All organizations should plan, execute, monitor performance, adjust,……..plan, execute, monitor performance, adjust,………repeat, repeat, repeat. Each one of these steps should incorporate performance metrics. Performance metrics are typically quantitative data sets, that are based on key indicators (what is important/key is different for each organization). Key decisions should not be made solely on objective data, but they should never without validating against the organizations performance metrics. Also, not having performance metrics (the data) is not an excuse, it is merely an admission that the data needs to be created and tracked moving forward.
Growth oriented organizations track their performance, and what is being tracked continues to evolve so that decision-making is more objective and brisk. When we understand what success looks like, and how we are doing comparatively, we are able to make decisions quickly and with greater accuracy. There is always a portion of our decision that is based on gut-feel, and we want that portion to be the tipping point rather than the basis for the decision. We may have the gut-feeling that we need to do something, but we need the supporting data to determine what that something is, and how we will move forward. Once we have the data, the gut-feel still plays a part in moving forward with the decision. There are many benefits of using support metrics in our decision-making process:
- More timely decisions: We have the “feeling” it is time to hire an additional resource. Once we look into the supporting data we realize that business has in fact increased, but while it looks sustainable, we still need to see more of an increase before moving forward. We decide to wait because we believe if the business activity continues to increase, the additional hire will be financially feasible and the probability of long-term employment will be higher.
- Objective data is hard to argue with: Strong disagreements and emotions are present when decisions are being made primarily on subjective data. When there is a disagreement, people feel they are right and we are wrong; they may even lose confidence in your decision-making going forward. If this happens, it is the fault of the leader for not requiring a more disciplined and objective approach to decision-making. With objective data, there is more common ground, and the approach is more sensible when the subjectivity is coupled with strong objective data.
- Discovery of inefficiencies: While the data may tell us that we need to do something, often times what needs to be done is different from what was originally proposed. Examples: Do we need more people on the team, or do we need a different process? Do we need that new system, or do we just need people to fully understand what is required of them? Do we need to invest more in that program, or do we need to divest ourselves of it because it is not growing fast enough? Sometimes when a team member approaches us and says, we really need to do “this”, we end up determining that something needs to be done – but after looking at the data together we determine that what is needed is totally different from what was initially proposed.
- Sustainable Growth: Every bad decision negatively impacts growth. A focus on objective data helps us increase the probability of making good quality decisions that promote consistent growth.
Take a look at the organizations/people who are consistently successful. How do they make decisions? They are not just “lucky”. People and businesses make bad decisions when objective (the facts) data is ignored. The “I want”, “I feel”, “We should have that because they do”, “This will solve everything”, kind of comment is subjective (an opinion). This subjective data is not necessarily bad, as long as it is weighed against the objective and factual data of what is really going on.