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Most Decisions Do Not Fail for Lack of Data
In corporate steering we talk a lot about data quality and forecast accuracy. Yet when decisions still take a long time, or do not get made where they belong, the data is rarely the reason.
The reason is missing ownership. In a specifically structural sense: anyone who does not understand the cause-and-effect relationships in their business can only take responsibility for results, not for measures.
This happens often. Business units report variances but not the drivers behind them. The conversation is about numbers, not about chains of effect. The effect: despite more data, decision-making gets slower. Not out of unwillingness, but out of uncertainty about which lever actually counts.
I was recently in a workshop with a long list of KPIs but no clearly defined value drivers. Everyone in the room was capable and engaged. The discussion only turned in the right direction once a colleague had set up a simple driver model. Which quantities really determine the result? Which measures sit within the business unit’s control?
From that point on, what had been missing emerged: responsibility not just for numbers, but for decisions.
Understanding creates ownership. More data, at first, only creates more data.