Fierce competitions exist in both domestic and international markets. More and more executive managements realize that only through efficient information utilization, companies can then achieve maximum profits. As more and more information technologies are applied in different industries, companies nowadays have stored massive amount of valuable data, such as product, customer, accounting, marketing, inventory, and sales data. However, all these data often are located in various different operation systems; they have very different definitions and data structures. How to transfer these data into useable decision making supportive information is a key topic every enterprise is facing right now.
In IT industry, the Business Intelligence technology, or BI, is the golden key to solve the above mentioned problem. BI technology uses data warehouse to construct a base for storing aggregated data, which can be used in supporting traditional query and reporting functions, more advanced multidimensional data analysis and very complex data mining techniques. Many researches show that having business intelligence applications in an enterprise is one key part in keeping the competitive advantages in nowadays market environment.
● Data Mining: Identify Problems, Discover Hidden Patterns, and Realize True Intelligence – to Predict Future
Similar to mining precious stones, mining can also be applied on a data warehouse, and discover unexpected results from data. It is one step further to OLAP. For example, a business user is able to find out a saving product's performance of a specific region of last year by using multidimensional analysis. If the question is about why a certain saving product's performance of a specific region suddenly increased or decreased, or to ask how a saving product will do in a specific region, then data mining tools are able to help answering these questions.
In simple term, data mining utilizes statistical and analytical mathematical solutions, combining them with artificial intelligence techniques, such as self-learning and neuron networks, to search for inter relationship among data. Such relationships usually reflect positive or negative correlations between different data group. A careful observer is able to obtain insights from these correlations and to apply these insights into business operations, helping decision makers to gain competitive advantages before others do.