How We Use Time: Consuming, Processing and Producing

Tracking Time

I’ve been tracking my time for close to a decade and over that time, thanks largely to the logistics of this process, the categories I use to bucket my time have evolved.

What started with three simple categories; labor, leisure & human functions, eventually evolved to include ten categories. Even those gave me problems though. For example both driving & biking to work count as commuting, but they are clearly different. How do you account for those differences and the impacts they will have on your life?

Because of this, I’ve recently begun thinking of how I spend my time in a more reductionist manner. I’ve been breaking down time spent into the things it consists of rather than the intended result. Similar to how a nutritionist might break down a meal into its elements; carbohydrates, proteins, vitamins, sugars, etc.

I have a notion that separating the goals of my time from the elements that make up the use of it will help me better optimize the way I synthesize the two.

How We Interact With The External World

The first elements I want to write about is the nature of how we are interacting with the world around us. As I’ve observed how I spend my time, all of it consists of; consuming, processing, producing or doing nothing.

Each time block we spend will consist of the four of those in some combination – likely never 100% any one.

For example, right now, by blogging, I am working in a largely informational space. My interaction with the external world is focused on information. As I write, I am producing information. But at the same time, as I hit the keys and during the pauses, I am also processing information. I am internalizing the external world and using my mind to make sense of it. I am also consuming information as I periodically check the internet for helpful information, writing on similar topics or a better word to use. You might say my time is 10% consuming, 60% processing and 30% producing.

These elements aren’t limited to the informational space though, they can also relate the the physical world. For example stacking wooden blocks is a production activity with the external physical world. I am using my time and energy to create external change. Observing the blocks is a consumption activity with the external physical world. I am using my senses to internalize what is external. Contemplating how to best to stack the blocks to achieve a goal is a processing task. I am thinking about the external world without affecting it.

Sometimes, of course, we are not doing anything. At least in the approximate sense. Sleeping is my best example of this (though sometimes I am aware of processing when I sleep).

Next Steps

I intend to use these elements as part of my new time tracking framework. My goal is to roll it out by early next year, as I enter my second decade of time tracking.

My hypothesis is that there is an ideal balance of elements, for a specific person, at a specific time to achieve a specific goal.

My first step is to gather benchmark data. As I tally up all of the time I spend, I will arrive at some total amount of activity that is consumption, processing, production and doing nothing.

After I have some data, I can experiment with the inputs and see how the outputs are affected. The ultimate goal is to better achieve my goals by thinking about time spent as a combination of many elements that are individually important rather than simply a black box designed to achieve some sub-goal.

Innovation Session: Evaluating My System for Gathering Data on Myself

Last December I started measuring a few things about myself every day. Now, four months in, I’d like to take a look at how it has gone and what that data has shown me so I can improve upon the system.

Success of the System

Over 102 days I completed the survey 80 times. Based on that I would deem the method a success. Any system that is able to remind me to do something and succeed in getting me to do it ~80% of the time is doing pretty well in my book.

Pivoting my completion percent by the day of week gave me the following.

gathering-data-on-myself-completion-percent

 

The astute reader will notice that my completion rate was >1 on Wednesdays. I thought there might be a bug or double logging errors in my system. When I looked into it I realized that a few of those were actually my Tuesday records being logged sometime after midnight. I’ve been doing these innovation sessions which often keep me up past my usual bed time on Tuesdays.

Nonetheless, mid-week my success rate is much higher than on weekends. This is probably due in part to the fact that on weekends I am more likely to be out of cell range while camping. I am also often hanging out with people at 8:00PM when my reminder sounds and sometimes forget to do it when I get home. A snooze option might help with that.

What I Learned

When I started this project I had three goals, each with their own type of questions:

  1. Things I want to quantify so I can later try to correlate them
  2. Things I want to codify so I can later look back on them
  3. Things I want to ask so they will stay on my mind

I’d like to evaluate each category on its success.

Category One: Qantifiable Items

Looking at these I don’t see much of a trend. After some iteration I landed on measuring my health in six areas on a scale of 1-10. On their own the items aren’t very helpful because they aren’t associated with any sort of action items. Data without action isn’t very valuable.

In order to make these items valuable I will need to associate them with something else. For example I could associate my vocational health data with records of who I had meetings with that day to help me see who I enjoy working with and who I might avoid when possible.

