ISM SECOND MODULE

Database
• It is an integrated collection of logically organised data to manage an
organisation. It excludes transient data such as input document,
reports and intermediate results obtained during processing. A
database models the data resource of an organization using the
relationships between different data items. Database systems require
the use of direct access storage devices.
DBMS
• A database management system (DBMS) is a collection of programs
that enables you to store, modify, and extract information from a
database. There are many different types of DBMSs, ranging from
small systems that run on personal computers to huge systems that
run on mainframes. The DBMS acts as an interface between the
application program and the data in the database.
Examples
• Computerized library systems
•  Automated teller machines
•  Flight reservation systems
•  Computerized parts inventory systems
Advantages
•  Control redundancy
•  Relating data items
•  Data integrating
•  Database performance
•  Data security
•  Management controls
Disadvantages
• It requires considerable outlay of resources.  Main frame hardware
is expensive and although today there are a number of PC based
database, multiple copies of the software, combined with training,
can become an expensive proposition.
DATA MANAGEMENT
• • Data integrity: The necessary data is collected and recorded on a form called a
source document that serves as input to the system. For example data,
describing a sale is entered on a sales order form.
• • Integrity and verification: The data is examined to assure its consistency and
accuracy based on prescribed constraints and rules.
• • Storage: The data is stored on some medium such as hard disk or magnetic
disk.
• • Maintenance: New data is added, existing data is changed and data no longer
needed is deleted for keeping the data resource current.
• • Security: The data is safeguarded to prevent destruction, damage, or misuse.
• • Organisation: The data is arranged in such a way to meet the information needs
to users.
• • Retrieval: The data is made available to users.
DBMS ARCHITECTURE
• Structured Query Language(SQL) as we all know is the database language
by the use of which we can perform certain operations on the existing
database and also we can use this language to create a database. SQL
uses certain commands like Create, Drop, Insert etc. to carry out the
required tasks.
these SQL commands are mainly categorized into four categories as:
• DDL – Data Definition Language
• DQL – Data Query Language
• DML – Data Manipulation Language
• DCL – Data Control Language
Query Processor :
• (a) DML Compiler
(b) Embedded DML pre-compiler
(c) DDL Interpreter
(d) Query Evaluation Engine
Storage Manager :

(a)Authorization and Integrity Manager

(b) Transaction Manager
(c) File Manager
(d) Buffer Manager
Data Structure :
(a) Data Files
(b) Data Dictionary
(c) Indices
(d) Statistical Data
Query Processor Components
•  DML Pre-compiler : It translates DML statements in a query language into
low level instructions that query evaluation engine understands. It also
attempts to transform user's request into an equivalent but more efficient
form.
• Embedded DML Pre-compiler : It converts DML statements embedded in
an application program to normal procedure calls in the host language. The
Pre-compiler must interact with the DML compiler to generate the
appropriate code.
• DDL Interpreter : It interprets the DDL statements and records them in a
set of tables containing meta data or data dictionary.
• Query Evaluation Engine : It executes low-level instructions generated by
the DML compiler.
Storage Manager Components :

