Wednesday, January 3, 2018

Climbing the Ladder

It would be great if we had the depth of information about the product exports of Tulsa that we have, say, for Italy.  If we did, we could build a map like this:
And, for each product on that diagram we could readily double-click to see what countries imported them (the customer base), and who else exported them (the competition).  Of course, we’d also want to know for each product what the relative economic complexity would be; that is, which products bring high-value to exporter, relatively speaking.  All of this information is available for countries, and that’s a good think IF you have a government who pays attention to such things and creates meaningful policies and incentives based on intentional goals.

Unfortunately, as far as I know there is no such dataset available at the state or city level, and honestly I don’t see much in the way of analysis and intentional goals at the national level.  So, we could wring our hands and lament our situation, or we could convince Hidalgo and his crew to convince states and cities to collect info and undertake a study for us, or we could take action locally and do something different, but along the same lines of thinking.  To me, this last notion seems more likely to bear fruit.

Perhaps a distributed, crowd-sourcing approach would work well enough?  If a city team wanted to devise a strategy for the metropolis overall, then getting input on all produced products (those consumed internally as well as exported), a marketing campaign to get feedback from the community – all those employees, customers, vendors of the firms in the city – could probably provide enough data to be useful.  For all I know, there could be such a database already, as a census of the economic makeup of the city. 
But even if there isn’t I think an even more distributed model could still work.  If each firm or individual just looked at his environment and purposefully decided to climb the ladder in their industry, progress would ensue.  For example, we could make a few simple rules or guidelines:
-          If we consume a product but don’t build the product, look into building it.
-          If we build a product but don’t design the product, consider designing it.
-          If we build a product and envision growth, see if we could train for it.
-          If we design a product but don’t teach the design skills, consider educating for it.
-          If we do something open loop, consider instrumenting the process.
-          If there is data in the process and it isn’t collected, collect it.
-          If there is collected data that isn’t much analyzed, see what can be learned from it.
-          If there are processes that require manual human work, see if it can be automated.
-          If there are devices that are smart, but mute, add communications.

I’m sure there are similar thought patterns that could apply to software-centric, biological, chemical, and other areas.  In fact, I’d say it’s a truism for technology today that ANY climb up the ladder will require savvy software programming, and probably have an opportunity for AI or other analytics as well.  It is a safe bet that we’ll need more training and specialization in cloud storage, Hadoop-scale databases, fog computing, Watson-style analytics, Google TensorFlow-style AI, and web-based user interfaces for all of it.

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