This time also coincides with data-wrangling activities, building pipelines to prepare datasets for visualization. On seeing this, I was surprised that slides were the second highest design activity (6 percent) - I suspect this is due to inefficiency of the tool itself, whereas Figma can be componentized and coded dataviz can be automated, Keynote involves a lot of manual pixel pushing.Ģ9 percent of the time was spent developing visualizations, typically in javascript (13 percent React, 6 percent Angular), but also occasionally in Data Studio (4 percent). Design and engineering activities together made up 60% of total hours.ģ1 percent of the time was spent on design, which can include everything from story discovery, typically bouncing between exploratory analysis and sketching story concepts with a pen and markers (4 percent), mocking up specific charts in Figma or Google Sheets (6 percent), prototyping different design approaches in Observable (3 percent design, 3 percent engineering), and even the occasional copywriting (2 percent). Design + Engineering Distribution of dataviz design and engineering time spent on 3iap client projects, split by activity sub-type. Roughly 60 percent of the total time was spent directly designing or engineering visualizations. How much “dataviz” work goes into dataviz? Distribution of 1,550 hours spent on 3iap client projects, split by activity type. I’ve kept a close record of the time spent on each project.Īs of early 2022, 3iap has logged ~1,550 hours of client dataviz work (in addition to sales / marketing / paperwork / etc., +300 hours of general product consulting to pay the bills, and an obscene number of untracked hours on silly side projects).īelow are findings about how that time was spent, in addition to highlighting 10 specific projects that represent a range of different dataviz work. Since then, 3iap has done a variety of projects, covering the full spectrum of work you might encounter as a dataviz consultant (e.g., research, analysis, data-wrangling, metrics, design, and various types of engineering). 3iap’s time-tracking datasetįrom the start of 3iap in 2020, the focus was data visualization for clients. The goal with sharing this data is to even out that informational asymmetry, and give a detailed reference of time and effort involved in producing (fairly complex) data visualizations. Or, even worse, it can be easy to give into the pressure from occasionally overzealous clients fixated on budget line-items (“You’ll spend how long on research?!”). Smaller, independent shops, or freelancers, earlier in their careers, don’t have this advantage so it can be difficult to estimate. If you’re a larger shop, with a long history and full portfolio, you have an information advantage. One of the challenges for estimating - and expectation setting - is having a track record of similar projects to reference. So, not only do I attempt to estimate timing for every project, I also track the actual time to see if I’m right. And, for anyone who’s worked for a fixed fee, it’s important for understanding if a given project will be profitable. It’s an important part of setting expectations (which make for happy projects and happy clients). I’m a Fred Brooks acolyte and appreciate all the unforeseen ways that a complex project can go sideways. I used to resist even answering the question. You can go here to read it on Nightingale.Įvery client asks, “How long do you think that will take?” This article is featured by Data Visualization Society (DVS).
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