Most people on this planet have dreams, ambitions, but don’t have very clear goals. Saying “I want to be a millionaire” is a little different from saying: “I want to have a million dollar stake in a corporation in the next five years”. Once we have a clear goal, we can divide and conquer.

Maybe a million dollars is a bit much to bite off right now, so lets do something most adults want to do, “lose weight “. Does that mean tomorrow, next week, next year, or when? Does that mean one pound or fifty? That goal doesn’t really help answer any of those questions so we revise our goal: “Loose 10 pounds by next year” this isn’t a very technical problem, but we can troubleshoot it just the same. Lets take a look a how we can help use our goal to break up the problem

First Break it down:

Need to lose 10 lbs in a year? We’ll it turns out that’s 0.83 lbs per month, or 0.026 lbs per day roughly, thats doable .

Set milestones:

Take those sub steps and make milestones, to ensure that everything is  on track.

Get More Data:

How can we be sure we’ve met the milestones without some metrics? In this case a bathroom-scale seems reasonable. Maybe we want to take some photographic evidence of changes…just make sure the data fits the goal. If we want add extra data, we can add extra goals. If we get a tape measure then another related goal could be “lose one inch from the waist in a year”

Normalize Milestones:

So 0.026 lbs per day sounds like a good milestone right? We go out, buy a scale and then realize that the data is horribly wild. Seemingly random weight losses or gains per day don’t make any sense. So that’s not a good measure of success, so ignore the static and wait for a month to go by.

Normalize Data Collection Methods:

Okay so a month goes by, and  we still have crazy data…if we measure weight today, we see a gain of 3 lbs for the month, and if we take a measurement yesterday, it seems like we lost 3 lbs for the months.  So there needs to be  a way to smooth out the data a little. Since a glass of water can change someones weight on a very sensitive scale, it is decided to weight take a measurement the same time every day. Right after we wake up, the data starts smooth out quite a bit, but its still not quite where it needs to be.

Normalize Data:

Take an average of all of the data. In this situation especially we are looking for trends over a large period of time, so don’t get hung up on individual data points.

Profit (? ):

So now that we’ve got our end goal, milestones, and data collection methods all defined we are good to go, right?

Well those steps alone aren’t enough to ensure our solutions success in this scenario. If we are still not losing weight we need to go back to our milestones and break those down. How many calories does it take to lose a 0.023 lbs? Set that as a new milestone to either give up that many calories per day, or to burn off that many calories.

Once we’ve set our new milestone, get the data…normalize…and repeat.

Even when we do all these steps, sometimes we fail to achieve our goals. This doesn’t mean that the technique failed, it means we failed defining our problem and breaking it into manageable pieces. A little harsh I know, but if we don’t have failure to motivate us, then what is there?

Don’t get hung up in the details of the weight loss example, its just an example…these troubleshooting steps are good for everything. We have to meet our milestones to meet our goals. Rome wasn’t built in a day, and it took Edison over a 1000 attempts to make a commercially viable light bulb. Start with a clear vision of the finish line, and as long as we take each step along the way without falling, then we can’t fail!

This is part of Richard’s series How To Troubleshoot Everything. He used this specific technique to lose 30 pounds in 4 months. He currently works for programs web things such as WhySpam.Me, a disposable free email tool.