Personal Analytics Are Becoming Automatic & Actionable

by Dave McColginCopyright © 2011, Artefact, All Rights Reserved.

Artefact Report

by Dave McColgin Artefact Reports / 2Issue #01 Personal Analytics are Becoming Automatic & Actionable

Background

‘Personal analytics’ is using data about oneself to get insight and take action. A small, devoted group of people has been using any means necessary to document the minutiae of their lives over time. Goals vary – some people just want to know themselves better across a variety of measurable dimensions. Daytum and ZeaLOG are two services that visually format any arbitrary data, but entering data and

The 2009 Feltron Report shows the quantity of relationships tracked by type over the course of a year

One user’s nightly hours of sleep from ZeaLOG, based on manual data entry

analyzing them are still demanding. A larger group of people has more specific goals, for example budgeting, weight loss, or improving medical symptoms. Products have been created to serve these purposes making it easier to get insights, but data entry still requires significant discipline. Easily captured and massive data are around the corner, and have implications for a variety of businesses.

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The Latest Trends

Data capture and visualization are getting easier Dozens of products that automatically capture information have recently appeared on the market, lowering the barrier to usage by expanding personal analytics to serve more goals and more people who wouldn’t have entered data manually. Carbon Diem will calculate Carbon footprints by tracking movement; countless monitors like The Energy Detective track your home’s energy usage; StressEraser monitors and helps regulate breathing and heart rate; FitBit automatically interprets sleep cycles from movements and ambient light; and myriad apps track geographic location over time with an extra gadget or even your existing phone. Popular services like Mint or Last.fm get their data from activities people already do in finance and music, respectively. Of course, some areas like calorie tracking have not yet found a method for automatic data capture.

Fitbit and BodyMedia have wearable sensors and paired with software to make accurate, regular data capture far easier than manual methods

Microsoft Hohm provides tips and detailed information with estimated savings given your home’s characteristics, and includes descriptions

Mint supplements your financial data with goal selection and extra information to provide advice and recommended financial products

Results are getting more actionable Newer products automatically make recommendations from data instead of simply showing histories and charts of raw data the user must analyze oneself. For example, StressEraser has a breathing guide and Microsoft Hohm provides energy saving advice with personalized savings estimates. Having a lot of customers lets users support each other directly – like BodyMedia’s fitness community – but also provides more data to create tailored guidance.

Mint is a great example of sophisticated and actionable output. It supplements automatically captured data with goal setting and other simple entry that support advice on saving amounts, better interest rates available from partners, and education about credit or retirement saving.

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Supporting decisions based on these data is becoming the key moment of delight and value for users, and those decisions, in turn, inform future recommendations.

The cutting edge is correlation across data The next step is to correlate seemingly disparate data to provide recommendations – and it’s already underway. Amazon’s recommended products may be based on purchases or page views of seemingly unrelated products that happen to be correlated across users. OKCupid is a dating site that matches answers to any user-submitted questions to the likelihood of reaching out to people with certain traits. Hunch puts this into practice directly to make recommendations on 30 categories from TV shows to politics based on questions like which direction you prefer your sandwiches cut. More data volume and diversity give more power. In the near future, expect to see more examples. Useful music recommendations will come from restaurants you’ve visited, and your favorite book may be recommended based partly on your attitude toward tattoos. As the book Super Crunchers describes, ‘Why?’ isn’t an important question when the correlations are strong.

Opportunities

The least valuable market position holds the data loggers and visualizers like ZeaLOG or visualizers that use Last.fm listening history. First, technology will make this logging progressively easier and slowly chip away the opportunities here. Second, visualizers need data to be useful, which either requires manual entry or depends on another source. Last, it takes user effort to look at raw data and derive insight.

Of moderate value are specific personal data applications that serve a strong user goal, like CureTogether, which helps those with medical conditions find solutions through group capture of detailed data. Expect to see products serving

OKCupid correlates answers to all sorts of questions with dating interactions to match you better

Hunch makes recommendations on diverse categories based on correlation with answers to questions that can seem random

more goals, and more products that span multiple goals like both sleep and exercise.

The highest value is the ownership of vast and diverse data about users. Finance tools, dating sites, search providers, and operating systems have such information and we expect to see new applications leveraging those data soon. Ads, for example, can become more powerful with correlations than with keywords alone.

