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The metric “picture” provides focus, direction, and helps us avoid chasing data.

How to Collect Data

Once we’ve designed what the metric will look like, and have an idea of what information, measures, and data we’ll need to fill it out, we need to discuss how to gather the needed parts. I’m not going to give you definitive steps as much as provide guidelines for collecting data. These “rules of thumb” will help you gather the data in as accurate a manner as possible.

Later, we’ll expand on some of the factors that make the accuracy of the data uncertain. This is less a result of the mechanisms used and more a consequence of the amount of trust that the data providers have with you and management.

Use Automated Data When Possible

When I see a “Keep Out, No Trespassing” sign, I think of metrics. A no-trespassing sign is designed to keep people out of places that they don’t belong. Many times it’s related to safety. In the case of collecting data, you want to keep people out.

Why? The less human interaction with the data, the better. The less interaction, the more accurate the data will be, and the higher level of confidence everyone can have in its accuracy. Whenever I can collect the data through automated means, I do so. For example, to go back to the example in Chapter 1, rather than have someone count the number of ski machine or stair stepper users, I’d prefer to have some automated means of gathering this data. If each user has to log in information on the machine (weight, age, etc.) to use the programming features, the machine itself may be able to provide user data.

The biggest risk with using automated data may be the abundance and variety. If you find the exercise machines can provide the data you are looking for (because you worked from the question to the metric, down to the information and finally measures/data), great! But normally you also find a lot of other data not related to the metric. Any automated system that provides your data will invariably also provide a lot of data you aren’t looking for.

For example, you’ll have data on the demographics I already listed (age and weight). You’ll also have data on the length of time users are on the machines, as well as the exercise program(s) selected; the users’ average speed; and the total “distance” covered in the workout. The machine may also give information on average pulse rate. But, if none of this data serves the purpose of answering your root question, none of it is useful.

In our workload example, it will be difficult to gather data about the work without having human interaction. Most work accounting systems are heavily dependent on the workers capturing their effort, by task and category of work.

Beware!

So what happens when your client finds out about all of this untapped data?

He’ll want to find a use for it! It’s human nature to want to get your money’s worth. And since you are already providing a metric, the client may also want you to find a place for some of this “interesting” data in the metric you’re building. This risk is manageable and may be worth the benefit of having highly accurate data.

The risk of using automated data is that management will want to use data that has no relation to your root question, just because this extra data is available.

You should also be careful of over-trusting automated data. Sometimes the data only seems to be devoid of human intervention. What if the client wants to use the weight and age data collected in the ski machine? Well, the weight may have been taken by the machine and be devoid of human interaction (besides humans standing on the machine), but age is human-provided data, since the user of the machine has to input this data.

Employ Software and Hardware

Collecting data using software or hardware are the most common forms of automated data collection. I don’t necessarily mean software or hardware developed for the purpose of collecting data (like a vehicle traffic counter). I mean something more like the ski-machine, equipment designed to provide a service with