If I can find a few fact items to associate with each of the six health items I might be able to make use of these but as they are, there isn’t much to see.

Category Two: Records of the Time

These were free text questions about how I succeeded and failed that day as well as what I was excited or worried about. Unfortunately free text processing is not my strong suit and so I am at a loss about what to do with these. I usually only wrote down a few words and so there isn’t much detail. Looking for the most common words returns a lot of generic words; got, was, had, etc.

In order to make this section more useful in the future it might be helpful for me to create some categories to choose from or to get more text recorded so I can better use it in the future. Overall I think the benefits from these questions actually fits better in the next section.

Category Three: Items to Keep in Mind

These were questions I asked myself because answering them would require me to reflect on my day. The questions were all along the lines of “how did you demonstrate X today?” The responses were free text and so I didn’t leave a response if I could’t think of anything for that day that answered the question. It turned out I recorded something only ~35% of the time.

Despite that, I think having these daily questions was actually a positive thing. Even on the 65% of days I did not have answers, I asked myself the question.  Skipping putting in an answer came with a desire to try harder tomorrow.

Conclusion

The system I set up to gather data is actually fairly effective, the questions I ask need improvement though. I would like to see more actionable items coming out of these daily surveys and so I will need to hit the drawing board again. When I sit down again I’d like to dig into some  aspects of my life that I have the power to change and that data can help influence my decisions on.

Innovation Session: Building A Better Surfing App With Data

Imagine this – its 6:00 AM on a Thrusday and you’re driving half an hour up the coast to surf a wave you never go to during the week. Why? Because your iPhone told you to. That killer session you had last summer, it looks like the swell is lining up to recreate it. So you grab your board and hit the road hoping to turn the stoke up to 11.

better-surfing-app-recommendations


The world of surf forecasting & reporting has evolved slowly over the last 50 years. While it has adapted to the world of websites and mobile apps – most are simply new skins on the broadcast weather radio reports surfers have relied on since 1967. They are channels for data. They tell you the swell height, period and direction and something about the wind. Even when they look amazing they are usually showing the same information.

They are not simple and intuitive nor are they predictive. We can do better.

surfing-apps

Tonight I’m going to dive into the world of surf forecasting through the lens of a data scientist and explore what a better solution would look like if Surfline, Magic Seaweed or Swellnet approached technology more like Google, Amazon or LinkedIn.

Why We Use Surf Reports

Surf forecasts & reports exist to help surfers make the most of limited resources – time & waves –  in order to maximize the desired outcome – stoke.

Outside of the small percentage of competitive surfers – most  are doing so because they enjoy it. The idea of optimizing for stoke isn’t new to surfers – check out this quora answer about who the best surfer is.

Despite some similarities, every surfer is unique in how they get stoked. Some surfers charge giant waves while others like a gentle roller. No forecast can accurately predict stoke without taking into account the wide range of preferences that exist.

So, an ideal surf forecast would know something about me and make it incredibly simple to transform my limited resources into maximum stoke.

Breaking Down The Factors

Most surf forecasts focus on a few pieces of data about waves & wind. These are incredibly important to surfing, but not the only factors in the equation. In the table below I outline a number of the factors a surfer takes into account when going to surf.surfing-factors

There are a lot of things to keep in mind when picking where, when and what to surf. Some things are controlled by the surfer – what board they ride – but others are up to nature – the swell and wind.

The items in green are what most surf forecasts report on, but that leaves the others for the surfer to decide. Each of these factors interacts with the others as well, resulting in a lot of permutations.

The swell direction dramatically affects which beaches will pick up the waves. Tide changes throughout the day will cause some spots to turn on and off. The time of day will affect the crowds at popular spots. A break choice will impact which board choice will work best.

The Two Paths For Surfers

Because of this complexity – most surfers begin to walk down one of two paths:

  1. The Path Of Complication: These surfers become encyclopedias of surf spot information. You’ll recognize them because they’ll start to say things like “its a 2′ dropping tide and 6′ 10second south swell – Newport will be drained out and besides, it is Saturday, we’ll never find parking, lets wait a few hours and try for a sunset session at Magnolia”. These surfers will eventually grow to love the raw data on StormSurf.com and will be able to carry on conversations with oceanographers.
  2. The Path Of Simplicity: These surfers start to run on auto pilot. As a result of information overload, they begin to simplify their process. They pick one or two breaks they tend to stick to. They have a go to surfboard. They surf at about the same time every day. These surfers will have their break dialed, but will miss out on a ton of stoke at other places and on other boards.

