The Personal Analytics of My Life

One day I’m sure everyone will routinely collect all sorts of data about themselves. But because I’ve been interested in data for a very long time, I started doing this long ago. I actually assumed lots of other people were doing it too, but apparently they were not. And so now I have what is probably one of the world’s largest collections of personal data.

Every day—in an effort at “self awareness”—I have automated systems send me a few emails about the day before. But even though I’ve been accumulating data for years—and always meant to analyze it—I’ve never actually gotten around to doing it. But with Mathematica and the automated data analysis capabilities we just released in Wolfram|Alpha Pro, I thought now would be a good time to finally try taking a look—and to use myself as an experimental subject for studying what one might call “personal analytics”.

Let’s start off talking about email. I have a complete archive of all my email going back to 1989—a year after Mathematica was released, and two years after I founded Wolfram Research. Here’s a plot with a dot showing the time of each of the third of a million emails I’ve sent since 1989:

Plot with a dot showing the time of each of the third of a million pieces of email

This essay is also in WIRED »

The first thing one sees from this plot is that, yes, I’ve been busy. And for more than 20 years, I’ve been sending emails throughout my waking day, albeit with a little dip around dinner time. The big gap each day comes from when I was asleep. And for the last decade, the plot shows I’ve been pretty consistent, going to sleep around 3am ET, and getting up around 11am (yes, I’m something of a night owl). (The stripe in summer 2009 is a trip to Europe.)

But what about the 1990s? Well, that was when I spent a decade as something of a hermit, working very hard on A New Kind of Science. And the plot makes it very clear why in the late 1990s when one of my children was asked for an example of “being nocturnal” they gave me. The rather dramatic discontinuity in 2002 is the moment when A New Kind of Science was finally finished, and I could start leading a different kind of life.

So what about other features of the plot? Some line up with identifiable events and trends in my life, sometimes reflected in my online scrapbook or timeline. Others at first I don’t understand at all—until a quick search of my email archive jogs my memory. It’s very convenient that I can always drill down and read a raw email. Because as with essentially any long-timescale data project, there are all kinds of glitches (here like misformatted email headers, unset computer clocks, and untagged automated mailings) that have to be found and systematically corrected for before one has consistent data to analyze. And before, in this case, I can trust that any dots in the middle of the night are actually times I woke up and sent email (which is nowadays very rare).

The plot above suggests that there’s been a progressive increase in my email volume over the years. One can see that more explicitly if one just plots the total number of emails I’ve sent as a function of time:

Daily outgoing emails and monthly outgoing emails

Again, there are some life trends visible. The gradual decrease in the early 1990s reflects me reducing my involvement in day-to-day management of our company to concentrate on basic science. The increase in the 2000s is me jumping back in, and driving more and more company projects. And the peak in early 2009 reflects with the final preparations for the launch of Wolfram|Alpha. (The individual spikes, including the all-time winner August 27, 2006, are mostly weekend or travel days specifically spent “grinding down” email backlogs.)

Distribution of emails per day

The plots above seem to support the idea that “life’s complicated”. But if one aggregates the data a bit, it’s easy to end up with plots that seem like they could just be the result of some simple physics experiment. Like here’s the distribution of the number of emails I’ve sent per day since 1989:

What is this distribution? Is there a simple model for it? I don’t know. Wolfram|Alpha Pro tells us that the best fit it finds is to a geometric distribution. But it officially rejects that fit. Still, at least the tail seems—as so often—to follow a power law. And perhaps that’s telling me something about myself, though I have to say I don’t know what.

Monthly distinct email recipients

The vast majority of these recipients are people or mailgroups within our company. And I suspect the overall growth is a reflection of both the increasing number of people at the company, and the increasing number of projects in which I and our company are involved. The peaks are often associated with intense early-stage projects, where I am directly interacting with lots of people, and there isn’t yet a well-organized management structure in place. I don’t quite understand the recent decrease, considering that the number of projects is at an all-time high. I’m just hoping it reflects better organization and management…

OK, so all of that is about email I’ve sent. What about email I’ve received? Here’s a plot comparing my incoming and outgoing email:

Average daily emails

The peaks in 1996 and 2009 are both associated with the later phases of big projects (Mathematica 3 and the launch of Wolfram|Alpha) where I was watching all sorts of details, often using email-based automated systems.

