I’m a graduate student studying and researching psychology so any time I want to understand something better I turn to data. As long as you collect it honestly and in a methodologically sound manner, data don’t lie. Good science is built on good data and one of the most important experiments I’m involved with isn’t funded by any grants and doesn’t have a team of scientists working on it — it’s the ongoing study of the way I work and live.
Every year I try to take a look at the data that best describes my work habits over the past 12 months to better understand whether I’m doing what I’ve set out to do. I’m trying to find inefficiencies, misguided attention, and other gaps so I can make sure I’m doing my best work as much as possible. I happen to work for myself while going to school full-time, but regardless of the details of your work situation you probably want to be operating at peak capacity as much as possible. Conducting an End of Year Review is a great way to recalibrate as you move into the new year.
The first step of any End of Year Review is deciding what questions you want to answer. This is partially dependent on the data you have available but some possible examples include:
- How am I using my time?
- What do my actions say about my priorities?
- Have I left important but non-urgent projects by the wayside?
- How much time do I spend doing email?
- What have I done in the past year that I want to make sure I never/always do again?
The answers to these (and I’m sure countless other) questions can provide very benficial information for how you’ll try to conduct yourself in 2013. The next step is to look at the data that will help you answer these questions accurately. While many people do End of Year Reviews that are nothing more than pure mental reflection on the past 365 days, I’m always skeptical of my ability to remember things accurately. One thing being a psychology student has taught me is to be intensely skeptical of my memory. We aren’t nearly as good at remembering things as we like to think. Go with the hard data whenever possible.
Sources of Data
Obviously, RescueTime is a great source of data if you’re interested in knowing how you spent time at your computer. This is the first place I start with any sort of review on my work habits. There’s nothing quite like the shock of seeing you spent over 24 hours on time wasting activities over the course of several weeks to serve as a serious wakeup call.
Other than RescueTime, other great sources of data include; your calendar, daily journal or log, digital pictures, financial information, saved text messages, archived information from task management software, personal writing of any kind, etc. All of these sources help you see where you spent time, attention, and energy.
As you look through your calendar you may remember the awesome conference you went to last February which reminds you to follow up with that promising business lead. Looking through a year of photos will make you realize you’ve accidentally distanced yourself from some people important in your life (work, personal, or both). You may look at a year’s worth of saved work files and realize the big project you told yourself you’d work on last year is still sitting forlornly in the “unfinished” file.
Using The Data
Once you have all this data and have done any analyzing you need to do to draw some conclusions (more time on work that matters, less time on Facebook, call Steve, more writing in the morning, less computer on the weekend, etc.) how do you move forward?
First, let me point out that a potentially great first step is to decide to spend a little bit more time and effort recording more data on yourself in 2013. The better the data you have, the more you can learn about what does or doesn’t work for you.
Assuming you’re happy with the data you collect, my favorite way to make changes is to focus on one major change for 30 days. For example, when I did my most recent End of Year Review I realized I was spending way too much time on mind-numbing websites. I decided that I’d severely limit the amount of time I allow myself to mindlessly surf for 30 days. At the end of that period I’ll re-assess how the past 30 days went and whether I want to a.) continue with the experiment, b.) modify the experiment, or c.) go back to the way I was before.
Obviously, you can make changes in your life without collecting data on yourself first. You could also “do science” without collecting data — but nobody would take you very seriously. Why not apply the same standards that ensure good science to the way you make changes in your own life?
As a health professional, I have an obvious interest in the relationship between lifestyle habits and their impact on health. As I specialise in the delivery of workplace health programs, this interest extends to the link between lifestyle, health and work productivity.
As I am someone who likes to track and monitor everything, I decided to use some of the data I capture on myself to conduct a basic case study. The goal was to see if there was any relationship between my sleep habits, activity throughout the day and work productivity.
Sleep - I used the FitBit Ultra to track my nightly sleep. It does this by measuring movement. While not completely accurate, it provides a good indication and as the same method was used for the period of my analysis it offered standardisation.
