A result of implementing information systems such as enterprise resource planning or customer relationship management applications to automate business processes is the accumulation of vast amounts of data. The secondary use of this data, after recording a transaction such as a customer purchase, is the status reporting of transactions or events such as total sales for the month. Some organizations have taken the next logical step and have developed reporting environments which include operational data stores, data warehouses and data marts to assimilate data from disparate operating systems and applications. These reporting environments are extremely beneficial to individuals who need the ability to easily obtain timely and accurate information for decision-making purposes. Accessing information from these reporting environments often range from ad hoc queries to multidimensional analysis. While these forms of data analysis are excellent at answering the question or questions posed by the information consumer, they do not provide any other insight.

Individuals and organizations are recognizing that addition value may lie within the vast amounts of data that they are storing. By applying data mining techniques, which are elements of statistics, artificial intelligence and machine learning, they are able to identify trends within the data that they did not know existed. Data mining can best be described as a business intelligence (BI) technology that has various techniques to extract comprehensible, hidden and useful information from a population of data. This BI technology makes it possible to discover hidden trends and patterns in large amounts of data. The output of a data mining exercise can take the form of patterns, trends or rules that are implicit in the data. Through data mining and the new knowledge it provides, individuals are able to leverage the data to create new opportunities or value for their organizations. The following are examples of practical uses of data mining and the value it provides those who use this technology to mine their data.

Fraud Detection

Within the financial services industry, credit card issuers have been using data mining techniques to detect potentially fraudulent credit card transactions. When a credit transaction is executed, the transaction and all data elements describing the transaction are analyzed using a sophisticated data mining technique called neural networks to determine whether or not the transaction is a potentially fraudulent charge based upon known fraudulent charges. This data mining technique yields a predictive result. While the prediction may or may not be correct, this technique requires the system to learn various patterns and characteristics of transactions so to fine-tune its prediction capabilities. By utilizing data mining, credit card issuers have decreased and mitigated losses due to fraudulent charges.

Inventory Logistics

In retail, every time merchandise is handled it costs the merchant. By incorporating data mining techniques, retailers can improve their inventory logistics and thereby reduce their cost in handling inventory. Through data mining, a retailer can identify the demographics of its customers such as gender, martial status, number of children, etc. and the products that they buy. This information can be extremely beneficial in stocking merchandise in new store locations as well as identifying “hot” selling products in one demographic market that should also be displayed in stores with similar demographic characteristics. For nationwide retailers, this information can have a tremendous positive impact on their operations by decreasing inventory movement as well as placing inventory in locations where it is likely to sell.

Defect Analysis

Quality control is critical to the success of any manufacturing company. Through the use of data mining techniques, manufacturers are able to identify the characteristics surrounding defective products, such as day of week and time of the manufacturing run, components being used and individuals working on the assembling line. By understanding these characteristics, changes can be made to the manufacturing process to improve the quality of the products being produced. High-quality products lead to improved reputation of the organization within its industry and help to drive sales. In addition, profitability improves through the reduction of return materials allowances and field service calls.

Focused Hiring

Some employers use data mining techniques to understand the characteristics of their top performing individuals. By understanding the characteristics of this group such as education, years of experience, skills and personality traits, a hiring profile can be established to help recruit and hire individuals who possess similar characteristics as their best- performing individuals. While this technique has been used, one must realize that profiling is based upon historical data, which may not be indicative of future top-performing individuals due to changes in social, economic and environmental conditions.

A few practical uses of data mining have been highlighted. However, there are many other ways that data mining can be applied to your corporate data, which will provide greater insight into your business or operations. Understanding what data mining is, why one would apply it and the corresponding benefits are important in advancing the use of data mining within your organization. The value that data mining provides is knowledge about patterns or events that you may not know. As data storage technology advances and information systems continue to collect and process data, a treasure is amassing that is waiting to be discovered. Are you ready to stake your claim and find your riches?

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