Mark Flaherty (MF): Here is a good example. I was once talking in front of an audience consisting of technology providers, actually software companies, and they said, very often what we will do is we will provide evaluation licenses of our software to prospective customers and we give them 30 days worth of customer service.
Well, as far as the sales people are concerned, they are still prospects, they are not customers because they haven’t obliged by the rule of the definition of customer which is giving the company money in exchange for the use of a product. On the other hand, as far as the customer service department was concerned, those individuals were just as customers as anybody else who shared their money with company, because they were under the licensed evaluation agreement, they were provided full customer service or full customer support.
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Therefore, you have got an individual who in one context, under one definition is not a customer, but yet under another definition, that same individual is a customer. So you have got conflicting definitions, different representations, different meanings across the organization, and we end up with a lot of different underlying semantics for the same thing.
Let’s summarize. We started at the highest level. We looked at the infrastructure, the technical infrastructure, and that there is a level of complexity there. Then we looked at the layer on top of that, the application where there is some level of variation and differences across the ways that we represent the same concepts. And we looked at the actual structures that are being embedded within those applications. Then we looked at the meaning of those structures.
We see that as we continue to drill down, there is still more confusion or potential for confusion. And our objective is to look at what tech tools and techniques can we use to harness this representation of our infrastructure and of our environment, so that we can get some more global knowledge and therefore some control about the information that’s within our administrative boundaries.
And so, part of that approach is to look at modeling what we have. There are all sorts of different ways of modeling information or modeling data. Data models embody what might we called implied knowledge, but that knowledge often remains hidden inside the data when you haven’t actually sat down and kind of teased out or done some analysis of what the actual representations are. But if we continue to look at the knowledge that’s embedded within these de facto models, we can actually come up with the understanding of what’s been hidden within the data.
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Let’s look at our embedded meta-data. We can look for things like business terms, the facts that are established, the relationships between business terms, conceptual domains such as states of the United States and then data element concepts that rely on those conceptual domains. And in fact, this is from the bottom up. This gives us the capability of getting the right pieces in place. We can get a representative view in different ways of, what I call, this chain of information.