Decision Before Data
In my consulting career I find that more and more executives are relying on data versus intuition to make business decisions. This aligns well with my belief that at the end of the day, the sole purpose of data is to inform a decision.
So before collecting any data, I first determine:
- What problems am I trying to solve?
- What are the core decisions that needs to be made?
If you agree with me, you will embrace the notion that data is not the star of the show, rather it is a supporting actor. The star is solving the core problems. But to do that, decisions will need to be made. And to make good decisions, you will need data.
So before you start to collect data, make sure you have clearly identified the problem you need to solve and the associated decisions necessary to take action to resolve the problem. I find it extremely helpful to begin by documenting your answers to the two basic questions listed above. It will force you to ensure you are working on the right problem. It will also require you to actually think through the problem by considering various points of view.
As you work through this process, consider one of these three distinct types of purposeful binary decisions:
1. START – To commit to something new (a venture, a product, a technology, or an approach)
2. STOP – To discontinue something (a venture, a product, a technology, or an approach) or to continue if your decision is not to stop
3. PIVOT – To change directions by deliberately deciding to shift your strategy
Interestingly, the more complex your organizational responsibilities, the more you need this simple approach of thinking through the decisions before launching into data collection.
In conclusion, this data and decision making process will:
- Ensure you are addressing the key problem.
- Help you communicate effectively and efficiently with your staff and customers.
- Focus and prioritize your data collection and analytic requirements.
- Help you and your staff ask the right questions that focus your precious resources.
- Ensure that your data analytic strategy supports the key business issues and goals.
- Provide a written baseline that all stakeholders can refer back to at any time in the process.