The conventional “data-driven” workflow follows the linear sequence of data analysis driving insights, which then informs actions. Many organizations intuitively understand this and rely on their data teams to interrogate the data and guide their business functions.
However, in organizations with a high velocity, hypothesis-driven experimental culture, this linear sequence transforms into an “action-driven” learning loop. This shift significantly enhances the value of data, and effectiveness of analysis.
While this distinction may seem straightforward, its implications are profound. It reframes the value of data not in terms of being “data-driven” but actually as being “action-driven” - highlighting the responsibility and contributions of both data and business teams to data ROI.
The potency of a hypothesis-driven "action culture" lies in the fact that data essentially reflects the underlying system. If business functions are not actively experimenting and targeting inputs and drivers, the system remains relatively static. In such cases, increasing the volume of analysis on a static system leads to diminishing returns.
However, when both components are active—when there is a robust analysis culture combined with a hypothesis-driven action-oriented business culture—it creates a synergistic effect, leading to remarkable outcomes.
