Data science, as a dynamic and emerging discipline, is wrought with contradictions:
– Quantitative / qualitative (linear / non-linear; intuitive / analytical; left brain / right brain)
– Design / development (design / implementation; strategy / execution; strategic / tactical)
– Quality / speed
– Technical perspective / social perspective
– Machine intelligence / human intelligence
– Deterministic domains / non-deterministic domains
Conventional approaches to data science pursue optimization of these contradictions. Optimization instead of resolution limits the effectiveness of data science. Systematic innovation pursues resolution instead of optimization. Systematic innovation is a combination of art plus science; it is a powerful approach that eats contradictions for breakfast.
Let’s explore using systematic innovation and systems thinking to resolve these contradictions. Let’s make data science more effective.
Data Science 2.0?
post @ LinkedIn
slides @ Google