OK. So email is one kind of data I’ve systematically archived. And there’s a huge amount that can be learned from that. Another kind of data that I’ve been collecting is keystrokes. For many years, I’ve captured every keystroke I’ve typed—now more than 100 million of them:

Diurnal plot of keystrokes

Daily keystrokes, averaged by month

There are all kinds of detailed facts to extract: like that the average fraction of keys I type that are backspaces has consistently been about 7% (I had no idea it was so high!). Or how my habits in using different computers and applications have changed. And looking at the daily totals, I can see spikes of writing activity—typically associated with creating longer documents (including blog posts). But at least at an overall level things like the plots above look similar for keystrokes and email.

What about other measures of activity? My automated systems have been quietly archiving lots of them for years. And for example this shows the times of events that have appeared in my calendar:

Diurnal plot of calendar events

The changes over the years reflect quite directly things going on in my life. Before 2002 I was doing a lot of solitary work, particularly on A New Kind of Science, and having only a few scheduled meetings. But then as I initiated more and more new projects at our company, and took a more and more structured approach to managing them, one can see more and more meetings getting filled in. Though my “family dinner stripe” remains clearly visible.

Here’s a plot of the daily average total number of meetings (and other calendar events) that I’ve done over the years:

Average events per day

The trend is pretty clear. And it reflects the fact that in the past decade or so I’ve gradually learned to work better “in public”, efficiently figuring things out while interacting with groups of people—which I’ve discovered makes me much more effective both at using other people’s expertise and at delegating things that have to be done.

It often surprises people when I tell them this, but since 1991 I’ve been a remote CEO, interacting with my company almost exclusively just by email and phone (usually with screensharing). (No, I don’t find videoconferencing with the company very useful, and the telepresence robot I got recently has mostly been standing idle.)

So phone calls are another source of data for me. And here’s a plot of the times of calls I’ve made (the gray regions are missing data):

Diurnal plot of phone calls

Yes, I spend many hours on the phone each day:

Daily hours on the phone and monthly hours on the phone

And this shows how the probability to find me on the phone varies during the day:

On-phone probability

This is averaged over all days for the last several years, and in fact I’m guessing that the “peak weekday probability” would actually be even higher than 70% if the average excluded days when I’m away for one reason or another.

Here’s another way to look at the data—this shows the probability for calls to start at a given time:

Call start times

There’s a curious pattern of peaks—near hours and half-hours. And of course those occur because many phone calls are scheduled at those times. Which means that if one plots meeting start times and phone call start times one sees a strong correlation:

Calls and meetings

Differences between meeting and phone call start timesI was curious just how strong this correlation is: in effect just how scheduled all those calls are. And looking at the data I found that at least for my external phone meetings at least half of them do indeed start within 2 minutes of their appointed times. For internal meetings—which tend to involve more people, and which I normally have scheduled back-to-back—there’s a somewhat broader distribution, shown on the left.

Call durationsWhen one looks at the distribution of call durations one sees a kind of “physics-like” background shape, but on top of that there’s the “obviously human” peak at the 1-hour mark, associated with meetings that are scheduled to be an hour long.

So far everything we’ve talked about has measured intellectual activity. But I’ve also got data on physical activity. Like for the past couple of years I’ve been wearing a little digital pedometer that measures every step I take:

Diurnal plot of steps taken

Daily steps averaged by month

And once again, this shows quite a bit of consistency. I take about the same number of steps every day. And many of them are taken in a block early in my day (typically coinciding with the first couple of meetings I do). There’s no mystery to this: years ago I decided I should take some exercise each day, so I set up a computer and phone to use while walking on a treadmill. (Yes, with the correct ergonomic arrangement one can type and use a mouse just fine while walking on a treadmill, at least up to—for me—a speed of about 2.5 mph.)

OK, so let’s put all this together. Here are my “average daily rhythms” for the past decade (or in some cases, slightly less):

Graphs of incoming emails, outgoing emails, keystrokes, meetings and events, calls, and steps as a function of time

The overall pattern is fairly clear. It’s meetings and collaborative work during the day, a dinner-time break, more meetings and collaborative work, and then in the later evening more work on my own. I have to say that looking at all this data I am struck by how shockingly regular many aspects of it are. But in general I am happy to see it. For my consistent experience has been that the more routine I can make the basic practical aspects of my life, the more I am able to be energetic—and spontaneous—about intellectual and other things.