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Excercises

1. Which data do you have rights to? How could you use them to provide decision support? 2. Could you get data from other providers or by offering value to users? 3. How could data help you offer better products, in addition to actionable results for users? 4. Are you using data for recommendations that could also be valuable to users as a personal history or source of self-reflection?

5. Do you have a way to accurately capture data that currently requires manual effort? 6. What useful correlations and recommendation types are you in the best position to make? 7. What specific problem or group of people could benefit from feedback they don’t have now?

References and Examples

A Picture of Youhttp://personas.media.mit.edu/personasWeb.html based on web search results

http://zealog.com track anything - set goals, make social comparisons

http://feltron.com/index.php?/content/2009_annual_report/ Feltron Report – a painstaking manual example

http://www.daytum.com make your own Feltron Reporthttp://www.hunch.com recommendations based on questionshttp://www.juicycocktail.com/limits/limits.php Limits iPhone app for any goal setting/tracking

http://tallyzoo.com TallyZoo lets you track anything

Budgeting and Financehttp://mint.com personal finance with recommendationshttps://www.pearbudget.com budget tracking

Energy Efficiencyhttp://www.theenergydetective.com energy usagehttp://www.microsoft-hohm.com home profile and energy usagehttp://www.carbondiem.com/index.html travel footprint

Fitness and Sleephttp://www.inotive.com/Site/Products_&_Services.html weight and social support

http://www.bodymedia.com exercise and sleep, automatically, plus manual diet

http://www.fitbit.com exercise and sleep, automaticallyhttp://www.calorieking.com both food and exercisehttp://tweetwhatyoueat.com social food diaryhttp://home.trainingpeaks.com visualize your training datahttp://digifit.com integrate data from all your trackers into one information source

http://www.trixietracker.com infant sleep, diapering, and morehttp://www.fitday.com food diaryhttp://www.lexwarelabs.com/sleepcycle iPhone sleep trackerhttp://www.psfk.com/2010/11/all-in-one-sports-goggles.html concept for aware sports goggles

Menstrual Cycles and Ovulationhttp://Mymonthlycycles.com breast exam, PMS, period, fertility/ovulation, appointments, pre-menopause, weight tracker, and related alerts and calculators

http://monthlyinfo.com reminders for period or ovulation

Musichttp://rocketsurgeon.squarespace.com/articles/2007/5/28/last-fm-

visualizations.html Last FM visualizershttp://www.apple.com/itunes/features/#genius iTunes Geniushttp://last.fm LastFMhttp://pandora.com Pandora

Privacy Risks Frankowski, D. et al. “You Are What You Say: Privacy Risks of Public Mentions.” SIGIR ‘06. August 6–11, 2006, Seattle, Washington, USA

Narayanan, A. and Shmatikov, V. 2007. How to break anonymity of the Netflix Prize dataset. http://citeseerx.ist.psu.edu/viewdoc/download%3Fdoi%3D10.1.1.100.3581%26rep%3Drep1%26type%3Dpdf&rct=j&q=netflix%20dataset%20break%20anonymity&ei=AF3LTIKqEoy4sQPby9XzDg&usg=AFQjCNGhxcawTfvQyzUfzfk6HztGIntFvA&sig2=fpqcV2I4SnK8dmqEO5DP0Q

Readinghttp://navel-labs.com/apps/readmore designed to achieve more reading

http://goodreads.com tracking and recommending books

Special Purposehttp://bedposted.com sex frequency and qualityhttp://curetogether.com crowd-sourced medical informationhttp://okcupid.com matchmaking based on diverse questionshttp://www.rescuetime.com personal productivityhttp://www.wheredoyougo.net location heatmap based on FourSquare

Stress and Blood Pressurehttp://stresseraser.com insert your finger for guided breathinghttp://itunes.apple.com/app/ibp-blood-pressure/id306526794?mt=8 blood pressure over time

Further readingWired article http://www.wired.com/medtech/health/

magazine/17-07/lbnp_knowthyselfBay Area meetup http://www.meetup.com/quantifiedself Quantified Self blog http://www.kk.org/quantifiedself Good magazine article http://www.good.is/post/the-quantified-self-

you-are-your-dataSuper Crunchers http://www.amazon.com/Super-Crunchers-

Thinking---Numbers-Smart/dp/0553384732OKCupid blog http://blog.okcupid.comPersonal Informatics at CMU http://www.personalinformatics.org