Analysis Paralysis Part 2: When Is Enough, Enough?
You’ve said “enough is enough” from your data. What now?
In my previous blog on analysis paralysis I talked about how, as counterintuitive as it seems, having a lot of data can be too much of a good thing. That’s because it’s easy to get stuck in a pattern of asking more questions when you have so much data in front of you. Even if you have the answers you need to move forward with a business decision, it is often difficult to know for certain. How can you spot the difference between doing your due diligence and spinning your wheels?
To avoid this vicious cycle, knowing when to say “enough is enough” is key. But as you and I both know, it’s not that simple, and I promised a solution. So, without further ado, here’s my one-of-a-kind, brought-to-you-exclusively-by-David-Avery-only solution that I’ve deduced from years of observing exceptional leaders and powerful executives make definitive decisions swiftly. I call it — drum roll, please — the decision-making curve. Here it is in two steps:
1. Establish the KPIs ahead of time
If you were driving from Los Angeles to New York City, would you just get in your car and start driving without ever whipping out a map and figuring out how what roads to take, when you need to sleep, when to fill up for gas, tolls, food, etc.? Of course not. But most of the time that’s exactly how we incorporate data into our decisions. We start talking about solutions and then justify them with data by asking: “And how will we know if this is successful?”
That seems like a good question, but it is far too open-ended and in a room of five people generally yields about six different answers. The best leaders have a success metric in mind before even hearing proposals. Is it ROI? Is it NPV? Is it Return on Invested Capital? Whatever it is, it is home base and it’s how they know they are on the right track.
Set your KPI(s) at the beginning of the decision-making process and keep moving forward. Establishing your path ahead of time ensures you don’t wander down a different one (or several different ones).
2. 1-3-9
This is the how you can implement the first step. The concept is that the importance of the decision should dictate how many critical data points you need before taking an action. For small decisions, you need just one key metric, for medium-sized decisions, three metrics, and the most important decisions need nine pieces of quantitative support. This formula yields the decision-making curve:
The “1-3-9” rule applies to organizations of all sizes; however, determining the importance of a decision is relative. Opening a new product line for a well-established market leader is a much smaller decision than for a new start-up. Make-or-buy incremental analysis takes on a whole new meaning when you are realizing incremental gains in one year versus two years and you have yet to at least break even on your P&L.
Once you’ve determined the magnitude of a decision for your organization, adhering to the decision-making curve will keep you from analyzing a decision from every possible angle, constantly second guessing yourself, and ultimately missing out on a time-sensitive opportunity.
While it’s a straightforward solution, the power is in its simplicity. Comment below on the success you have when implementing the decision-making curve. In the meantime, find out common misconceptions about analysis paralysis by keeping an eye out for one of my upcoming blogs: Analysis Paralysis: Mythbusters