Essays on tech, data, and product strategy

If data is the new oil, then data science is the new refinery.

I was recently asked whether studying Data Science has helped me in my day-to-day job. My response was yes, but not in an obvious way - it's resulted in better designed customer solutions by improving my empathy.

Let me take a step back. For the past few years, I've been leading Software-as-a-Service (SaaS) platform integrations for enterprise clients. I often describe the work as similar to being a clothing tailor. If a software consultancy is a bespoke tailor that customizes every detail at a premium price; than, a SaaS platform is a made-to-measure tailor who cuts from an existing pattern at an economical price. Over time, I've learned how to measure and cut software for customers of all shapes, sizes, and sophistication.

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The first time I encountered the potential application of machine learning was a project where I was helping a company protect itself from competitors infringing on their trademark. One key component of trademark infringement is whether there is the likelihood of confusion by a consumer. High-profile examples include Apple suing Samsung for copying their minimalist smartphone designs, or Tiffany & Co. suing Costco for selling Tiffany-inspired jewelry. 

The rise of social networks means consumers are generating tremendous amount of content about brands. My project involved writing a prototype software program to analyze online user comments that mentioned two competing brands, then determine if the user was identifying the product with the wrong brand. An example could be someone sharing a photo of a Captain Morgan rum bottle and calling it by their competitor's name, Admiral Nelson.* The goal was to run a daily scan and gather examples as evidence of brand confusion over time. The field of analyzing human text using computers is a form of machine learning called Natural Language Processing (NLP).

In a world where physical goods are easily copied, the only competitive advantage left is your reputation. 

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Back in college, I had a summer job completing research for a clinical health professor. She was a leading expert in diagnosing and treating open human wounds. My job was to survey other experts, get them to examine photos of open wounds, and then recommend a treatment.

A few months ago, I discovered a smartphone app which replaces this work.* You take a photo of an open wound and upload it to the cloud. I suspect that the photo is run through an image recognition model, called a Convolutional Neural Network (CNN), that identifies specific features of the wound for treatment. Current machine learning is very good at completing narrowly defined tasks, such as analyzing a specific type of medical image, because they have millions of previous examples to train from. It is not good at handling non-standard cases. 

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What if I'm missing out on an incredible book or song because the inputs don't capture interests of mine that I didn't even know existed?