Question

Data mining helps companies discover patterns and relationship in raw data to help make better business decisions. Business intelligence is the actual decision-making process that is driven by data and helps professionals generate, aggregate, analyze, and visualize data.

Describe 2-3 issues companies may face when data mining does not meet business intelligence needs.

Answer 1

Let us first understand the basic meaning of data mining which is as follows:

Data Mining Meaning:

Data Mining means the mining of data, i.e., the data which is analyzed, evaluated and measured according to the needs and wants of the company. It is examining the database to generate new useful information which will help the organization in the long term. Data Mining is a process of examining the data through various algorithms, machine process, statistical tools, and many other methods to evaluate the data.

Let us now understand the issues which companies or businesses face when data mining does not meet business intelligence need:

• Efficiency and Scalilibiliy of data algorithms: Data Mining leads to the extraction of data from large data. Many companies fail to extract the data in an effective manner. The lack of effectiveness in data extraction becomes a major reason for the nonfulfillment of the requirements of the business house. The data should be effectively evaluated and scaled which helps the evaluator in scaling the data.
• Parallel, distributed, incremental data mining: The data in this process is divided into small portions that are processed in their portions itself. These portions are further distributed for evaluation through algorithms and methods. The processed data is then further distributed for the increment process. This incremental process updates the data rather than updating from scratch then these portions are merged to make the data into information. But when this data is evaluated there are chances that the data is wrongly interpreted. It also depends on the technology used by the company to increment the data process.