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MB0047 Q3. What are Value Chain Analysis & describe its significance in MIS? Explain what is meant by BPR? What is its significance? How Data warehousing & Data Mining is useful in terms of MIS?

Answer:
Business Process Re-engineering
The existing system in the organization is totally reexamined and radically modified for incorporating the latest technology. This process of change for the betterment of the organization is called as Business process re-engineering. This process is mainly used to modernize and make the organizations efficient. BPR directly affects the performance. It is used to gain an understanding the process of business and to understand the process to make it better and re-designing and thereby improving the system.
BPR is mainly used for change in the work process. Latest software is used and accordingly the business
procedures are modified, so that documents are worked upon more easily and efficiently. This is known as workflow management.

Significance of BPR
Business process are a group of activities performed by various departments, various organizations or between individuals that is mainly used for transactions in business. There may be people who do this transaction or tools. We all do them at one point or another either as a supplier or customer. You will really appreciate the need of process improvement or change in the organizations conduct with business if you have ever waited in the queue for a longer time to purchase 1 kilo of rice from a Public Distribution Shop (PDS-ration shop). The process is called the check-out process. It is called process because uniform standard system has been maintained to undertake such a task. The system starts with forming a queue, receiving the needed item form the shop, getting it billed, payment which involves billing, paying amount and receiving the receipt of purchase and the process ends up with the exit from the store. It is the transaction between customer and supplier.
The above activities takes place between the customer and supplier which forms the process steps this example explains the business process. The business process may be getting admission to the college and graduating from the college, building house, and implementing new technology to an organization (Example EDUNXT in SMUDE), etc
A Process can be represented by triangle and following figure shows continuous process of Business.
Business process reengineering is a major innovation changing the way organizations conduct their business. Such changes are often necessary for profitability or even survival.

BPR is employed when major IT projects such as ERP are undertaken. Reengineering involves changes in structure, organizational culture and processes. Many concepts of BPR changes organizational structure. Team based organization, mass customization; empowerment and telecommuting are some of the examples. The support system in any organization plays a important role in BPR. ES, DSS, AI (discussed later) allows business to be conducted in different locations, provides flexibility in manufacturing permits quicker delivery to customers and supports rapid paperless transactions among suppliers, manufacturers and retailers. Expert systems can enable organizational changes by providing expertise to non experts. It is difficult to carry out BPR calculations using ordinary programs like spreadsheets etc.
Experts make use of applications with simulations tools for BPR. Reengineering is basically done to achieve cost reduction, increase in quality, improvement in speed and service. BPR enable a company to become more competitive in the market. Employees work in team comprising of managers and engineers to develop a product. This leads to the formation of interdisciplinary teams which can work better than mere functional teams. The coordination becomes easier and faster results can be achieved. The entire business process of developing a product gets a new dimension. This has led to reengineering of much old functional process in organizations.
Data ware house is center part of data repository. Data warehousing provides a strategic approach to all the business. Data warehouse is broadly famous for its characteristics like:
a. Subject oriented: Data warehouse has the ability to analyze the data. The ability to define by subject matter makes DW subject oriented.
b. Integrated: This resolves the problems of conflicts and inconsistencies existing in the units of measure.
c. Non volatile: Once the data is entered in the warehouse it shall not change. This characteristics is very important because after all the purpose of heuristic data is for future use.
d. Time variant: The data warehouse focus on change over time. To discover new trends in business, analysts need large amount of data which is contrasting to OLTP (Online transaction Processing) which works on heuristic data.

Data Warehousing – Data Warehouse is defined as collection of database which is referred as relational database for the purpose of querying and analysis rather than just transaction processing. Data warehouse is usually maintained to store heuristic data for future use. Data warehousing is usually used to generate reports. Integration and separation of data are the two basic features need to be kept in mind while creating a data warehousing. The main output from data warehouse systems are; either tabular listings (queries) with minimal formatting or highly formatted "formal" reports on business activities. This becomes a convenient way to handle the information being generated by various processes. Data warehouse is an archive of information collected from wide multiple sources, stored under a unified scheme, at a single site. This data is stored for a long time permitting the user an access to archived data for years. The data stored and the subsequent report generated out of a querying process enables decision making quickly. This concept is useful for big companies having plenty of data on their business processes. Big companies have bigger problems and complex problems. Decision makers require access to information from all sources. Setting up queries on individual processes may be tedious and inefficient. Data warehouse may be considered under such situations.
Data warehouse Architecture:

Data warehouse Architecture
Data Mining – Data mining is primarily used as a part of information system today, by companies with a strong consumer focus -retail, financial, communication, and marketing organizations. It enables these 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. And, it enables them to determine the impact on sales, customer satisfaction, and corporate profits. Finally, it enables them to "drill down" into summary information to view detail transactional data. With data mining, a retailer could use point-of-sale records of customer purchases to send targeted promotions based on an individual's purchase history. By mining demographic data from comment or warranty cards, the retailer could develop products and promotions to appeal to specific customer segments.

Data Mining is a collaborative tool which comprises of database systems, statistics, machine learning, visualization and information science. Based on the data mining approach used, different techniques form the other discipline can be used such as neural networks, artificial intelligence, fuzzy logic, knowledge representation, high performance Data mining refers to extracting or mining knowledge from large amount of data. There may be other terms which refer data mining such as knowledge mining, knowledge extraction, data/pattern analysis, data archeology, and data dredging. The Knowledge discovery as a process may consist of following steps:

1. Data Cleaning: It removes noise and inconsistent data.
2. Data integration: It is where multiple data sources are combined.
3. Data selection: Data relevant to the analysis task are retrieved from the database.
4. Data transformation: Data are transformed or consolidated into forms appropriate for mining by performing summary or aggregation operations, for instance.
5. Data mining: An essential process where intelligent methods are applied in order to extract data patterns.
6. Pattern evaluation: To identify the truly interesting patterns representing knowledge based on some interesting measure.
7. Knowledge presentation: Visualization and knowledge representation techniques are used to present the mined knowledge to the users.

When you look at the above step you will find that data mining is a very important step in knowledge representation. It interacts with the user for knowledge base.
So it is found that there is necessity of a typical architecture for data mining as a big process. The architecture of the data mining has the following components:
1 Database, data warehouse and information repository: This is one or a set of databases, data warehouse, and information repository which can be used for data cleaning and data integration.
2 Database server: This Server is responsible for fetching the relevant data
3 Data mining engine: This helps in accessing the user through applications. It accesses data from the warehouse with the help of standard data connectivity mechanisms. Usually database drivers are used to connect the database.
4 Patterns evaluation model: It acquires the data to be evaluated form the database, producing the pattern edge. This model scans the data. It searches and creates the interesting patterns based on the thresholds.
5 Graphical user interface: It communicates between the user and the data mining system. It allows the user to interact with the system and specifies the data mining queries or task.
6 Data mining is applicable to any kind of information repository. Some of these may be relational databases, data warehouse, transactional databases, advanced database management systems, www and files. Advance database systems include object oriented databases, object relational databases, and application oriented databases.
7 The best example for data mining which is so close to our lives is Google. The success of Google depends on the use of data mining techniques in the analysis of data in the search engine to meet your search demand.

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