And for me one of the objectives is to have ideas, and hopefully good ones. So can personal analytics help me measure the rate at which that happens?

It might seem very difficult. But as a simple approximation, one can imagine seeing at what rate one starts using new concepts, by looking at when one starts using new words or other linguistic constructs. Inevitably there are tricky issues in identifying genuine new “words” etc. (though for example I have managed to determine that when it comes to ordinary English words, I’ve typed about 33,000 distinct ones in the past decade). If one restricts to a particular domain, things become a bit easier, and here for example is a plot showing when names of what are now Mathematica functions first appeared in my outgoing email:

First email appearance of Mathematica functions

The spike at the beginning is an artifact, reflecting pre-existing functions showing up in my archived email. And the drop at the end reflects the fact that one doesn’t yet know future Mathematica names.  But it’s interesting to see elsewhere in the plot little “bursts of creativity”, mostly but not always correlated with important moments in Mathematica history—as well as a general increase in density in recent times.

As a quite different measure of creative progress, here’s a plot of when I modified the text of chapters in A New Kind of Science:

Plot of when chapters were modified in A New Kind of Science

I don’t have data readily at hand from the beginning of the project. And in 1995 and 1996 I continued to do research, but stopped editing text, because I was pulled away to finish Mathematica 3 (and the book about it). But otherwise one sees inexorable progress, as I systematically worked out each chapter and each area of the science. One can see the time it took to write each chapter (Chapter 12 on the Principle of Computational Equivalence took longest, at almost 2 years), and which chapters led to changes in which others. And with enough effort, one could drill down to find out when each discovery was made (it’s easier with modern Mathematica automatic history recording). But in the end—over the course of a decade—from all those individual keystrokes and file modifications there gradually emerged the finished A New Kind of Science.

It’s amazing how much it’s possible to figure out by analyzing the various kinds of data I’ve kept. And in fact, there are many additional kinds of data I haven’t even touched on in this post. I’ve also got years of curated medical test data (as well as my not-yet-very-useful complete genome), GPS location tracks, room-by-room motion sensor data, endless corporate records—and much much more.

And as I think about it all, I suppose my greatest regret is that I did not start collecting more data earlier. I have some backups of my computer filesystems going back to 1980. And if I look at the 1.7 million files in my current filesystem, there’s a kind of archeology one can do, looking at files that haven’t been modified for a long time (the earliest is dated June 29, 1980).

Here’s a plot of the latest modification times of all my current files:

Modification dates of all current files

The colors represent different file types. In the early years, there’s a mixture of plain text files (blue dots) and C language files (green). But gradually there’s a transition to Mathematica files (red)—with a burst of page layout files (orange) from when I was finishing A New Kind of Science. And once again the whole plot is a kind of engram—now of more than 30 years of my computing activities.

So what about things that were never on a computer? It so happens that years ago I also started keeping paper documents, pretty much on the theory that it was easier just to keep everything than to worry about what specifically was worth keeping. And now I’ve got about 230,000 pages of my paper documents scanned, and when possible OCR’ed. And as just one example of the kind of analysis one can do, here’s a plot of the frequency with which different 4-digit “date-like sequences” occur in all these documents:

Occurrence of years in scanned documents

Of course, not all these 4-digit sequences refer to dates (especially for example “2000”)—but many of them do. And from the plot one can see the rather sudden turnaround in my use of paper in 1984—when I turned the corner to digital storage.

What is the future for personal analytics? There is so much that can be done. Some of it will focus on large-scale trends, some of it on identifying specific events or anomalies, and some of it on extracting “stories” from personal data.

And in time I’m looking forward to being able to ask Wolfram|Alpha all sorts of things about my life and times—and have it immediately generate reports about them. Not only being able to act as an adjunct to my personal memory, but also to be able to do automatic computational history—explaining how and why things happened—and then making projections and predictions.

