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Posts Tagged ‘analytics’

Talk to researchers, and they’ll tell you many of the techniques behind this latest AI and machine learning renaissance actually date back as far as the 80’s..  What’s changed is cheap access to storage and processing power in the form of “the cloud,” and by extension, the cloud has made it far easier for companies to incorporate sophisticated data-and-analytics capabilities into their applications and workflows. (more…)

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When new technologies catalyze a shift in socio-economic paradigms, economic historian commonly refer to it as a revolution – the industrial revolution of the 19th century, the agrarian revolution before that.  In the wake, a new age is born. (more…)

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This quote, from statistician George Box, is one of my favorites, because of its far reaching implications.  It serves as a reminder to me that whether it’s a predictive model written in Python saved in a Jupyter notebook or a mental model based on personal experience and encoded in the wet-ware of my brain, it’s still wrong – in some way and to some degree that I may not be aware of or even able to discern.

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This coming Tuesday, September 12th, I’ll be speaking at Product School in Santa Monica on data’s role in product development.  To help me prepare, I wanted to start exploring the topic first here with a post.  (Writing always helps me to structure my thinking.)

Consider this your preview of what I hope will prove to be an engaging discussion, and if you’re in the LA area, come out to join us in person.  All the details are available through Eventbrite.

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Long before Steve Jobs or Bill Gates were even twinkles in their fathers’ eyes, the word computer was a job title for someone who computes or performs mathematical calculations.  Depending which online resource you trust most, its use dates back to the 1600’s.  Not until much later, sometime in the 1800’s, did it come to refer to a device rather than a human.  From what I can gather, the word calculator underwent a similar evolution.

I’ve taken you on this little jaunt back in time in part because I’m under the influence of a book that I’m currently reading, Etymologicon, but mostly to make the point that another title – “data scientist” – is likely to follow the same trajectory.

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I have been learning a lot about digital advertising and mobile as my interest in music has expanded into video and media more generally.  Along the way, I’ve been struck by the resemblance the evolving digital advertising space bears to the early days of electronic trading on Wall Street (naturally, I’m also not the first to make this connection).

I thought I would try unpacking the analogy a bit here to see how well it holds up and whether there are any lessons that could be learned from the equities markets.  (This thought exercise takes up some space so for those so inclined, I’ve made it easy to skip to just the predictions and/or the lessons.)  For an interesting read on the advent of electronic trading, I highly recommend Michael Lewis’s Flash Boys – very entertaining, even for someone like myself who lived it first hand.

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In product development, after brainstorming (or ideating if you prefer) the next step is usually prototyping and concept selection.  Picking up on my last post, once you have used the business model canvas to generate a few ideas about how you might leverage resources from the current business to launch a new one, how do you pick which design to go with?  How do you prototype a business model?  I’m pretty sure a 3-D printer won’t work for that.

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