Making Decisions vs Finding Answers
Much has been written about the importance of being patient when making decisions. I was reminded of this recently after reading a popular circulating article by Jennifer Roberts on how much can be learned by taking the time to contemplate and observe.
With the current emergence of big data and real time information placing a highlight on real time decision making it is more important than ever to get the balance right between how much time is spent getting context and collecting information before using information to make decisions.
We see this distinction in how a CEO or a researcher might tackle a problem. A leader will focus on making a decision, while a researcher is focused on discovering an answer. In both cases data or information is at the heart of the process, but the handling of the data is different.
For the leader, the tyranny of now creates a temptation to make a decision based on experience, and then support (or deny) that decision with available data. Because the decision is likely made prior to the full use of available data, the data is used as a confirmatory tool. This model of leadership and decision making places an emphasis on experience in leadership.
For the researcher, experience plays less of a role. While researchers will develop a hypothesis and then confirm or deny it with available data, there is more of an emphasis on the exploratory use of data in research.
I wonder how our approach to decision making would change, as leaders, if we shifted from the active position of needing to “Make Decisions” to the more integrated position of needing to “Understand & Decide”?
I think the conflict between these two styles is the conflict that we are seeing now in how we decide and lead. In the startup world, lean startups are focused on making decisions quickly and then supporting (or denying) those decisions through rapidly developed and executed prototypes.
Emerging from the other side of the ring, we have an explosion of R&D driven “big data” and software tools to support exploratory analysis.
Both positions are insightful and hold enormous potential, but the convervenge of these positions into an “Understand & Decide” model is the eventual landing place.
Here are some practical strategies for shifting from an R&D or Decide & Support model to an Understand & Decide model
- Seek first to Understand, then to be Understood – Stephen Covey’s 5th habit of highly effective people is as relevant today as when first proposed. For a decision of any type to be accepted, we need to convince the recipient that we understand the implications of the decision from their perspective and not just our own. This simple mental model helps encourage us to be more contemplative in decisions
- Make data exploration part of your daily routine – For me, life is better if I have structured daily habits. I am intentional in establishing and tracking daily habits that I believe are necessary for growth. One habit that I believe is useful is to make exploration or “discovery” a structured part of your daily routine. Set aside a period each day (15 minutes is a start) that you devote to open, even playful discovery focused on those areas where you have an impending decision. You will be amazed how much you can learn even in a short period of time and how this will impact your future decisions.
- Shift from a data driven decision to a data driven hypothesis – While many of us would claim we are making data driven decisions, one way to ensure that those decisions are being driven by data rather than supported by data is to shift the utility of data to earlier in the process when we are developing the hypothesis. This approach is particularly important to entrepreneurs who are practicing lean principles. The use case would look something like this; rather than developing just a hypothesis and then using rapid prototypes or minimum viable solutions to test the hypothesis, start with trying to support your hypothesis with available data. Testing ideas with rapid prototypes and MVA’s is definitely more efficient than executing those ideas and discovering they fail, but if you start with a better informed hypothesis you can avoid unnecessary testing and reduce the number of course corrections required to succeed.
Let me now what you think of these ideas, and if you have other ways to consistently build data exploration into you decisions?
Thanks for reading!