As personal analytics develops, it’s going to give us a whole new dimension to experiencing our lives. At first it all may seem quite nerdy (and certainly as I glance back at this blog post there’s a risk of that). But it won’t be long before it’s clear how incredibly useful it all is—and everyone will be doing it, and wondering how they could have ever gotten by before. And wishing they had started sooner, and hadn’t “lost” their earlier years.

Comment added April 5:

Thanks for all the great comments and suggestions, both here and in separate messages!

I’d like to respond to a few common questions that have been asked:

How can I do the same kind of analysis you did?
Eventually I hope the answer will be very simple: just upload your data to Wolfram|Alpha Pro, and it’ll all be automatic. But for now, you can do it using Mathematica programs. We just posted a blog explaining part of the analysis, and linking to the source for the Mathematica programs that you’ll need. To use them, of course, you’ll still have to get your data into some kind of readable form.

What systems did you use to collect all the data?
Different ones at different times, and on different computer systems. For keystroke data, for example, I used several different keyloggers—mostly rather shadowy pieces of software marketed primarily for surreptitious uses. For the phone call data, all my landline phones have always been connected to our company phone system (originally a PBX, now a VoIP system), so I was able to use its built-in logging capabilities. For email, I had a script set up as part of our company email system back in 1989 that forks off a copy of all my messages, and sends them to an archive. This script has had to be updated quite a few times over the years when we’ve changed email systems.

How does your treadmill setup work?
It’s pretty straightforward. I have a keyboard mounted on a board that attaches to the two side rails of the treadmill. I’ve carefully adjusted the height of the keyboard, and I’ve put a gel strip in front of it, to rest my wrists on. I have the mouse on a little platform at the side of the treadmill. And I have two displays mounted in front of me. I’ve sometimes thought about developing some kind of kit to let other people “computerize” their treadmills… but it’s seemed too far from my usual business. (And when I first had the treadmill set up, I was still a bit embarrassed about my impending middle age, and need for exercise.)

With everything you have going on, do you find time for your family?
Happily, very much so. It’s helped a great deal that I’ve always worked at home, so when I’m not actively in the middle of working, I can spend time with my family. It’s also helped that I’ve been very consistent for a long time in taking an extended dinner break with my family (that’s the 2.5 hour gap visible in the early evening in most of my plots). In the blog, I concentrated on work-related personal analytics; I have quite a lot more that’s family oriented, but I didn’t include this in the blog.


  1. Now that’s what i call business intelligence! Tnx for sharing this Stephen. Really interesting. You have put a lot of work and effort into this.

  2. It would be interesting to see if the keystroke data allow you to predict the types of errors being made and thereby create a DWIM typing program to save time by being tunable to individual typing mistakes and fixing them automatically. I also find the 7% backspace figure interesting and have to believe when people used non-WYSIWYG keyboards as on typewriters, they they must have controlled their error rates to reflect the increased difficulty of correcting errors.

    There is of course the interesting results that could be obtained from analyzing the lexicon of the text over time. Finding what content was being discussed. Creating a virtual summary of the subject matter. That would provide the most important results, a sort of automated diary of what you were thinking about over time.

  3. With better human-centric programming languages and data structures also the human data starts making more sense than we can follow. For example, subjective relevance, meaningful perceptions, intentions, achievements and synchronous events and process remain hidden in the analysis. Once these are revealed and represented in the data, then we have something. If we don’t allow individuals to express themselves in a relevant way or do not collect such data somehow, no pattern recognition schemes and Big Data tools in the world will help us to find out what is really happening n human lives …

  4. Truth time, I’m equally impressed by your dedication to schedule as I am with this analysis. The one piece of data that stood out was your weekend call schedule? Are you really on the phone that late?

  5. So you did all this to find out that you sleep between 3am and 11am, have dinner around 8pm and are very busy otherwise?

  6. It looks like in mid 2009 you spent a good chunk of time in a different timezone than you usually reside.

  7. Interesting and inspiring. I suspect someday our devices will have a variety of very precise sensors and so much spare capacity that they will measure us constantly in incredible detail. Software that aggregates many individual observations into cohesive stories, agents that watch our data exhaust for interesting correlations, and discovery tools for people / places with massive feature sets as inputs are capable of creating new fortunes and changing society.