How Can We Do Better?

An intelligent surf forecast would make recommendations for me. It would take into account where I have surfed before, cross reference that with a database of historical swell conditions account for the board I used and optimize for stoke.

Much like how Amazon recommends products we might be interested in based on other products we have purchased – an intelligent surf forecast would recommend times and days for me to surf based on past sessions that I gave good ratings to.

Data Gathering

To do this – the app would blend user collected data with buoy data.

From the user we would want to record: date & time, duration, location, surfboard used & a session rating.

better-surfing-app

 This data would then be combined with swell data we have on record for that break. Because we have the location and time – we can easily do a look-up in our own records to find the tide, swell and wind information for that spot during the time that session took place.

Personalization

Now, you’ll notice for surfboard I entered ‘Jezebel’ – one of my boards. I am a big believer of the ride everything movement and I think this is a great opportunity to encourage diversity of quiver. Rather than just having the person select a type of surfboard – we can actually have them enter their quiver and then select the board they rode each time. Future recommendations would be able to refer to the board by what the surfer calls their board.

better-surfing-app-surfboard-detail

 Imagine being able to look at your quiver like this.

better-surfing-app-quiver

I bet a lot more people would longboard if they saw how stoked they were whenever they took out the log.

Recommendations

This is the intelligent part of the app. Once a data set was populated we would use a bit of simple aggregation to start making recommendations. It would take into account all of the factors and make a prediction about what attributes could combine to create a session with maximum possible stoke.

There are some simple operations like ranking all of the surf spots you’ve been to by the average score you give them.

better-surfing-app-surf-spots

There are also some more complex lookups. Lets look at a few scenarios a surfer might find themselves in.

Say there is a forecast showing a 5′ SW swell heading in over the weekend and I want to know what the best possible beach & board combination for the weekend is. The app would look at all of the past times when a 5′ SW swell was present. It would group those by location and board combinations, average the ratings and show me the top three. Rincon with my hybrid, Blacks with my thruster or 52nd Street with my booster board. This is awesome.

better-surfing-app-recommendations

We could also limit the parameters a bit. Lets say I am in Newport Beach and have two hours, but am not sure where to go surf. The app could get my location from GPS and limit the search to surf spots within a half hour drive. We could also limit the tide to the current tide. We then group like before and make a recommendation.

If I’m feeling longboardy – the app could filter by my sessions where I rode a longboard and show me the best spot to go. If I’m meeting friends at a particular break – the app can limit it to that spot and let me know which board to bring. If I know I want to surf my thruster today at Newport, the app can figure out which tide gives me the types of waves I like best. There are a lot of cool ways to use this data.

We can also get really specific. Imagine an alert that let you know you might be able to recreate that awesome session you favorited from a few years ago by bringing your hybrid to Upper Trestles at 2pm on Saturday. I would pay money for that.

Combining Data

From what we saw above – there is a lot we can do. The one limiting factor is how much data we have. If I surf regularly, the data set will grow, but the more breaks I go to and boards I ride, the more diluted the data becomes and the harder it is to find a match.

For the scenarios above we were exploring using my own data only – this is important because we are optimizing for my stoke and I might like something very different than another surfer. If, however, I’m on a surf trip the above scenarios aren’t going to help me very much. This is when we might want to use anonymous data from similar surfers.

We could build this data set using a network map of surfers with common ratings in similar scenarios. Lets say that I usually rate Blackies very high and the board I ride there is a longbaord. A network map would group me close to other surfers that rated blackies high. Maybe a few of them also like Malibu and a few other Malibu surfers like La Jolla Shores. Our app now knows that Blackies, Malibu and La Jolla Shores are similar based on surfers who have surfed more than one of them and rated them similarly. This opens up a lot of possibilities and expands the data set.

Conclusion

Surf forecast sites have focused on giving surfers data. We don’t want data, we want to go surfing. By taking advantage of mobile technology and some data know-how there is a lot of opportunity to build something amazing. Hopefully one of the current surf forecast companies will take note and start working on something similar, I would be the first one in line to start using it!