Classification of Corporate Data Part 1 of a 4-Part Series

Originally published April 3, 2014

Most of us are so ensconced in corporate data that we do not spend much, if any, time thinking about the world of corporate data. That is sort of like living in Aspen, Colorado, and never noticing the mountains. Taking time to reflect on the larger picture can yield some real benefits.

There are many ways to look at corporate data. One simple division of corporate data is between structured and unstructured data. Structured data is data that is repetitive and predictable. Typically structured data is data that is divided up into records, attributes, keys and indexes. Unstructured data is data that is organized in a fashion that is not comprehensible to the computer. The English language is structured (as your English teacher tried to convince you). But the structure of the English language is not immediately apparent to a computer. So one division of corporate data is between structured data and unstructured data.

Now let’s look at unstructured data. Unstructured data can be broken into two major classifications – repetitive unstructured data and nonrepetitive unstructured data. Repetitive unstructured data is data that is voluminous and highly repetitive. Nonrepetitive unstructured data is data that is voluminous and nonrepetitive.

Three Major Classifications of Corporate Data

These are three major classifications of corporate data – structured corporate data, unstructured repetitive corporate data and unstructured nonrepetitive corporate data.

In order to understand these three classifications of data, let’s look at some examples.

Every time a sale is made or a sales contact or a bank withdrawal is made, a transaction is generated. Typically the transaction ends up as a structured record. There are literally thousands of examples of structured data. Bank transactions, sales, inventory management and manufacturing production – each of these events and more result in structured records.

Unstructured repetitive records form perhaps the largest bulk of data in the corporation. When an organization does analog processing, the activities being measured by analog processing become unstructured repetitive records. There are plenty of other kinds of unstructured repetitive records. Records of telephone calls or records of the transmission of an email are examples of unstructured repetitive data. The term repetitive comes from the fact that the data is highly repetitive, both in terms of content and in terms of structure.

The third classification of corporate data is unstructured nonrepetitive data. There are many examples of unstructured nonrepetitive data. There are emails, healthcare records, warranty claims, corporate contracts, call center interchanges, marketing responses and many more types of this data. With unstructured nonrepetitive data, there are many unstructured records but any one record is probably quite different from any other record when it comes to content and structure.

Gaining Value from Corporate Data

These then are some of the ways that corporate data can be classified. So why does this classification make a difference? It makes a BIG difference when it comes to using the data to get value out of it. Stated differently, in order to get value out of corporate data, there are entirely different approaches to achieving value, depending ENTIRELY on the data that is being analyzed.
 
  • Bill InmonBill Inmon

    Bill is universally recognized as the father of the data warehouse. He has more than 36 years of database technology management experience and data warehouse design expertise. He has published more than 40 books and 1,000 articles on data warehousing and data management, and his books have been translated into nine languages. He is known globally for his data warehouse development seminars and has been a keynote speaker for many major computing associations.

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