    I look forward to reading more about this from you and other data pioneers.

  8. I would love to know the different software used for tracking these things. Email and calendar are fairly similar and you could write a script to do it your self. But what about phone call logging and keystroke logging? The phone call logging started in 2004 so there is a very good chance it started when you bought a very early smart phone of some kind. Keystroke logging would be extremely interesting. I have an interest in language processing and it would be interesting to analyse my own.

  9. Thanks for sharing, much appreciated reading!

  10. if you like analysing facts about life, then you might find the Radiolab discussion/presentation on the Feltron report quite interesting.

  11. I’d be interested in knowing the time distribution of your backspace keystrokes. Are they more likely at noon, or at 3am?

  12. Amazing data and analysis. I think it would be interesting with your keystroke data do to a heatmap visualisation of each keystroke in the form of a keyboard layout.

  13. Awesome! Can you throws on how you record your data?

  14. wow… you are a SUPER NERD! (What did your wife say about your crazy hours and data collection?) Don’t you ever want to sit around and be a lazy bum sometimes?

  15. Stephen, are you aware of the Quantified Self movement?

  16. I’d really like to start doing something like this but I don’t know what metrics I should start tracking. But an amazing article, Stephen.

  17. Interesting! Have you also got daily data on your weight? That could be used to find out if your regular exercise is enough and, indirectly, whether you were able to eat healthily during the busiest time of finishing your book. There might also be some peaks on certain holidays. 🙂

    And what about the times you have been sick – there must have been those? Any pattern preceding those times? Correlation to the time of the year?

  18. Wow, really interesting stuff!

    It would be really useful to know what and how you used to keep all of these different records going for so long alongside your better things to do.

  19. Dear Stephen,
    maybe the way of life, to have success.
    Thank you, sencerely

  20. Just eyeballing it, that “distribution of emails per day” graph looks a lot like the graph of Planck’s Law.

  21. Wolfram Alpha should develop a desktop application to enable personal analysis over many different digital areas. Individuals could then develop a picture of themselves – for themselves. I think people would be amazed to find out how much can be known about them via simple coordinates like email usage and phone calls, let alone searches.

    Perhaps this will greatly change how people view, and use, services such as Google and Facebook. Or at least make collecting and analyzing user data more competitive – given that the user himself knows how valuable he really is.

  22. I’m in desperate need of Stephen’s mentorship.

  23. If you can’t express it in numbers, you don’t know anything about it?

    Similarly, if you can’t visualize those numbers, they’re meaningless!

    I bet he’s not sharing the real interesting stats: Automated vs manual mails; Reply rates; most often used words at different times of the day; Most often typed keys – and at different times of the day; These stats correlated with more global stats; Digital relationships – longest time between first and last correspondence; the list is probably infinite 🙂

  24. Great dataset, yet the analytics ‘just’ make it to distribution plots and trends over time.

    I want to see models!

    Let’s start simple and look at association. You want to be happy, so measurements of happiness might be:

    -measures of mood
    -measures of quality time spend with spouse
    -amount of alcohol you drink
    -sleeping statistics
    -frequency and intensity of sex life

    From the emails, extract frequently used words and emotional expressions and find out which concepts relate mostly to your quality of life. Starting with simple correlations, a step can be made to create a model, i.e. a combinations of indicators that relate to happiness.

    Inspection of such a model shows you to which patterns you are most reactive to, i.e, influence your happiness most.

    Next is to setup a scoring: take the input of your current daily life, and define alerts for future happiness. Prediction of your future happiness that do not make the cut off create an alert and you can be advised which change of words in your email would lead to the highest amount of hapiness.

  25. An application of this should be developed for companies to measure and report employee efficiency. Great information. I agree with Nathan, some more information on how you kept this data tracking going would be interesting as well.

  26. Awesome.

  27. Yes, I look at a lot of this kind of data too, and more. Some time in the future I plan to release a comprehensive set of analytics on my “happiness time series” — currently a measure of my personal happiness (overall and about ten subfactors), to the nearest 1/100th of a %. I currently have over a decade of this data, and run a battery of statistics on it annually.

  28. Dr. Wolfram,

    Can you post a photo of your ergonomically-sound treadmill/computer setup?

