Using Database to improve business processes and decision making

Types of database design

1. Conceptual design: The conceptual design which is also called as logical design of a database is an abstract model of a database from a business perspective. It describes how the data element in database is to be grouped. When all the required requirements have been gathered and analyzed, the subsequently step is to create a conceptual schema, using a high level conceptual data model for the database. This phase is called conceptual design. The result of this phase is an ER (Entity-Relationship) diagram. It is a high level data model for the specific application area. It describes the relationship between different entities (items, objects) to each other. It also describes the available features (attributes) what each entity has. It includes the definitions of all the concepts (attributes, entities) of the application area.
2. Physical design: The physical design shows how the database is actually arranged on a storage device. The goal of the physical design, is to implement the database. In the physical design, one must know which Database Management System (DBMS) is used. Example, different DBMS's have different names for data types and have different data types. The SQL clauses to create the database are written. The indexes, the rules (integrity constraints) and the users' access rights are well defined. At last, the data to test the database is added in.

Distributed database: Storing database in more than one place is known as distributed database. Types of distributed database are listed below:

1. Partitioned database: Separate locations store different parts of database.
2. Replicated database: Central database duplicated at different locations.

Data-warehouse: Huge collection of data that is recorded according to subject, date, time and is non-volatile (data from data-warehouse can only be accessed not changed). Data in data-warehouse can’t be updated. For improving decision making, profit making, data-warehouse is used.

Data mining: Extracting valuable data from huge collection of data. It is used for improving decision, probability increment, etc.

Using Database to improve business processes and decision making

Business uses their database to keep track of basic transactions such as paying to suppliers, processing order, keeping track of customer and paying for employees.

Organization also need database to provide information that will help the company run the business more efficiently and help managers and employees make better decision.

On a large company with large database, on large system for separate function such as manufacturing, sales and accounting, special capabilities and tools are required for analyzing vast quantity of data for accessing data from multiple systems. These capabilities include data-warehouse, data mining and tools for accessing internal database through web.

1. Data-warehouse: It is a database that stores current to historical data of potential interest to decision maker throughout the company. Data originate in much core operational transactional system such as system for sale, customer account, and manufacturing and may include data from website transaction.

Data-warehouse standardize information from different operational database so that information can be used occur the enterprise for management analysis and decision making. Data-warehouse makes the data available for anyone to access as needed but it cannot be altered. Data-warehouse also provides a range of standardized query tools and analytical tools and graphical representation facilities.

Organization also use intranet to make data-warehouse information widely available throughout the firm. Data in data-warehouse are subject-oriented, integrated and non-volatile.

Fig: Component of data-warehouse

Data-mart: It is a subset of data-warehouse in which a summarized or highly focused portion of the organizational data is placed in a separate database for a specific purpose of users. For example: a company might develop marketing and sales data-mart to deal with customer information.

Tools for business intelligence

1. Multidimensional data analysis –OLAP: It is also defined as online analytical processing (OLAP). OLAP supports multidimensional data analysis and enables user to view the same data in a different way using multidimensional such as product, pricing, cost, region, time-period, age, group, etc. Production manager could use a multidimensional data analysis tool to learn information about the status of product from different view i.e. dimension. OLAP also allows user to obtain online answer in fairly short interval of time even when the data are stored in a very large database.
2. Data mining: Once data have been captured and organized in data-warehouse and data-mart, they are available for further analysis using tools for Business intelligence (BI). Principal tool for BI includes software for database query and reporting tools for multidimensional data analysis and tools for data mining. Data mining discovery driven. It is simply defined as extracting hidden knowledge from a huge collection of data i.e. data-warehouse. Data mining provide insight into corporate data that cannot be obtained with OLAP by finding hidden pattern and relationship in large database and inferring rules from them to predict future behavior. In data mining, pattern and rules are used to guide decision making and forecast the effect of those decision. The type of information obtained from data mining includes:
3. Association: Are occurrence linked to a single effect and for instance when a coke is purchased 80% of the time the customer also purchase bhujiya. Pen-refill
4. Sequence: Events are linked overtime. For example: if a house is purchased, a new refrigerator is purchase. Iphone-cover
5. Classification: It recognizes pattern that describe the group to which an item belongs by examine existed item that have been classified. It helps discover the characteristics of a customer who are likely to use the product.
6. Clustering: It works in a similar manner as a classification when as groups have been yet defined. For clustering, we use a demographic or psychographic mechanism to identify group of customer.
7. Forecasting: It uses prediction in a different way. It uses a sense of existing value to forecast, what other value will be.

Different terminologies

• Predictive analysis: Uses data mining technique, historical data and assumptions about future conditions to predict outcome of event.
• Text mining: Extracts key elements from large unstructured data set. Example store email
• Web mining: Discovery and analysis of useful patterns and information from www. Example: To understand customer behavior, evaluate effectiveness of website.

1. Web-content mining: Knowledge extracted from content of webpage.
2. Web-structure mining: Link to and from webpage.
3. Web-usage mining: User interaction data recorded by web server

Reference

Laudon, Laudon, "Management Information Systems Managing the Digital Firm", twelfth edition