Thomas L. Friedman wrote a fairly good piece for the New York Times. The theme is linked to something that has kept public policy makers awake for a very long time – the Productivity Trilemma. These two themes explain part of the reason for the rise of Data Science and how Web Analytics must evolve.
- The era of average people relying on doing an average job for average pay is over.
- Technology is more efficient than ever at destroying average jobs.
- Everybody has to get smarter.
To summarize the Productivity Trilemma:
- Productivity growth causes growth in GDP, producing negative employment effects.
- Real interest rates outpace real growth rate of GDP, causing regressive redistribution effects, leading to the impoverishment of debtors and the enrichment of creditors.
- Governments attempt to keep employment high through deficit spending to compensate for employment effects, enriching creditors and ultimately impoverishing all debtors.
Policy theorists have known of this problem since the late 1990’s (cited). I recall a paper from Europe dating to the 1970’s though. We know this problem exists.
The implication for web analytics and data science:
- Automation technology is destroying manual data entry and dashboarding positions.
- Get smarter, get creative, and get into experimentation-as-a-value-add. Do it now.
- Data Science is a creative outgrowth of BI and Web Analytics, maneuvering directly to be the destroyer of not just manual entry, but any thinking at all.
I can’t solve the Productivity Trilemma. That’s something for all of society to decide. In the meantime, the best defense against these forces is a very aggressive offense.