Activity - I also used the FitBit to monitor my steps and activity throughout the day. However, this analysis focussed on my activity during work hours and did not include my morning run. The reason being is that my run is daily and I was more interested in the incidental activity I undertake during the day.
Productivity – Being an office based worker, productivity is traditionally difficult to measure. However I have used RescueTime for quite some time to monitor what I am doing on my computer and rating my level of productivity. I used the daily efficiency rating in RescueTime as well as looking at both morning and afternoon efficiency independently.
I collected all this data over a two month period and analysed only work. I then analysed the data using SPSS to determine statistical relationships.
So what did I find?
The key findings that were statistically significant (at a 0.05 level) included:
An inverse correlation between how many times I awoke during the night and my productivity. This means that the more interrupted my sleep was, the less productive I was during the day. This relationship was also true for the number of hours worked, so the more times I woke during the night, the less hours of work during the day.
A correlation existed between the amount of sleep and productivity. I was more productive at work following a longer sleep.
Not surprisingly there was also a link between the length of my work days and my productivity. So the longer I worked, the less efficient I was. This is of concern for those longer work days (9 hours + in my case).
The relationship between sleep and morning and afternoon productivity was similar. This indicates that poor sleep impacted my whole day, not just the afternoon when the tiredness may have been exaggerated.
My productivity in the morning and afternoon was closely related. This indicates that you generally have good or bad days, as opposed to just an unproductive afternoon.
Interestingly, there was no strong relationships between daily activity and productivity. However, I am probably not the best subject in this case as I have minimal variation in my daily activity levels.
What does it mean?
While this was only a short term case study with one subject, it did highlight the importance of health and lifestyle factors on your work productivity. It is also worth noting that I am of good general health and fitness, so the impacts on productivity would likely be more dramatic for people of poorer health.
It was interesting that the number of times I woke up during the night had a greater impact on my productivity than the amount of sleep I had. However, this is to be expected given that the benefit of sleep is largely associated with those deeper sleep stages, and regular interruptions limit your ability to spend time in these stages, regardless of your sleep volume.
My average time spent sleeping each night was 7 hours and 14 minutes. However, I appeared to be most productive when I obtained around 7.5 hours, with a noticeable decline when I slept for less than 6 hours and 45 minutes. My least productive days were associated with only 6.5 hours of sleep.
Thanks to RescueTime and the FitBit, this small case study was quick and easy to conduct. It provided me with some individual benchmarks I want to achieve in order to maximise my productivity by focussing on good quality sleep.
Build it and they will come? Performant Search brings Flexible Reports Part 1: Key Word Filtering works!Posted: November 7, 2012
Our job was to find a long term scalable solution to the problem of Searchable Time. This post discusses our search capability and some ways to use it, now that we have reliable and speedy access to this feature. There will be a follow up post presenting the technology chosen, for those interested.
RescueTime has three features that depend on what we are calling “search”, I will be presenting two of them here: using keywords and expressions as a reporting filter with the “Search” field, and the Custom Report module (the third is “hints” in projects time entry interface).
I’ve been putting “search” in quotes (though I’ll stop that affectation now) because what we’re doing here is a bit different than a traditional Google-style search. We’re giving you a way to see a view of your RescueTime history across any span of time you choose, pivoted on your perspective of interest, eg. Categories or Activity Details or Productivity, for any activity we find that matches your search request. A “Custom Report” is just a way to save a search query for repeated use. But what does this all mean?
If you take a moment and think about it, this filtering can be very powerful. If you pick a good set of keywords, and possibly some tweaking with logical expressions (more on that later), you can get a fascinating view across your history, regardless of category, productivity, or other classification that is focused in high resolution at particular project, client, or other meme that might appear in many different applications and websites. How much time did you spend dealing with “John”? or, what is my pattern of time spent in a console versus my text editor (“terminal iterm aquamacs sublime vim”)?
Consider your document names, or folder names, email addresses, chat identities, and websites as potential members of a search expression to build these reports. The search engine will also understand logical AND and NOT and nesting. The default relationship between words is OR.
