Data Mining Concepts for Business Intelligence

Companies around the world use data mining to extract various forms of data from a variety of sources and then analyze and translate that data into a meaningful form that can be used to compete in the global business sector. Considering that these companies amass large amounts of statistical data, it is important to have a master data management system in place to help extrapolate that data into useful forms of information. The information collected when mining data can be used as a process of knowledge discovery to see a map of interrelated business objects and their relationship values and for data wherein organizations can analyze the information gathered to increase revenues, reduce expenses and improve their bottom line profits.

These companies work with Extract, transform and load (ETL) data mining software and advanced tools as the main analytical system to manage this information. ETL tools give companies the ability to transfer enormous amounts of data from external data sources into one central location. Once transferred the records can be algorithmically cleansed, stored in a data mart or even a warehouse for statistical analysis at a later time. These tools give businesses the ability to do predictive analysis, draw conclusions between different relationships, interpret data from varying angles and to discover critical correlations. The process essentially gives big business economic and technical insight into hidden business trends or consumer patterns which increase or diminish business growth.

Typical sources of data can include any forms of information that can be retrieved such as facts and figures, random numbers, text based information and any other forms that can be statistically analyzed using a software computer program. Since large amounts of supplied data is usually processed, it is almost impossible for a human to process this volume of information without the aid of data mining software like Oracle.

What type of company employs the concepts, methods and strategies of data mining?

Multi-national with a vast consumer base and strong brand loyalty tend to use the benefits of data mining as a means to skillfully analyze relationship patterns between products, create forecast modeling and predictive models, and analyze relationship indicators between competitors and to get a better picture of consumer demographics as it relates to the type of products they provide. These methods also work well to help give companies a broader picture of how product positioning as compared to different pricing structures. These commonly include large financial institutions such as banks, loan companies, and insurance carriers and are widely used in business and consumer markets, which are often the target for PR and marketing firms.  The data collected through this mining process furnish major retailers with critical economic indicators that can be used to plot and chart increasing or deceasing consumer market patterns pertaining to a product or line of products.

A simple of example of this type of datamining takes place when companies use rebate forms, product warranty mail in devices and other forms of communications that prompts an action from the consumer. Whenever you shop at a major retail store, certain types of information are often collected on you. This data is stored using a database management system that the retailer can use to communicate special promotions, announce sales and introduce new products.

Where are data mining records stored?

Larger companies often have the computer resources to store large volumes of information, but smaller companies often rely on the application of data warehousing concepts of firms with significant investments in business intelligence software to warehouse data for them. Ideally the concept makes it possible to have once central location for record data that can be accessed through the internet from any location around the world.  This vast repository of information can be called upon whenever a retailer needs a fresh picture of their business analytics to determine how effective their marketing practices are.

This type of innovative ETL architecture gives the client access to the data around the clock through web-based control panels and onscreen interfaces. These services are an excellent for small and medium sized businesses. Some firms provide added management services beyond storage and retrieval. Screen scraping, data cleansing, text analysis and data mapping are common record management services available with these data warehousing firms.