Early in my education as a computer programmer I encountered Niklaus Wirth’s seminal Algorithms + Data Structures = Programs. The fundamental insight was that algorithms and data structures have to be fashioned in concert; a good choice of data structure can simplify an algorithm, a clever algorithm might allow a simple data structure.
An example from the pandemic environment we are all living through is working with exponential functions (an algorithm). You quickly learn that expressing the data as logarithms (a structural choice) greatly simplifies much of the analysis. Complex curves turn into simple straight lines.
If you’re a good engineer, computer programmer, or data scientist, you’re trained to think about these tradeoffs in a systematic way. In the realm of knowledge work, we have lost sight of this useful distinction. We spend the bulk of our time and attention talking about the equivalent of algorithms.
What is the opportunity to simplify or improve our effectiveness at knowledge work if we devote more attention to the data layer of knowledge work? What kinds of tradeoffs should we be looking for when doing knowledge work? What choices about how we organize and manage data might improve the quality or effectiveness of doing knowledge work?
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