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Goji Berries. 99 Problems---Solving Problems With Data

Goji Berries
“Data is the new oil,” according to Clive Humby. And it may well be. But we have yet to build an engine that uses the oil efficiently and doesn’t produce a ton of soot. Using data to discover and triage problems is especially polluting. Working with data for well over a decade, I have learned some tricks to produce less soot and more light with data. Here’s a synopsis of a few of the things that I have learned.

Is the Problem Worth Solving?
There is nothing worse than solving the wrong problem. You spend time and money and get less than nothing in return—you squander the opportunity to solve the right problem. So before you turn to solutions, find out if the problem is worth solving. To illustrate the point, let’s follow Goji. Goji runs a delivery business. Goji’s business has an apparent problem. The company’s couriers have a habit of delivering late. At first blush, it seems like a big problem. But is it? To answer that, one good place to start is quantifying how late the couriers arrive. Let’s say that most couriers arrive within 30 minutes of the appointment time. Is that good? To find out, we could ask the customers. But asking customers is a bad idea. Even if the customers don’t care about their deliveries running late, it doesn’t cost them a dime to say that they care. Finding out how much they care is better. Find out the least amount of money the customers will happily accept in lieu of you running 30 minutes to the delivery. It may turn out that most customers don’t care—they will happily accept some trivial amount in discount in lieu of late delivery. Or it may turn out that customers only care when you deliver frozen or hot food. This still doesn’t give you the full picture. To get yet more clarity on the size of the problem, check how your price adjusted quality compares to others.
Misestimating what customers will pay for something is just one of the ways to the wrong problem. Often, the apparent problem is merely an artifact of the measurement error. For instance, it may be that we think the couriers arrive late because our mechanism for capturing arrival is imperfect—couriers deliver on time but forget to tap the button acknowledging they have delivered. Automated check-in based on geolocation may solve the problem. Or incentivizing couriers to be prompt may solve it. But either way, the true problem is not late arrivals but mismeasurement.
Wrong problems can be found in all parts of problem-solving. During software development, for instance, “[p]rogrammers waste enormous amounts of time thinking about, or worrying about, the speed of noncritical parts of their programs,” according to Donald Knuth. (Knuth called the tendency “premature optimization.”) Worse, Knuth claims that “these attempts at efficiency actually ha[d] a strong negative impact” on how maintainable the code is.
Often, however, you are not solving the wrong problem. You are just solving it at the wrong time. The conventional workflow of problem-solving is discovery, estimating opportunity, estimating investment, prioritizing, execution, and post-execution discovery, where you begin again. To find out what to focus on now, you need to get till prioritization. There are some rules of thumb, however, that can help you triage. 1. Fix upstream problems before downstream problems. The fixes upstream may make the downstream improvements moot. 2. `diff` the investment and returns based on optimal future workflow. If you don’t do that, you are committing to scrapping later a lot of what you build today. 3. Even on the best day, estimating the return on investment is a single decimal science. 4. You may find that there is no way to solve the problem with the people you have.  
MECE
Management consultants swear by it, so it can’t be a good idea. Right? It turns out that it is. Relentlessly working to pare down the problem into independent parts is among the most important tricks of the trade. Let’s see it in action. After looking at the data, Goji finds that arriving late is a big problem. So you know that it is the right problem but don’t know why your couriers are failing. You apply MECE. You reason that it could be because you have ‘bad’ couriers. Or because you are setting good couriers up for failure. These mutually exclusive comprehensively exhaustive parts can be broken down further. In fact, I think there is a law: the number of independent parts that a problem can be pared down is always one more than you think it is. Here, for instance, you may be setting couriers up to fail by giving them too little lead time or by not providing them precise directions. If you go down yet another layer, the short lead time may be a result of you taking too long to start looking for a courier or because it takes you a long time to find the right courier. So on and so forth. There is no magic to this. But there is no science to it either. MECE tells you what to do but not how to do it. We discuss how to in subsequent points.
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Gaurav
Gaurav @soodoku

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