Let’s consider another example: How much did the last mini-release cost us?
You’ve got a team working on a project codenamed “Piranha”. This name appears in code filenames and directories, or Eclipse project names. It appears, with a little discipline, in your email subjects. And your support ticketing and requirements tracking system. And your marketing material’s files and web pages. And your internal chat group. And your meetings entered via offline time tracker. You get the idea– we can give a total time cost of this project, with 0 (zero) data entry across your entire organization . Well, plus any time your team spent learning about piranhas on Wikipedia (pick smart project names for best results, use logical operators to help out, eg “piranha NOT wikipedia NOT vimeo). You can then save this as a Custom Report for ongoing metrics, and side by side comparison with other ongoing custom reports.
Thank you to all our customers for sticking with us and giving feedback during the iteration of this slightly magical tool. We think search is finally fully operational.
Working at a small startup can get pretty crazy. Awesome, but crazy. It’s way too easy to get pulled in a thousand different directions and end up feeling totally scattered and drained. Some would say that’s doing a startup right, but it can get downright exhausting. I felt this way recently, and I stumbled on a somewhat counterintuitive way to balance things out. I gave myself more to do. Specifically, I gave myself exactly one more thing to do. I created a weekly work ritual for myself. I started writing a “this week in productivity” blog post to add some extra content to the weekly email reports we send out to our users.
I wasn’t really trying to form a new habit, but that’s what happened, and it had some really great side-effects. Now it’s baked into my weekly routine and I wouldn’t want to give it up.
Calling it a “writing project” would be extremely generous. It’s little more than a collection of links to things that I’ve noticed throughout the week that relate to productivity. It’s blogging at it’s lowest common denominator. But it was still a challenge for me. When it comes to writing, blogging, emails, or any other meaningful typed communication I’m a complete train wreck. I fret over my word choices for hours. I over-use adjectives. I take five hundred words to say something that could just as easily be expressed in twenty. (see I’m doing it now!) Given my hectic schedule, taking some time to do something, every week, no matter what, was also a challenge.
But, once I started, some cool stuff started happening. I got a chance to step back from the chaos of my work, shut everything else off, and focus on just one thing for a while. Having a chance to get creative with it made me quickly start feeling more comfortable with writing. From a pure entertainment perspective, it gave me a chance to catch up on a bunch of blogs and websites I didn’t have time for throughout the week. And there’s just something that feels empowering about having “my thing” to do every week.
I think everyone should find their “me time” activity at work. That one little ritual that lets you retreat from the whirlwind and do something for yourself, but in a way that actually makes sense to do at work. It doesn’t need to be anything spectacular or anything, just something that makes sense for you.
It should be personal: Don’t just take on something new just because it needs to get done anyway. Give some thought to it, and find a project that you’re going to find some personal value in.
It should be sustainable: Don’t bite off more than you can chew. If you take on too much, you’ll overload yourself and end up feeling worse.
You should take some time to reflect on it: Forming a new habit is an accomplishment. Make sure you step back and look at your progress every now and then, so you can see how it’s impacting you.
Last week, I went to see my doctor for my annual checkup. I was PREPARED. I had detailed graphs tracking my weight, sleep, sedentary time, and the number of steps I take every day. It was laid out all pretty, and easy to understand at a glance. In my head, it was fantastic. My doctor would be totally impressed that I was so proactive, and have all sorts of expert opinions on how I could best use these piles of data I’ve been compiling.
That’s not how it went down. At all. Instead, he politely listened and looked at what I had, then said “Oh, that’s neat. Was that hard to do all that? Ok, moving on…”
Neat. That was it.
