Data Mining: Data Mining Software

Data mining is searching and examining data for useful information and analysis. It is a process of leveraging previously unknown information and turn that data into Business Intelligence. Data mining tools “mine” databases for hidden patterns—patterns that show the present and probable future purchasing behavior of customers. Savvy corporations then use these results to isolate and target high-end customers, to remarket their products, increase sales, expose fraud, and reduce costs.

Data mining is a strategy that uses On Line Analytical Processing (OLAP), algorithms, statistics, artificial intelligence, knowledge trees, nodes, models—an array of technical disciplines to uncover patterns of customer behavior and relationships in customer data that may be used to make reasonable assumptions about their future purchases.

Data mining extracts data that you did not know existed. Finding relationships between different patterns of information is the key. For the ultimate goal is to find a solution to a problem that will enhance your bottom line.

It is recommend that you begin by summarizing your available data statistically. Some also use visual aids, such as charts. The information will not jump out at you even after you have it organized. It must be analyzed for relationships between groups of data and a model must be built, tested, and verified. Therefore, whom you entrust to guide your data mining efforts must be able to master its key tools, such as statistics, so that they can recognize statistically significant patterns .

Data mining software automates the detection of relevant patterns in a customer’s behavior, integrates it with your data warehouses, and presents it in a relevant way for its users. From that process, you can build models to predict their future purchasing patters. If it were a retail model, you might see patterns that indicate that single women prefer a certain cosmetic over married women. Information like that tells you about the demographics of two quite different consumer groups and you can market to each accordingly.

Data Mining and Customer Relationship Management (CRM)

Customer relationship management (CRM) is software that manages the customer-service strategy of a company. The goal is to target sub-groups within your customer base for specific marketing campaigns. Since these massive databases house mission critical information about customers, accessing them is critical. However, the explosion of information has affected a company’s ability to analyze and leverage that data. CRM should not be carried out in drips and drabs, focusing on one goal at a time, isolating rather than integrating your information.

Thus, the need for a data mining capability that goes beyond the capabilities of relational databases. Not to mention that there are new technologies on the way that will bring this integration to an even wider business audience.

Data mining is the result of research and product development dating back to the 1960s with mainframe computers. The process continued with data access in the 1980s to data warehousing in the 1990s to the current technology that allow users to leverage data in real time.

A recent Gartner report determined that data mining is the number one technology that "will clearly have a major impact across a wide range of industries within the next three to five years." The report also listed data mining as the top 10 new technologies in which companies will invest during the next five years.

Technical Definitions

Episodic Mining examines information from one episode, such as a print campaign.

Strategic Mining targets a bigger unit of information to see if the results will yield insights into a specific issue, such as budget overruns.

Continuous Mining focuses on changes in patterns to uncover the factors that influence change.

Predictive Modeling is used to predict the future.

Discovery Modeling examines patterns in data and applies them to predict future behavior.

Forensic Analysis looks for unusual and specific cases.

Problem analysis examines a problem to see if data mining can solve it.

Data Preparation extracts data for use by a data mining algorithm.

Data Exploration reveals errors in the exploration process.

Pattern Generation uses rule induction and discovery algorithms to validate and interpret discovered patterns

Pattern Deployment uses the discovered patterns in decision support systems to produce reports and cleanse data.

Pattern Monitoring is a strategy to detect shifts in patterns as soon as possible.

Data Mining: Data Mining Software

Data Mining and Customer Relationship Management (CRM)

Technical Definitions

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