org dedicated to ‘reducing the harmful effects of drugs based on the best evidence possible’, I started re-reading ‘Drug War Heresies.’
Did you know they served cocaine laced wine in the White House, the Vatican and Thomas Edison’s house? Vin Mariani.
If web analytics was a country, which one would it be and why?
Lots of sophisticated foodies who can appreciate a range of human expression. On the other hand, we are at times depending on an intellectual Maginot Line to ensure our continued survival.
You are a mentor in the Analysis Exchange – is that because you think you’re the best thing since segmented wheat products?
Hmmmmmm . . . wheat products.
Taking care of the people who take care of you, and finding time to work in some people who just need your help, seems like a pretty good way to go about your business.
Who is your superhero-sans-cape in the web analytics community and why?
Since I already nominated ETP for the community web analytics award of the year, my answer is June Dershewitz.
She is someone who is consistent in her attention to, and always actively looking out for, the interests of web analysts near and far. Doing that while at the same time keeping everything laid back is a pretty cool quality.
If you knew the real reason behind the chicken crossing the road, who is the first person you’d tell?
Chick-fil-A. They could catch them all, produce more tasty chicken sandwiches and then expand their operations.
What is your wish for 2011?
My wife to approve the red one for our (almost) one year old daughter.
Complete the sentence: “I am a web analytics nerd because…”.
The Analytics Scroll is blank.
No, that’s not from damn Kung Fu Panda . . . originally.
You win 2 free passes to eMetrics – to whom do you give the other pass?
Hold a contest for needy attendees where they submit:
1) 100 words why they would like to go
2) 100 words describing a problem, inputs, and analysis
3) One slick chart, graph or whatever illustrating that problem or a solution
Pick the winner based on individual awesome-ness and potential to learn at eMetrics.
Is online privacy a myth? Do you care?
I care about transparency in tracking, we should expect and demand honesty to the degree it is possible. If we are being tracked, disclose it.
The history of privacy is, itself, quite interesting to me:
- President Jackson being hauled before the Supreme Court ex parte to stop the Post Office from regularly opening the mail
- The 1928 Olmstead decision allowing wiretaps and the 1967 Katz decision, which reversed Olmstead
- The Pond post WWII spying
- The FBI’s COINTELPRO activities
- Plausible deniability in ECHELON
- NSA program after 9/11
- President’s Surveillance Program data mining everything they can
- Building a hub for monitoring in San Francisco, ignoring that a technician may need to fix a wall socket at some point
The saddest development in large scale information collection by governments is, apparently, that this is another area where America is being overtaken by China.
I call on the US Government to re-double their tracking efforts, with mobile technology we are really making it so easy for you. Build a giant Hadoop Cluster somewhere in the middle of the US and just start filling it up. Put the empty missile silos from the Cold War back to use!
Remember when America was the land of dreaming the impossible, and then achieving that dream? We could be there again, Hadoop Cluster of mobile data in 2011!
What do you tell someone who asks you if a stand-alone metric is “bad” if it’s 37%?
We used analysis of the data with the ROOT framework from CERN, as well as the Trilinos libraries from Sandia National Laboratories, to build, after a probability proportional to size sampling, an iteratively reweighted least squares estimation because R is for n00bs.
Integrating the iteratively reweighted least squares estimation, or IRLSE as we in the business call it, the model is further validated under Fisher’s Method to clearly prove a paradigm which is neither statistically significant, nor has a meaningful magnitude.
The duality of qualitative outcomes, both “good” and “bad”, are required outputs due to model encompassing the universe of possible, and impossible, outcomes.
Please put on your 3-D glasses so you can appreciate the graphical illustration of the model in the image below. In the image, a “good” result starts on the top surface towards the left of the image. As it transverses the model, “good” becomes “bad” in a very real sense.
To answer your other question, it is a little over 1 run to 1 out, 2 balls and a strike exchange rate.