Practical applications of data mining Essay

When user knows exactly what he or she wants from data warehouse, then he uses query tool to get the required information. Whereas when the user has vague idea of what he or she wants, then he goes for data mining from data warehouse. Query tools are useful to query, analyze, edit, search, report, sort, find, merge and summarize the data. Whereas data mining deals with unknown data and finds patterns from previously unused data. Data mining uses statistical and machine learning techniques. It finds the relationships or associations between different elements. Data mining is a knowledge discovery process. It converts the huge data into useful business intelligence.

Usually Query tools provide GUI (Graphical User Interface) based front end to interact with database. They support SQL, TSQL and PL/SQL queries. Query tools come with query editor, import/export facilities, report and graph generation tools. Using these tools users are able to insert, delete, update, print and modify the table rows and fields in a database. Data mining works on previously unknown but useful data of the organization. Basically data mining involves discovery process.

Data mining is useful specifically in marketing function of the organization. It can be used to increase revenues and profits. Current day organizations are gathering data from different sources such as surveys, interviews, social media (such as Facebook, Twitter, and WhatsApp) and CRM systems which interact with customers. Using the data gathered, marketing department can help in increasing return on investment, sales forecasting, reducing cost of marketing campaigns, and increasing customer satisfaction. Customer profiling and market segmentation using data mining helps the marketing department to hit the exact targets. Marketing departments in organizations use data mining tools and software.

Customer profiling, a major application of data mining, finds the patterns of customer purchases, hobbies, age, income, gender, etc details. Data mining helps in finding details about new customers, their needs and expectations. This helps the organizations in retaining the customers for long time. Using data mining, marketing department can also find the best customers. This helps them in predictions and where to focus the marketing campaigns. Data mining helps finding customer behavior and purchase patters which help the organizational revenues.

Different practical applications of data mining include basket analysis, customer segmentation, database marketing, customer loyalty identification, sales forecasting, CRM records analysis, call analysis, production planning, marketing plan, merchandise planning, and market segmentation. Credit marketing and bank fraud detection also be found from data mining. For example, organizations can also find which customer has used the support and warranties many times. Competitive intelligence and strategic planning are also other applications of data mining. Data mining can be used in loan applications decision making models, identifying stock trends, astronomy, meteorology, biology, healthcare, medicine, geology, sports, tax fraud detection, monitoring money laundering, molecular biology and cancer research. Data mining can answer questions such as what products are more profitable among MarutiSuzuki car models? What are the major regions generating revenues for Hindustan Lever? Which product is most liked by users?