Synthesis unlocks the power of open data. We build original datasets that can detect shifts in consumer preferences and identify growth audiences for our partners.
The era of big data promised with algorithmic certainty that we would reach new depths of understanding and predictive power over human decisions. Yet time and again we see that this results in reductive logic—the echo chamber of a news feed algorithm or the GPS which only offer the most direct route, not the scenic one.
What if, rather than unrelentingly collecting more data, we ask sharper questions of the numbers and celebrate the individuals behind them, not the most obvious commonalities?
What we search, what we watch, what we publicly share, what products and services we value—every data source reflects a different aspect of behaviour. We emphasise layering relevant datasets rather than relying on one source and scale. And we have learnt, through this approach, that the magic often comes from the contradictions between datasets.
Our goal is to detect and model shifting patterns in language and behaviour, then bring to life the people whose actions create these shifts. We go deep into the context in which patterns are created, interrogating data sources (platform mechanics, incentives, biases) and culture (norms, flux) in order to shed light on why shifts are happening and lay out the future directions of change.
We call this Human Centred Data Science.