Traveling jobs on refineries

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A hundred years ago, the only data a shopkeeper had to work with was the inventory on the shelf, and the money in the till at the end of the day. That data was recorded with a fountain pen. The consumer based her purchases on pretty pictures on the box or on anecdotes from her friends.

Fifty years ago, mail order companies knew where you lived and what you ordered. In addition, they could buy some basic demographic information about you. That was it for personal data pre-web.

With the advent of e-commerce, retailers could track every click and purchase, and capture every abandoned shopping cart.

In the 1990s, Amazon pioneered the use of data to help its customers make better decisions. First, implicit data: Clicks and purchases of all users are aggregated to suggest items to a shopper in response to their most recent click. Second, explicit data: Customers have the opportunity to publish reviews that potentially influence the purchasing decisions of other customers. User-generated content turned marketing–previously viewed as carefully controlled and released information–on its head.

I think of Amazon as a data refinery: Amazon takes the data that people create, refines the data, and returns results, allowing people to make better decisions. Amazon now influences how a billion people shop.

This article looks at three common questions that many people ask every day: (1) Who should I work with? (2) Which route should I take? (3) Where should I stay on my next trip? The answers to these questions, their decisions, are now influenced by the personal data of a billion people.

(1) Who should I work with?

A startup I am advising recently hired a star engineer. How did they find him? Not through referrals or a headhunter, but through a post of his on Quora, a question-and-answer site. Like the shopkeeper, employers now have vastly more data resources. And like Amazon, job and professional sites now refine data that people create to help both individuals and companies make better decisions.

For example, LinkedIn provides tools for individuals to both refine their own personal data, creating a work identity that transcends a specific job, and to find others by acting as a refinery for other people’s data. Similar to e-commerce, the asymmetry between buyer and seller is fading away.

This does not only apply to full-time jobs. The number of marketplaces with different mechanisms to match talent and tasks is exploding. Underlying the future of work is identity that persists across tasks and jobs where reputation is a key output of the data refinery.

Within firms, data refineries are used to create teams and track interactions. A hedge fund with more than 100 billion dollars under management captures video and audio of its meetings and other data sources and correlates them to the outcomes of trading decisions. And Google’s “People Analytics” has reinvented HR.

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