Data-driven transformation will lead to radical new business realities. Instead of competing based on the market wide accepted assumptions of the future (for which you would be ending up in a red ocean of sharks), X needs to identify alternative scenarios about the future that X can conquer to strive (ending up in blue oceans with less competition).

While most people agree that becoming data-driven represents an important opportunity, ambitions speak louder than words. Tracking how much is aimed to unlock concretely within the next 3 years allows to assess if the company is ambitious enough or if actions are required.

Innovation is the engine of data-driven transformation, as it allows to identify opportunities and derive prioritised transformation needs. To identify promising data-driven potentials, the ideation process requires a divergence of ideas that form the foundation for a fruitful convergence on the most promising ideas.

Innovative ideas of how to gain value from data is critical, but ideas are always flawed at the beginning and need to mature through iterations of systematic assumption testing and pivoting of initial idea designs. Thus iterations are a good indicator for the progress made in increasing the maturity of ideas. Be it first iterations of initial ideas or follow up iterations moving ideas closer to implementation.