Business intelligence data mining techniques
Data mining can be seen as a series of automatic search procedures that are used for ascertaining any actionable insights from large sets of high-dimensional data clusters. Data mining uses advanced techniques garnered from machine learning, pattern recognition and statistics. It is automated business intelligence (BI) solutions and scalability which is the driving force between distinguishing data mining from the myriad statistical modeling and machine learning applications. The applications of BI that data mining targets includes:
- Fraud detection – discovering criminal activity online and in real-time
- Customer retention – being able to take proactive action when a customer leaves and discovering why the customer is unhappy
- Targeted monitoring – discovering which promotions work best and targeting the customers based on their historical purchases
- Risk Analysis – customers can be labeled into groups according to their credit and insurance history, furthermore a predictive model can be built for future risk classification
Predictive modeling is undoubtedly the most commonly used of all data mining techniques and is an excellent method for creating decision supporting solution, as well as building on existing business intelligence systems. Ultimately, a robust and heavily automated forecasting system is created for the data storage environments.
The data mining process is performed in three necessary steps. Historical data is mined in order to create a pattern which actively predicts all future behaviours – this includes but is not limited to credit history, customer profitability, transactional history and direct mailers. This model then scores future transactions based on the modeled behavior. The final step is the system acting on an optimization strategy for the future business objectives.
Further key areas that data mining can be applied to include customer relationships, business process efficiency and profitability – data mining for BI allows a company to discover potential new clients, increase their profit margins and to decrease costs, yet not at the price of production methods.
There are many more business applications with in-depth techniques that BI data mining analysts spend years perfecting.
