Bryan Houston is a data mining specialist from our sister company, The Intelitech Group.

To understand the mountains of data we accumulate doing business, let us compare it to some other mountains. The mountain ranges that surround the Pacific Ocean are known as the Pacific Ring of Fire. This arc stretches from New Zealand, along the eastern edge of Asia, north across the Aleutian Islands of Alaska, and south along the coast of North and South America. The Ring of Fire is composed of over 75% of the world's active and dormant volcanoes. These mountains, while all similar, contain different materials, were formed over varying lengths of time, and continue to grow in unique ways. Two of these volcanoes that have attracted a lot of media attention historically and even recently are Mount Kilauea in Hawaii and Mount Saint Helens in Washington State. While both of these mountains are the same in that they are active volcanoes, it would be foolish for geologists to neglect to give each of them unique respect. 

The mountains in the Ring of Fire are comparable to the mountains of data we have in our databases. They have been formed differently, contain unique arrangements of data, and if not treated with unique care, have the potential to produce disastrous results. However, unlike the mountains in the Ring of Fire, we not only want to monitor our mountains, we want to mine them.  So what do we do, how do we begin?

Just as the mountains in the Ring of Fire have a similar makeup, the mountains that we want to query contain some basic ingredients. The key ingredient in our mountains is data, and the key to the data mining process is harnessing the power that is stored in the ones and zeros.  It is when our data is turned into information that we have power—the power to see the past, the power to predict the future, and the power to make decisions.  

But what is the difference between the mountain of data we are looking at and the power of information we hope to find?  Data and information are two words in the English language that are often used interchangeably; it is no wonder that we have a hard time distinguishing the two! There are some fundamental differences between data and information.  For example, the address of a client is data, but that same address given to the billing department is information. The phone number of a debtor is data, but given to a collector, it becomes information. So what makes the same string of characters boring data in one instance, and powerful information in the other? The relationship between data and information is not the string of characters, but rather, the relationship of those characters to the decision at hand.

As we contemplate the place of data and information in our own organizations, it becomes quite apparent that all the data we store could be used as information. What may be data to us now could quickly be information to us later. Often we look at our database and become overwhelmed when trying to make sense of it all.  We need to look at our mountain of data with a purpose and ask ourselves some questions: 

• Why do we store this data? 
• What are some decisions we can make using this data? 
• Are we making those decisions?  If not, why not? 

We store data with the idea that it will help us at some point in the future. If we are unable to see the benefit in the future, chances are our mountain is bigger than it needs to be. With the vast amounts of data available, it is easy to add more and more data. However, the power and reward of data mining is not in the size of your database, but in your ability to take that data and turn it into information to answer questions that help your organization. It becomes our responsibility to not only know what data is being stored, but to determine the questions that will need to be answered. It is when we can do this that our data bank will change into an information system.