Create Solutions From Challenges
Digital Measurement Professional Outlook
At eMetrics NY I was on a panel with Chris Berry of Syncapse moderated by Gary Angel wherein Chris made the distinction between
- Sentiment Analysis and
- Opinion Mining or Topic Classification
During our introductions prior to the distinction made by Chris I had conctiously chosen to simplify the discussion and discuss Topic Classification under the broad scope of Sentiment Analysis.
Chris’ distinction moved the conversation into a far more productive direction about how the usage of the two methods are very different.
The Business Case For Over-Simplification
The tweet by @AndrewJanis, with responses and re-tweets, summarizes why I chose over-simplification:
I am reminded of the person who watches a baseball game, and when they see a home run somehow they aren’t impressed.
When people don’t know the complexity of what they don’t know, those unknowns sure look easy.
People and Skill Classification
The three basic buckets of skills needed for digital measurement are:
- A Huge Bucket Including Math, Stats, Econ and Much More
- The Ability to Operationalize Automation in a Timely Fashion
- The Understanding of Business Operations and Optimization
Hire 58% Not 50%
Consider the three individuals sourced to make up those buckets above, what would an overlay of those three skill sets above look like for Quantitative, Technology and Business people?
Glad you asked
For the purposes of this overly generalized generalization, people are in columns and skill sets are in rows. Line up each specific person type with each skill bucket set for my completely unscientific estimation of the skill capacity.
Quantitative and Technology people have to cross train just to get things done, on top of which they all have at least some understanding of business skills.
The Business Person just may be the odd duck out.
Aside from those specific exceptions, MIT Business School I’m looking at you, Business People have a brief introduction to quantitative skills and would largely be self-taught in technology skills.
Why in the world does this matter?
The Future of Digital Optimization is Irreducibly Complex
During the same Sentiment Analysis panel I stayed far away from discussed specific methods used because they are far beyond the scope of a panel.
It makes me very happy that I wasn’t the first person to let out an sigh signalling information overload.
However, I was able to almost keep up and have since operationalized the use of LDA with some customized tools and techniques. Without a blend of quantitative and technology skills, and a dash of business skills, that would have been impossible.
Shortly it may be impossible to do much without serious quantitative and technology skills in digital measurement.
Adobe Moves Into New Territory
Adobe is looking for a Senior Researcher, Analytics in their Advanced Research Lab. Taleo sucks so I can’t post a link, but here’s the job description:
Before you rush out and apply for it check your resume, does it say ‘Ph.D. in Machine Learning’ or some other quantitative field?
Don’t waste your time or Adobe’s, they aren’t interested.
At this point the Quantitative and Technology people are probably a little bummed if they lack a Ph.D., but very excited that Adobe may be operationalizing advanced technologies.
Some Business oriented people I know aren’t sure what to think. They haven’t the familiarity with the topics outlined in the job posting sufficient enough to fill in the blanks and get real excited.
So if you run a company and would like to be prepared for the future, your choice is clear:
The pace of development in the digital optimization arena is increasing every day, if you aren’t moving ahead you are probably falling behind.
ACCELERATE November 18th
My guess is that this will come up during the one day ACCELERATE on November 18th in San Francisco, if you haven’t signed up yet make sure to sign up for tickets here.
I am presenting in the Super Accelerator session on any topic of my choosing, and I will choose wisely.