There is misconception about data mining and data warehousing. Both of them are related to business intelligence tools that are used for turning data into effective knowledge. Many IT professionals use them as synonyms with some differences between the tools. Although the goals of both are related, data mining and data warehousing use different methods and processes for achieving these goals.
Data mining software is one of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.
This tool is used by companies with a strong consumer focus – retail, financial, communication, and marketing organizations. This tool allows the companies to determine relationships among “internal” factors such as price, product positioning, or staff skills, and “external” factors such as economic indicators, competition, and customer demographics. That enables them to determine the impact on sales, customer satisfaction, and corporate profits. Also, it enables the companies to “drill down” into summary information to view detail transactional data.
Data warehousing describes the process of building decision, support systems and a knowledge-based applications architecture and environment that supports both everyday tactical decision making and long-term business strategizing. The Data Warehouse environment positions a business to utilize an enterprise-wide data store to link information from diverse sources and make the information accessible for a variety of user purposes, most notably, strategic analysis.
So the two application types are similar because they rely on historical data to drive profitability in the future. However, the methods the two employ are different, and require different skill sets of the analysts that analyze data. Unfortunately they both lack delivering a predictive model 🙂
Thank you for your attention!