031 - Productivity Data, Part 2 - Passive Tracking

031 Productivity Data, Part 2 – Passive Tracking

In our last cast, we discussed the power of active tracking for greater productivity. If not, it’s highly recommended you go back and listen to that cast first. You will learn about empowered choice and for that alone, the previous cast is worthwhile. Today on this cast, we discuss the importance of taking charge of the passive data being collected about you already and learning to track data that you can use productively without having to do all the work. In contrast to our active tracking discussion, we delve into the world of ambient data ripe for the picking, if you know the right ways to do so. Also, the pitfalls, perils, and opportunities that abound with passive productivity/wellness data tracking. Pull up a seat and join us for this important and timely discussion on passive tracking for productivity.

Enjoy! Give us feedback! And, thanks for listening!

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In this Cast

Ray Sidney-Smith

Augusto Pinaud

Francis Wade

Art Gelwicks

Show Notes

Resources we mention, including links to them will be provided here. Please listen to the episode for context.

Some recommended readings, even though we didn’t discuss these in the recording itself:

The Perils of Time Tracking

Productivity and Continuous Improvement – Measurement (and RescueTime) makes it happen, both Personally and at Work

If You’re Not Collecting Productivity Data, You’ll Never Succeed at Work

Items we discussed in the recording:



Khan Academy – Statistics and Probabilities


Google Fit

Getting Things Done by David Allen

Quantified Self

Nudge: Improving Decisions About Health, Wealth, and Happiness by Cass Sunstein and Richard Thaler



Gyroscope (mobile)

Time Doctor




ProcrastiTracker (OSS for Windows)

Workrave (RSI prevention but helps passively track your mouse/keyboard activity, free, Windows)


Other notables we didn’t mention in this cast:




Raw Text Transcript

Raw, unedited and machine-produced text transcript so there may be substantial errors.

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