How data mining and data warehousing are related to ERP?

Data mining and Data warehousing – Modeling the investigated system and discovering relations that connect variables in a database are the subjects of data mining. Modern data mining system self learn from the previous history of the investigated system, formulating and testing hypotheses about the rules, which this system obeys.

When concise and valuable knowledge about the system of interest had been discovered, it can and should be incorporated into some decision support system, which helps the manager to make wise and informed business decisions.

Below are all the questions that can probably be answered if information hidden among megabytes of data in your database can be found explicitly and utilized.

• What goods should be promoted to this customer?
• What is the probability that a certain customer will respond to a planned promotion?
• Can one predict the most profitable securities to buy/sell during the next trading session?
• Will this customer default on a loan or payback on schedule?
• What medical diagnosis should be assigned to a particular patient?
• How large are the peak loads of a telephone or energy network going to be?
• Why does the facility suddenly start to produce defective goods?

Implementing a data warehouse provides significant benefits – some tangible, some intangible. The benefits include the following:

• More cost-effective decision-making:
  A data warehouse allows reduction of staff and computer resources required to support queries and reports against operational and production databases. This typically offers significant savings. Having a data warehouse also eliminates the resource drain on production systems when executing long running, complex queries and reports.
• Better enterprise intelligence:
  Increased quality and flexibility of enterprise analysis arises the multi-tiered data structures of a data warehouse that support data ranging from detailed transactional level to high-level summary information. Guaranteed data accuracy and reliability result from ensuring that a data warehouse contained only “trusted” data.
• Enhanced customer service:
  An enterprise can maintain better customer relationships by correlating all customer data via a single, data warehouse architecture.
• Business engineering:
  Allowing unlimited analysis of enterprise information often provides an insight into enterprise processes that may yield breakthrough ideas for reengineering those processes. Just defining the requirements for data warehouse could results in better enterprise goals and measures. Knowing what information is important to an enterprise will provide direction and priority for reengineering efforts.
• Information system reengineering:
  A data warehouse that is based upon enterprise-wide data requirements provides a cost-effective means of establishing both data standardization and operational system inter-operability. Data warehouse development can be an effective first step in reengineering the enterprise’s legacy system.