    Thank you.

  29. So can the new wolfram pro do this kind of analysis for me?

  30. This is great! I started analyzing myself from last year.

  31. Someone has too much time on his hands since he finished Wolfram Alpha. Time to start working on Wolfram Beta! 😉

  32. Fascinating. We can get huge amount of interesting and usable data from trivial things in our lives.

  33. This is so cool!! Thanks for sharing, it’s interesting life statistics..

  34. Missing dataset: the browsing history. Why?

  35. Very interesting – but no analytics on time spent with family?

  36. Interesting data Stephen, it should be available in Wolfram Alpha, where else?!!

  37. Sorry Stephen – but you have a sad life. Why has our generation become so compulsive in using email? Surely there is more to life???

  38. Wow! Very interesting. All I can say is you’re a busy man!

    No data on time spent with family?

  39. I cant believe i read this!

  40. Very interesting, making yourself a kind of research subject.

    What I’m wondering is how you determined that despite your assumption that other people were collecting this kind of personal data, “apparently they were not”. Do you mean people you know were not, although you had assumed they were? How could you possibly know there’s no one else out there doing it?

  41. Most cool. Didn’t know that you’ve been keeping track of yourself to this degree. Quick question, does your keystroke information contain time/dates? Would you be able to do a graph, date on x axis, time on y axis, broken down by hour (or quarter), with the z axis (shade of color) giving the percentage of backspace keys you’ve hit.

    I’d love to see if the error rate (or backspace rate) changes with how long you’ve been awake… 🙂

  42. How was this data collected? I’d love to capture this kind of information on my own habits.

  43. You are remarkably consistent with your meetings / events. I have found it difficult to do creative work with interruptions at that frequency.

    The analysis is great.

  44. This is exactly the kind of personal business intelligence described in Chapter 10 of _Ubiquitous Computing for Business_. For Wolfram, he’s using the data to discover little things about himself – most of which he probably knows subconsciously (e.g., 7% of the key-presses he makes are backspace). But more interesting is that when you share some of these insights with others, you can streamline the overhead of communication overhead. This is increasingly important for virtual teams and global organizations who aren’t able to build mental models of each other’s work rhythms.

  45. Mi novia me envió este vinculo y como recursos de investigación cito 2:

    1) ManicTime una aplicacion de windows que permite saber que uso / o no uso le das a tu ordenador, que programas, paginas o acciones realizas en tu ordenador. Lo que permite analizar como empleas tu jornada laboral a cabalidad.

    Ademas este libro que compre ayer valga el comercial donde describe como nuestras vidas son en realidad una serie de habitos repetitivos.


    Espero que les sirvan a otros.

  46. Why can’t I put sparklines in the comments? I mean we’re talking about data here, how about some visualization tools for us 🙂

  47. Did you build your tracking systems as you found more things to track, or had you always planned on capturing specific actions of data? I have noticed a few asking ‘what about time with family’ – I gather that data points can’t be taken on that because, whilst phone records and keystrokes can be recorded, ‘offline’ time with people requires additional trackers. But for those asking ‘but what about family’ – your family dinner stripe is very clear and quite consistent.

    I did do something like this over a period of time (about a year) for emails and contacts – tracking interactions with someone I was doing beta reading for, with separate charts for each chapter that she wrote etc. Odd how close tracking eventually meant that we had quite an effective writer/editor relationship over that period. Thus I am particularly interested in the tracking you have done for A New Kind of Science.

    Thanks so much for providing us with this kind of insight, and I hope inspiration for those who are also looking to track their own personal data in such a fashion.

  48. The e-mails per day distribution looks like a negative binomial. Can be thought of as a Poisson distribution with non-constant mean coming from a Gamma distribution. Often used to model over-dispersed count data.

  49. I’m glad I’m not the only one hoarding personal metrics ‘just in case’. Fantastic analysis, I’ve been looking forward to doing the same one day soon.

    Have you considered looking for correlations between e-mailed words and words typed (like the functions)? And your most typed word.

    I have not been logging my keystrokes, but I’m on it now.

  50. Hi, looking at keystrokes typed plot (blue), it seems you travelled in mid-2009, could you perhaps adjust for the timezone difference there?