I am in San Antonio, Texas at the Alamo. Tremendous battles fought here in 1835. In the beginning of the war of independence for Texas, thought I talked about a friend's a question this last week about data structures and how to classify data attributes. He was saying that they had two different groups of people inside of his company, both talking about using the same term to describe something, when in fact, they're not sure that it is exactly the same thing. And when it comes down to it, they have arguments about what really is meant by that term. This isn't an uncommon thing, particularly between finance and risk, for example, they sometimes use the same terms, but they have very different meanings for what those terms mean.

If we took it out of the esoteric world of actual data and finances and those kinds of things. Let's just talk about if we have the same thing happening with someone pools. When you say pool, you might have a certain image come to mind, it could be all the way from, from a very large Olympic size 50 metre pool to kid pool, a wading pool that you might see kids in front yard. And so if by using the term pool, we really mean different things then don't need to be categorized and classified as different attributes attribute is simply something that describes another element. And in our data, we should make different attributes for those different kinds of pools. Now, that might be one set of characteristics, but not everything falls into that sort of problem space.

In some cases, you're talking about the same kind of swimming pool. For example, the University of Texas natatorium is a very large swimming pool, that actually it's one single pool but can be divided into multiple smaller pools can have a diving area, it could be divided into a diving area and 150 metre swimming pool or can be divided into the diving area and to 25 yard pools. Now in this instance, we're talking about the same physical thing, but it's three different parts to it. And in some cases, someone might be referring to one part of the pool, the diving tank, or one of the 25 yard pools when they're talking about the pool. But in other instances, people might be talking about the entire size of the pool. So this sort of characterization is around metric.

If we're talking about the gallons in the pool, that's a metric, we're adding up all of these gallons to get to how many gallons there are in the pool. Now you could be talking about the gallons of of water in the diving tank, the gallons of water in the 25 yard pools, or the gallons of water in the 50 meter pool configuration. What I told my friend is the power of step ups which I've talked about on other videos allows you to not have to replicate and show three different distinct pools or four different distinct pools or even five different distinct pools with the diving tank, 25 yard pool, another 25 yard pool, 50 metre pool or the entire thing, you could categorize all three of those as three independent things. But if you do that, then the gallons in the pool are getting duplicated in multiple different structures.

If you use the power of step ups, you could describe each one of those things if they are discrete, you could describe them as a portion of the pool. When you want to talk about the entire pool, you add together all three or all four or two of the portions that are needed. So if I made the lowest level, the the diving tank and it's categorization of the gallons in it, and I made another 25 yard pool and it's gallons and another 25 yard pool and it's gallons and say that I used to power Step up some I say I want to talk about the 50 meter pool. Well, we know that that's going to be in effect, the 25 yard gallon tank and the $25 gallon tank, and it's going to be a little bit more. And if I ever want to talk about the entire structure, I can still add together all four of those roads and get to the entire structure.

And in doing that, I've never had to replicate the metric involved. What this requires is, it requires getting down to what is really meant by language, and what are we counting and adding up and what goes into it but specific metric. It's not an easy process to do. But if you do it, then you start to get to clarity in what your data means. You start to decrease the duplication of data, and the reconciliation that goes off with different questions and asked about the same sorts of materials. The power of step ups can help us break the gap on many of our most difficult data structure needs.