Business intelligence is information about an organization's past performance used to predict its future performance. The purpose of this information is to reveal trends that can grow business profit. Data mining allows users to sift through the enormous amount of information available in data warehouses, and in this filtering process, the gems of business intelligence are discovered.
Data mining is extracting hidden knowledge from large amounts of raw data. It can also be defined as extracting predictive information hidden in large databases. Data mining is not business intelligence itself. Business intelligence is usually drawn from the organizational data warehouse, which analyzes and reveals cumulative information about past performance. Data warehouses and business intelligence provide a method for users to predict future trends by studying past patterns of organizational data. Data mining is more intuitive; it allows for enhanced understanding beyond data warehouses. The application of data mining in an organization will serve as a guide to uncover trends and tendencies hidden in historical data. This application will also be used for statistical forecasts, data aggregations, and classifications.
Most companies collect, refine, and deduce massive quantities of data. Data mining techniques can be implemented rapidly on existing software and hardware platforms to enhance the value of existing information resources. They can be integrated with new products and systems as they become part of the system. When implemented on high-performance client/server or parallel processing computers, data mining tools can analyze massive databases to deliver answers to many different types of predictive questions.
Data mining software allows users to analyze large databases to solve business decision-making problems. Data mining tools predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. Data mining tools can answer business questions that traditionally were too time-consuming to resolve. Data mining is, in some ways, an extension of statistics, with a few artificial intelligence and machine learning twists thrown in. Like statistics, data mining is not a business solution but a technology.
When did data mining start?
Data mining techniques result from a long process of research and product development. This evolution began when business data was first stored on computers, continued with improvements in data access, and, more recently, generated technologies that allow users to navigate their data in real-time. Data mining takes the evolutionary process beyond access to past data and navigation in past data; it also processes the data for future prediction.
Data mining is ready for application in the business community because it is supported by three technologies that are now mature enough:
Massive data collection
Powerful multiprocessing computers
Data mining algorithms
The core components of data mining technologies have been under development for decades in research areas such as statistical data, artificial intelligence, and machine learning. Today, these methods' maturity, high-performance databases, and integration capabilities make these technologies practical and available.
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