A One Way Street to Clarity and Simplicity - More on Mapping a taxonomy to a taxonomy

In my last post, I did not elaborate too much on rules sets that contain the logic to map from one space to another. These rules sets are interesting in that usually when you map from a complex multi-dimensional taxonomy space to a simpler domain specific taxonomy space it is a one way mapping. A good way to think of it is to think about how photography works. A camera has a lens that focuses an image of a three dimensional space onto a two dimensional piece of film. Needless to say, there is a loss of information when the camera takes a picture because the resulting image is just a single view of a three dimensional image. Can we recreate the three dimensional space from our two dimensional photo? Not really, though I have seen some software that guess. Nevertheless, we still love photography. I was just looking at my wedding pictures last night, and in a way photography gives us a clearer vision of our shared reality from an authorial viewpoint.Great portraits or landscapes captures a moment and gives it clarity.

Let's get back to our idea of rules sets (our taxonomy camera), and how they map from from a complex multi-dimensional taxonomy space to a simpler domain specific taxonomy space. We develop the simpler taxonomy to give us a perspective of a domain which gives us vision of clarity and simplicity. We use it to give an authorial view of certain business sectors in a way that our more general purpose taxonomy can not do.

For those with a mathematical bent, I can say that our rules sets are prioritized rules and the fact that we have rules with greater priority than other rules makes these rules sets one way, and collapse the information to a simpler view. If we ran our rules sets on companies classified using the complex taxonomy to get the simpler classification, and then ran the rules sets in reverse on the simple taxonomy to get the categorizations in the complex taxonomy, the original complex classification will not be the same as the derived categorizations.

Mapping a taxonomy to a taxonomy

In my last post, I talked about "meta-terms" which mapped a commonly used expressions to nodes in multiple trees. This concept could be taken much further. When our team built our four dimensional taxonomy, our goal was to be able to classify any business, and to find similarities between companies even though traditionally they  would be considered to be operating in different arenas. My favorite example is to look at Intuit which creates financial software for the consumer, and compare it to H.R. Block which provides a financial services for consumers. In the tax arena, they both provide help to people doing their taxes, and compete directly. Our taxonomy categorizes Intuit as a consumer software company for taxes, while H.R. Block is a consumer service company for taxes. As you see these companies overlap on what they do, and who they do it for, but not on how they do it. Interestingly enough, Intuit started offering a professional help service and H.R. Block started offer a software package.

What this brings up is that our taxonomy is complicated. Our team produces reports on Merger and Acquisition activity in a variety of segments (http://mandasoft.com), and each of these business segments like to break down using their own taxonomies specific to their domain. How do we reconcile the need for a taxonomy with nodes that can be used cross multiple domains, while needing to have easy to understand domain specific terms in a given domain?  The way I like to see this problem is that we have a vocabulary that works great when looking at the business world at the 50,000 foot level, but when we get down into trenches, the terms start to look vague and confusing at the lower altitudes. The way we solved this was by building a system to create 50 ft level simple taxonomies for specific domains (e.g. healthcare media and software). We then categorize each business using the 50,000 foot level taxonomy, and we then have rules sets that map from 50,000 ft level taxonomy to the 50 ft level taxonomy. The utility is especially noted when we create multiple domains with their own rules sets  (e.g. healthcare media and software, and  Cloud Computing) and a business which may reside in both domains, only needs to be categorized once at the 50,000 ft level taxonomy. We can create as many domains as we need and not have to reclassify companies as our domain views evolve!

Why the North American Industry Classification System (NAICS) is no good?

When navigating a database of businesses, you need a taxonomy in order to find companies in an industry you are interested in. You would think that the NAICS would be ideal, however in practice none of the commercial databases use it. The reason is found on the US Census web site.


As stated on US Census web site, "The North American Industry Classification System (NAICS) is the standard used by Federal statistical agencies in classifying business establishments for the purpose of collecting, analyzing, and publishing statistical data related to the U.S. business economy." and "NAICS was developed under the auspices of the Office of Management and Budget (OMB), and adopted in 1997 to replace the Standard Industrial Classification (SIC) system. It was developed jointly by the U.S. Economic Classification Policy Committee (ECPC), Statistics Canada , and Mexico's Instituto Nacional de Estadistica y Geografia , to allow for a high level of comparability in business statistics among the North American countries."


The reason that it is not useful is that it is used to track broad trends. When you need to analyze business segments of our market you will see that a finer grained and richer taxonomy is needed. I have started this blog to explore this issue as my team and I continue to tackle the issues. Stay tuned.