The economy of time

Disclaimer: It has been a tough month of personal life issues. And then, on top of that, is navigating the recent national news of mass shootings in Buffalo, NY, and Uvalde, TX. I do not have the words to process this, nor do I barely have the brain space, but I want to keep with my goal of a post a month. I am leaning into this reality and felt inspired to write a “what they didn’t tell you about being a [data professional]” post with a psychological twist. The experiences described below are from my own in higher education, tech, and consulting, and stores from friends and colleagues in this space. Of course, take from it as you see fit.

What is missing?

If you scroll through data science articles on medium or do a google search, you will find plenty of content on all things you should do, read, and watch to become a data scientist. Or those “things I wish I knew before becoming [insert title here]” articles. Those perspectives are fascinating and informative, but I often feel that these posts sometimes miss the basic human aspect of being in this kind of career. Also, while I speak from a data profession perspective, some of these points probably go beyond that career track.

What’s missing in the conversations? The economy of time. This is at the heart of things no one teaches you about being in a data profession.

Photo by Who’s Denilo ? on Unsplash

The philosophy of time

I like to joke with my friends and say, “…well, you know time is an illusion,” when something is not happening as planned. The culture around differs between and within countries and regions. We joke on social media about the friends who are always late or personality types who are always on time. Regarding the workplace, the practices around time and productivity are different in Denmark compared to the United States. Within the United States, states can barely agree upon the concept of pushing the clock for daylight savings time. We can get all philosophical about the concept of time as we know it being a social construct, but that is beside the point.

Culture of time in the workplace

In the workplace, especially in a data career, time is everything and expensive. Time is money when data professionals are asked questions that needed answers ASAP or yesterday because leaders need to make informed decisions. The urgency changes depending on the company or industry – from getting a research report out within a year to being on call at odd hours as a data engineer to fix broken data pipelines.

With the pandemic, how time went when going into an office every day versus working from home felt different. There are many stories about how much people reflected upon their use of time and their value, which is undoubtedly one factor in the great resignation.

The intersection time between life and work

Somehow before the pandemic, work was always a part of life, but life was not a part of work. Whatever is happening to you in your life was placed at the door because you had a job to do. When the news reported major tragedies, the most that would happen in a conversation between colleagues, we would just move on because we are at work.

Photo by Liam Edwards on Unsplash

All of these things still exist today, but there is a different flavor to them in this pseudo-post-pandemic world we are in today. There are more conversations about how people spend their time or speak about mental health in the workplace. Witnessing repeated injustices upon BIPOC and AAPI communities from police brutality, gun violence, and other violent attacks fueled by hate based on race and ethnicity infringe on your time and ability to process or do anything. The concept of being given grace, space, and time is becoming more mainstream and needs to continue because more work needs to be done there.

Unexpected lessons regarding time and data careers

So how does the economy of time relate to me as a working professional? One solid example is getting a random request to answer a question.

In managing ad-hoc requests, one of the things I find to be shared regardless of the industry is getting a request to provide a data point, and someone ends or starts with, “this should be a quick ask.” There can be whole data professional anonymous groups on this one experience 🤣. It is almost never mal-intentioned either. As the data expert, you know more or less what it takes to answer the question, and it may feel pretty simple from the outside perspective when you are asking to provide a number or a small table of numbers.

Photo by Christina @ wocintechchat.com on Unsplash

When I get a request, here are just a few of the questions that immediately go off in my head as I am processing an ask:

• What do you need to answer this question?
• Do you have all the correct information to answer the question?
• Is the data in the best format to answer the question?
• What do you need to do to get the data to answer the question?
• What is the quickest answer I can get?
• Has this question been asked and answered already somewhere else?
• How will this information be used?
• What is the context for this question?
• Is this even the right question for this issue??? 👀

Not only am I asking these questions of myself, but I am asking other questions to the stakeholder as well. I am balancing multiple projects or various priority levels and figuring out how to fit this in. I am doing my best to communicate all this while also managing time around life. But here we are, two groups of people, bringing their intersectional selves to a head on the concept of time.

Here are the things I didn’t know and lessons learned as a data professional:

🎤 You’re an advocate for data 🎤 – I don’t know who needs to hear this, but the data does not (or will ever) speak for itself. You have to speak for the data. Having the most beautifully designed dashboard or reports with data tables and visuals only is only part of the journey. You need to take the time to explain the results with a story and provide recommendations on actions one should take based on the information. And, of course, that takes time. I don’t think it should be all on data professionals to be responsible for the data literacy in the workplace, but it will often come down to them. I didn’t realize that I needed to be done until I was in it to make the time for several meetings or write up that additional document to advocate for the data analysis until I was in it.

📉 Test all your assumptions 📉 – The concept of testing your assumptions goes beyond the practices you were taught in statistics. Any assumptions you make about the data - from there should only be unique values in a table, or all values come in this one format - make sure to test it. And do so regularly. You never really know when data is not going to behave as expected, so to avoid it sneaking up on you (as much as possible), make sure your assumptions are valid. Because when you don’t, this eats up on time. Whatever time you think it will take to answer a question, as some buffer time plus a day.

🛏️ Your rest is critical 🛏️ – It feels like this should be obvious, but it is not when you are neck-deep in code debugging away, and nothing is working out. You think, “Oh, I’ll give myself another 30 minutes and then walk away,” but you don’t because you feel like you are almost there to get a solution. It is even worse when you’re telling yourself a narrative that what you are doing or being asked of you is simple, so you should get it right away. Oh, the lies I have told myself and still catch myself doing these days. Your body and mind can only withstand so much analytical work. That window of time shrinks when you are over-stimulated by the world around you with the added pressure you put on yourself (📌 Note: Key experience for my folks of color). How much time you spend on a problem is hard to determine or see when you are in it, but with time and experience, that will change as you get to know yourself and develop skills. I like to use a Pomodoro timer if I have an extensive request to work on. If I am still stuck in one place after 3 or 4 rounds of 25 minutes sprints, it is time to let it go. Nine times out of 10, when I go to bed after struggling with a problem for so long, when I wake up the next day, have some breakfast, and get back to it, I get to a solution so much quicker.

Final thoughts

You need time to

• think
• process
• manage unexpected events
• manage life happening

They say how you spend your time is a measure of your values. How much do you value yourself? How much do you value others? What does the economy of time look like in your workplace? And does it match your values?

You know, all the things I think about 😎. With all the volatile events happening in the U.S. and globally, we could all afford some extra time and space to just be before moving on to the next thing.