Data Mining: What is it?

Businesses have lots of data, but what they lack are answers to their most common questions and concerns. Data mining is the best software available to address these concerns. Data Mining automates the extraction of information from databases that can be used to predict patterns of behavior. It is able to predicts future trends and behaviors, using statistical analysis and modeling techniques, to find patterns and relationships hidden in your organization's databases.

Data mining permits users to analyze large databases to facilitate sound business decisions. Data mining software utilizes captured customer information to build a model of customer behavior that could be used to predict which customers might respond to new marketing efforts, for example. This information is then fed into your marketing mix so that targeted customers are matched with the right products.

Data mining helps you navigate through layers of related and unrelated data to find relationships in your data to help you maintain a high-standard of customer relationships. It can tell you what you did not know as well as what might happen. A model can then be fashioned that will predict the behaviors that you seek among the targeted customers whom are mostly likely to respond to your marketing efforts.

A Discovery Model is used when you know the answer and then apply it to an unknown situation. You start by sifting through data looking for patterns and trends that will help you come up with some conclusions. These tools can discover specific types of information with very little (or no) guidance from the user. Data mining results are not arbitrary, however. It is designed to yield as many profitable facts as possible in the shortest amount of time.

Using data mining in this way reduces costs and improves the value of customer relationships. Businesses can then focus on customers and prospects and marketing strategies to best reach them.

However, there is a downside to data mining. "Information overload" is one of the most common. With too much raw information, there is the danger of imposing patterns when none exist. The imposition of misleading or irrelevant data is known as "data dredging" or "overfitting the model."

Also, flawed methodology can undermine your data mining efforts. For example, if you're working from questionable data, it follows that your final analysis will be skewered. You must make sure that your data has been examined, cleaned, audited and purchased from trusted vendors. Inferior technology and incompetent employees are the leading causes of bad data. You must insure that your interfaces and data translation are user friendly and that your employees double-check their data entries and their work Having everyone on the same page from the get-go is mandatory.

If used correctly, data mining discovers unseen patterns in data and forecasts the future purchasing habits of customers. But who you put in charge of running your data mining is critical. They must possess highly specialized IT skills as well as display proficiency in some of data mining's more specialized areas, such as statistics, alogrithms and model building, to name just a few. Even then, your role as a non-technical specialist is needed, because you must test the experts' model against existing truths about the workings of the marketplace.

In other words, do your homework.

Sponsored Links