Document Note: This article discusses the importance of intention when it comes to data quality. It argues that maps should be measured on how well they help achieving their purpose, rather than on self-referential measures such as internal consistency, resolution, and update speed. The author uses the example of a trip to the library to illustrate the point that to know the proper level of detail in a map, one must first know the intention of the map. The article concludes with the idea that intentionality can help measure the impact and effectiveness of data, and should be brought into data work in order to measure the impact, partner with stakeholders, and prioritize work.
Data is a woven net thrown over the world, capturing it in a grid of variable resolution. We want to capture as much detail as we possibly can, but it’s then crucial to make judicious decisions about how we translate what we capture into a useful map. (View Highlight)
Maps should be measured on how well they get you where you want to go. To do that maps need to be as detailed and accurate as necessary and no more. (View Highlight)
we need to know where we want to go.
This is intentionality, the missing ingredient I see lacking most often in my work as an analytics engineer. (View Highlight)
Defining these foundational parameters of our map will save us needless time and expense, but most importantly, they allow us to actually measure the quality of our map. (View Highlight)
There is constant discussion of how hard it is to measure the impact of data teams, and that’s directly because of a lack of intentionality with our data. The more we can stake out our desired destinations when building our maps, the better we can measure how well they got us where we wanted to go. (View Highlight)
make sure you know what your intention is. (View Highlight)
