DATA MART AND DATA MINING

Presented by, Lekha

Ashmitha Ria Roy

Sindhu.U Perumal

K Archana

DATA MINING

Data mining, also known as "knowledge discovery," refers to computer-assisted tools and techniques for shifting through and analyzing these vast data in order to find trends, patterns, and correlations that can guide decision making and increase understanding.

Data mining covers a wide variety of uses, from analyzing customer purchases to discovering galaxies.

In essence, data mining is the equivalent of finding gold nuggets in a mountain of data. The monumental task of finding hidden gold depends heavily upon the power of computers.

Advantages of Data Mining

Marketing / Retail Data mining helps marketing companies to build models

based on historical data to predict who will respond to new marketing campaign such as direct mail, online marketing campaign and etc. Through this prediction, marketers can have appropriate approach to sell profitable products to targeted customers with high satisfaction.

Data mining brings a lot of benefit s to retail company in the same way as marketing. Through market basket analysis, the store can have an appropriate production arrangement in the way that customers can buy frequent buying products together with pleasant. In addition, it also help the retail company offers a certain discount for particular products what will attract customers.

Finance / Banking Data mining gives financial institutions

information about loan information and credit reporting. By building a model from previous customer’s data with common characteristics, the bank and financial can estimate what are the good and/or bad loans and its risk level. In addition, data mining can help banks to detect fraudulent credit card transaction to help credit card’s owner prevent their losses.

Manufacturing By applying data mining in operational

engineering data, manufacturers can detect faulty equipments and determine optimal control parameters.

Governments Data mining helps government agency by

digging and analyzing records of financial transaction to build patterns that can detect money laundering or criminal activity.

Implications

Marketers can effectively target the wants and needs of specific consumer groups by analyzing data about customer preferences and buying patterns.

Hospitals use data mining to identify groups of people whose healthcare costs are likely to increase in the near future so that preventative steps can be taken.

Disadvantages of data mining

Privacy Issues The concerns about the personal privacy have been

increasing enormously recently especially when internet is booming with social networks, e-commerce, forums, blogs…. Because of privacy issues, people are afraid of their personal information is collected and used in unethical way that potentially causing them a lot of trouble. Businesses collect information about their customers in many ways for understanding their purchasing behaviors trends. However businesses don’t last forever, some days they may be acquired by other or gone. At this time the personal information they own probably is sold to other or leak.

Security issues

Security is a big issue. Businesses owns information about their employee and customers including social security number, birthday, payroll and etc. However how properly this information is taken is still in questions. There have been a lot of cases that hackers were accesses and stole big data of customers from big corporation such as Ford Motor Credit Company, Sony… with so much personal and financial information available, the credit card stolen and identity theft become a big problem.

Misuse of information/inaccurate information Information collected through data mining intended for marketing or ethical

purposes can be misused. This information is exploited by unethical people or business to take benefit of vulnerable people or discriminate against a group of people.

In addition, data mining technique is not perfectly accurate therefore if inaccurate information is used for decision-making will cause serious consequence.

Data Mart 

A is an index and extraction system. Rather than bring all the company's data into a single warehouse, the data mart knows what data each database contains and how to extract information from multiple databases when asked.

Creating a Data Mart can be considered the "quick and dirty" solution, because the data from different databases is not scrubbed and reconciled, but it may be the difference between having information available and not having it available.

Data Marts Advantages

The implementation of data marts enable users to gain faster access to common data utilizing a technique called dimensional data modelling, which optimizes data for reports.

  For example, since data is prepared in common format, users with little or not training at all, can browse a data mart and obtain information as needed.  

Data marts can improve end user response time, as it contains raw data which allows computer systems to focus on a single task, thus, improving performance.   As opposed to OLTP systems, data marts can also store historical data which enable users to analyze data trends.  

Moreover, data marts are not as expensive and complex as data warehouses to setup and implement because technical issues are not so difficult to resolve 

Data Marts Disadvantages

Alike any other system, data marts have many issues including functionality, data size, scalability, performance, data access, and consolidation.  

Since data marts can be broken into different departments to focus on their individual needs.   This approach makes data access, consolidation, and cleansing very difficult.

  For instance, when a company has a data mart for each of its departments including sales, inventory, tracking, shipping, receiving, and production.  

Combining revenue information from each of these departments into a single data mart can be overwhelming and confusing, due to the volume of data to be analyzed

NEED FOR DATA MARTS

The majority of databases are designed to hold the current data needed by an organization to perform its business activities. In a business organization, current data might include information concerning bills due, inventory levels, and product orders, and would most likely be contained in a billing/inventory/order database. In most cases, the minute that data become outdated, they are deleted from the database. For example, once a bill is paid, data about the bill is removed. Fortunately, many organizations have realized the value of being able to analyze historical data in order to discover patterns of behavior and predict future trends. For example, analyzing historical data can tell a retailer what items were ordered, in what quantities, and by which customers.