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JP Morgenthal

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That's Classified Information

That's Classified Information

With the advent of computer storage, business has become increasingly more reliant on electronic information as a major source for maintenance and continued growth. The information we store electronically tells us what customers like and dislike, how much material to buy, and where we spend our money. Typically all this information is stored and accessed directly through applications. These applications provide us access to this data through application programming interfaces or embedded reporting systems. Surprisingly, with all this dependency on data and the applications that manage them, few businesses have focused on ensuring that this data is turned into information, the difference being that information has inherent contextual value versus being raw, undefined content.

Of course, with the development of Web services and other integration technologies, we have the ability to turn our data into information on a singular and constrained basis. That is, we can turn a diverse data set into information around a particular use. For example, data from the accounting system can be coupled with data from the inventory system to generate invoices for our customers. However, these exercises don't often take the opportunity to classify the data as it's turned into information so it may be leveraged for other uses, such as vendor-managed inventory or supply-chain management. Instead, each of the aforementioned business functions would most likely use similar aggregation techniques to create the invoice to complete their tasks.

Classification of information is perhaps one of the most underrated and underused processes surrounding business data. Classification offers the business an ability to look for patterns of use surrounding this data, as well as access and search for this data more effectively in other downstream processes. This isn't a novel concept; intelligence organizations have been qualifying and classifying information for years. The difference now is the availability of XML, XML tools, and XML standards, such as Resource Definition Framework (RDF) and XML Topic Maps (XTM). These technologies make classifying and organizing data, turning it into information, affordable and easy.

Moreover, using XML to classify data provides additional opportunities to add important missing contextual metadata that allows businesses to identify the relationships between various sets of information. In our invoice example, we could develop a new data set that represents the relationship between inventory data and accounting data so we can dynamically explore and relate this data on demand. We don't need to have statically defined relationships that are bound to a single purpose, but instead dynamically defined relationships that can be reused for multiple purposes.

So what does it mean to classify information in this manner? XTM, for example, defines a framework that comprises topics, associations, and occurrences. The topic is a real-world subject, such as a book or a person. Topics are named and have an instance that is URI addressable called the occurrence. These topics also participate in associations with other data. Here's an example of a topic map:

<topic id="XMLJ">
<instanceOf><topicRef xlink:href="#magazine"/></instanceOf>
<baseNameString>XML Journal</baseNameString>
<resourceRef xlink:href="http://www.sys-con.com/xml"/>

In this example a map called XMLJ is described as being an instance of a magazine named XML Journal and having a reference http://www.sys-con.com/xml. While this may seem excessive to state a simple assertion, it's extremely powerful to be able to describe the relationships between sets of atomic data in this way. Because of this nomenclature, we can understand the relationship between the name XML-Journal and the Web site http://www.sys-con.com/xml. We can now answer this question of this information regardless of whether the requesting agent wants to understand what the URL points to or what the URL of the magazine is.

If we extend this paradigm to our internal data sets, we have a very powerful facility that will allow us to explore the relationships between sets of data in our stove-piped applications and thus provide effective and useful reuse and integration.

More Stories By JP Morgenthal

JP Morgenthal is a veteran IT solutions executive and Distinguished Engineer with CSC. He has been delivering IT services to business leaders for the past 30 years and is a recognized thought-leader in applying emerging technology for business growth and innovation. JP's strengths center around transformation and modernization leveraging next generation platforms and technologies. He has held technical executive roles in multiple businesses including: CTO, Chief Architect and Founder/CEO. Areas of expertise for JP include strategy, architecture, application development, infrastructure and operations, cloud computing, DevOps, and integration. JP is a published author with four trade publications with his most recent being “Cloud Computing: Assessing the Risks”. JP holds both a Masters and Bachelors of Science in Computer Science from Hofstra University.

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