Although I was hoping for a more enthusiastic response, I sort of expected it. My doctor has a set way of doing things, and this new information didn’t really fit into it. Furthermore, he didn’t know much about the integrity of the data (for example, how do I actually measure sleep quality?) From his perspective, it was a lot to take in without an obvious huge benefit. I wasn’t totally crestfallen, though. I know enough about what I’m tracking to see the value in it, and it gives me a complementary framework to improve many of the things my doctor wants me to do. (If you’re wondering, I need to sleep & exercise more)
It got me thinking about how this same situation can happen in the workplace. I think it’s natural for motivated people to want to optimize their productivity. When they find something that works for them, they want to share it. Similar to my experience with my doctor, bringing this information to your boss can be problematic. There are all sorts of reasons that the information you find so exciting and meaningful will fail to make the same impression on your manager.
Here are a few points to consider when having a data-driven conversation with your boss (or other co-workers, for that matter).
It’s easy to get too far down in the weeds.
I’ve personally gotten my share of blank stares when presenting ideas based on some obscure productivity metric. (Hey, check out this 20% drop in my task-switching ratio! We should all be doing this!!!) Sometimes, you’ve been thinking about this stuff for a while, and you have extra context that may be difficult to quickly convey. If you can’t explain it without a 20 minute backstory, it’s probably not going to go over well.
Don’t hit your boss in the face with a data-firehose.
Even if you’re talking about less-arcane data points, it’s still easy to overload someone with data. If you roll into the conversation with 18 different metrics, it’s going to be too much to take in, even if each individual item is easy to understand. When it comes down to it, any conversation about productivity should ultimately be about trying to reduce complexity and not introducing more work. If you’re essentially saying “look at all this new stuff that you weren’t keeping track of before, but you can now!”, then that’s going to be a problem.
Get detailed-enough to be useful, but no more.
Personal analytics are great, because you relate to the information in a way that no one else could. Be careful to strike the right balance when talking to others about your data, especially a manager. If you’re not granular enough, the data may lack sufficient meaning (an example here might be tracking a measure of multi-tasking without the context of why you’d want to alter that metric). On the other hand, if you go the other direction and say “here’s a second-by-second breakdown of everything I spent time on in the last month”, it’s too noisy Also, you might actually be setting yourself up for an awkward conversation. Oftentimes, when presented with an overwhelming list, someone will scan for the first thing they have ANY context for, and unfortunately, that might end up being the 2% of your time you spent on Facebook, even though the 30% of time you spend in email might be much more meaningful and actionable.
Show some real results, then propose an experiment.
The single best way to impress someone with your personal analytics project is to take a data point that they’re already familiar with, and show how something you’re tracking relates to a measurable improvement. Charts and graphs are nice and all, but if you say “Hey boss, my billable hours went up 15% by me cutting back on the time I spent in email, here’s the data to prove it.”, then it’s going to get people’s attention. Once you’ve found something that works for you, think about how it could apply to others. Propose an easy trial with a couple colleagues to see if your efforts are repeatable with others on the team. (alternatively, skip the manager and set it up with them directly) Obviously, if you’re having this conversation with your manager, it may be their decision to figure out if or how to apply what you’ve discovered to the rest to the team, but laying out a framework is a better starting point than just saying “Here’s what worked for me, now you go figure out something to do with it.”
Maybe the conversation makes more sense for your peers than your boss
There are some hurdles that come with translating an idea that works for you into something that your manager spreads throughout the team. Sometimes, self-tracking projects can take on a bit of an authoritarian feeling when imposed from the top-down. It can really take the “personal” out of personal analytics. Perhaps a more grassroots approach makes more sense. If you’re excited about some self-tracking you’ve done for yourself, share it directly with your co-workers. If what’s working for you will really also help others be successful, then it should be easy to get others excited about it. And if others on your team are finding value in it, your manager almost certainly won’t stand in the way.
What works for you just might not work for the whole team, and that’s OK.
Finally, if it doesn’t seem interesting to others on the team, then perhaps its something that really works better for you than it does for others. There’s nothing terribly wrong with that. That’s the beauty of personal analytics, it helps you understand what works for you. Although if you’re on to something that really works for you, everyone will eventually wonder how they can use the tricks you have up your sleeve.
RescueTime aims to give people an easy platform for self-tracking and experimentation with their personal productivity. If you’d like to give it a shot, you can sign up for a RescueTime account here.