Organizations are under pressure to make decisions faster than ever before, and often rely on data to make those decisions.
While companies may believe that’s a sound strategy, it actually may be harming their ability to compete and come up with innovative solutions, experts say.
The reason is because many business leaders seem to abandon their business acumen when confronted with data, so they’re not making decisions that truly move an organization forward. Instead, because they are intimidated by the data or don’t really understand how to use it effectively, they become mired in “safe” decisions that don’t drive necessary change.
That’s where “quantitative intuition” (QI) comes into play, a strategy that uses a systemic framework to makes smarter data-driven decisions, experts say.
In a recent Columbia Business School webinar, Prof. Oded Netzer, Google management consultant Paul Magnone and American Express Vice President Christopher Frank explained how the strategy works:
- Precision questioning. “Don’t expect the data to provide both the question and the answer,” Frank says. “Ask yourself: ‘What do I wish I knew?’ What is the most essential business problem? Do a quick check around the table and ask: ‘Do we all have the same understanding?’ You may find that you’re all asking different questions because you have a different outcome in mind.”
- Pattern recognition. Magnone suggest leaders always put data in context. How does it compare to the competition? How does it compare to something the organization has done in the past?
- Parallel/holistic view. Netzer says too many teams simply summarize data and then ask “What do you think?” instead of using their business knowledge and judgment to make some conclusions. Instead of just summarizing data information which is “easier” and “risk-less,” he suggests synthesizing information, which calls for making a conclusion about the data and then supporting your viewpoint with two or three supporting facts.
Netzer says that organizations suffer when leaders and their teams become intimidated by data because they think they have to be math whizzes or data analysts to understand it. That’s one of the reasons that QI was developed, because “we want to make decisions that stick,” Frank says.
A critical piece of that is learning to make the leap from just summarizing or stating the obvious “what” of an issue to using business savvy to come to finding the “so what” or “now what” of that issue, Netzer states.
“We live in a world of big data, and we used to say that what gets measured, gets done,” Frank says. “But now everything can get measured and it seems like sometimes nothing gets done. One of the biggest challenges is: ‘How do you make effective decisions with information? More important, how do you make them at the right speed and also make decisions that have impact?”
The answer, he says, is QI. “Too many times, we get tied up in the data instead of actually making a decision,” Frank says.