Designing Great Data Products

by Jeremy Howard, Margit Zwemer, Mike Loukides

In the past few years, we’ve seen many data products based on predictive modeling. These products range from weather forecasting to recommendation engines like Amazon's. Prediction technology can be interesting and mathematically elegant, but we need to take the next going from recommendations to products that can produce optimal strategies for meeting concrete business objectives.

We already know how to build these they've been in use for the past decade or so, but they're not as common as they should be. This report shows how to take the next to go from simple predictions and recommendations to a new generation of data products with the potential to revolutionize entire industries.

Genres: Programming, Computer Science, Business, Nonfiction

First published March 23, 2012

About the author

Jeremy Howard is an Australian data scientist and entrepreneur. He began his career in management consulting, at McKinsey & Company and AT Kearney. Howard went on to co-found FastMail in 1999 and Optimal Decisions Group. He later joined Kaggle, an online community for data scientists, as President and Chief Scientist.

Together with Rachel Thomas, he is the co-founder of fast.ai, a research institute dedicated to make Deep Learning more accessible. Previously, he was the CEO and Founder at Enlitic, an advanced machine learning company in San Francisco, California.

Reviews

[Mike Benner]

Very quick read but the content was light and not that satisfying. Some decent high level stuff on the "Drive Train Approach" but overall I would only pick this up if you are looking for a quick read while your kids are playing at the park.

[Amyth Banerjee]

Short. Interesting examples & food for thought. Worth exploring the drivetrain approach to design better data products of the future.

[Edward Pok]

Good easy read. Not too complex and easy to follow

Nice intro of the drivetrain approach - the next progression from being data driven. Good examples from different industries. Let the data drive us :)

[Shawn]

Quick, decent

Clear, concise and presents an interesting heuristic for designing data products. Worth skimming. Will only take 15 minutes or so

[Claudio Noguera]

Short read, very simple concept but still not widely applied in my experience.

[Pritesh Shrivastava]

A very short book on why data science needs to be integrated with optimization techniques, to deliver data products that don't just give predictions, but act on them, and potentially transform / disrupt industries.

[Alex Ott]

too high level...

[AMAN]

Short and full of great practical knowledge. It got me deep into optimization