• They provide the interface between the low-level data stored in the
database and application programs and queries submitted to the system.
• Authorization and Integrity Manager : It tests for the satisfaction of
integrity constraints checks the authority of users to access data.
• Transaction Manager : It ensures that the database remains in a
consistent state despite the system failures and that concurrent
transaction execution proceeds without conflicting.
• File Manager : It manages the allocation of space on disk storage and
the data structures used to represent information stored on disk.
• Buffer Manager : It is responsible for fetching data from disk storage
into main memory and deciding what data to cache in memory.
Data Structures
Following data structures are required as a part of the physical system
implementation.
• Data Files : It stores the database.
• Data Dictionary : It stores meta data (data about data) about the
structure of the database.
• Indices : Provide fast access to data items that hold particular values.
• Statistical Data : It stores statistical information about the data in the
database. This information is used by query processor to select efficient
ways to execute query.
TYPES OF DATABASES
• Operational databases store detailed data needed to support the
business processes and operations of a company. They are also called
subject area databases (SADB), transaction databases, and production
databases. Examples are a customer database, human resource
database, inventory database, and other databases containing data
generated by business operations.
• Many organizations replicate and distribute copies or parts of
databases to network servers at a variety of sites. These distributed
databases can reside on network servers on the World Wide Web, on
corporate intranets or extranets, or on other company networks.
Distributed databases may be copies of operational or analytical
databases, hypermedia or discussion databases, or any other type of
database. Replication and distribution of databases improve database
performance at end-user worksites. Ensuring that the data in an
organization’s distributed databases are consistently and
concurrently updated is a major challenge of distributed database
management
• Access to a wealth of information from external databases is
available for a fee from commercial online services and with or
without charge from many sources on the World Wide Web. Web
sites provide an endless variety of hyperlinked pages of multimedia
documents in hypermedia databases for you to access. Data are
available in the form of statistics on economic and demographic
activity from statistical databanks, or you can view or download
abstracts or complete copies of hundreds of newspapers, magazines,
newsletters, research papers, and other published material and
periodicals from bibliographic and full-text databases
Database models
• To describe structure of a database several concepts are used.
• The collection of concepts is known as data models.
CATEGORIES OF DATAMODELS
• OBJECT BASED LOGICAL MODELS
• RECORD BASED DATA MODELS
OBJECT BASED LOGICAL MODELS
• Object oriented data model
• Entity Relationship data model(ER)
Object oriented data model
• Logical organisation of real world entities. It includes: Constraints on
these entities, Relationship among entities
Object oriented data model
ER Model
• Used to produce a type of conceptual schema or semantic model of a
system., often a relational data base and it requirements in top-down
fashion
• Diagram created using this process are called ER Diagrams
Entity Relationship Data Model
Record based data Models
• Relational Data model
• Network Data Model
• Hierarchical Model
Relational data model
• To represent data and relationships among them .This model uses
relations(Group of tables)Every table has a number of
attributes(column) with unique names.
Relational Data Model
Network Data Model
• This model represents data by collection of records and relationship
among data. This is represented by links, which can be viewed as
pointer
Network Data Model
Hierarchical Data Model
• This model is similar to network model in the sense that data and
relationships among data are represented by records and links
respectively. They use tree structures to represent relationship
among data.
Hierarchical Data Model
Data warehouse
• A data warehouse stores data that have been extracted from the
various operational, external, and other databases of an organization.
It is a central source of the data that have been cleaned, transformed,
and cataloged so that they can be used by managers and other
business professionals for data mining, online analytical processing,
and other forms of business analysis, market research, and decision
support. Data warehouses may be subdivided into data marts, which
hold subsets of data from the warehouse that focus on specific
aspects of a company, such as a department or a business process.
• Data mining is a major use of data warehouse databases and the
static data they contain. In data mining, the data in a data warehouse
are analyzed to reveal hidden patterns and trends in historical
business activity. This analysis can be used to help managers make
decisions about strategic changes in business operations to gain
competitive advantages in the marketplace.
• Data mining can discover new correlations, patterns, and trends in
vast amounts of business data (frequently several terabytes of data)
stored in data warehouses. Data mining software uses advanced
pattern recognition algorithms, as well as a variety of mathematical
and statistical techniques, to sift through mountains of data to extract
previously unknown strategic business information.
For example, many companies use data
mining to:
• • Perform market-basket analysis to identify new product bundles.
• • Find root causes of quality or manufacturing problems.
• • Prevent customer attrition and acquire new customers.
• • Cross-sell to existing customers.
• • Profile customers with more accuracy.
Data Sources:
• INTERNAL DATA
• EXTERNAL DATA
Components of Data warehouse
• Data Warehouse Database
• Sourcing, Acquisition, Clean up and transformation tools
• Meta data
• Access Tools
• Data Marts
• Data Warehouse administration and Management
• Information Delivery System
Advantages of Data Warehouse
• Provide cost effective decision Making
• Better Enterprise intelligence
• Enhanced customer service
• Business re-engineering
• Information system re-engineering
Disadvantages of Data warehouse
• High installation cost
• Time taking
• Change resistence
• Specific skills required
• Complex
• Management acceptance
• Security issues
Uses/Applications of data warehouse
• Standard reports and queries
• Queries against summarised data
• Data mining
• Interface with other data warehouse
Data Mining
• It is the research search for relationships and global patterns that
exist in large databases but are hidden among vast amount of data.
These relationships represent valuable knowledge about the
database and the objects in the database.
Need for Data Mining
• Operational
• Decisional
• Informational
• Specific application
Components of Data Mining
• Database, Data warehouse, Other Information Repository
• Database or Data Warehouse server
• Knowledge base
• Data Mining Engine
• Pattern Evaluation Module
• Graphical User Interface
Data Mining Process
• Data Collection
• Feature Extraction and Data Cleaning
• Analytical Processing and algorithms
Data Mining Techniques

• Cluster analysis
• Induction
• Neural Networks
• Online analytical processing
• Data visualisation
Advantages of Data Mining
• Automated forecasting of trends and behaviours
• Automated determination of earlier unknown trends
• Extensive Depth and Breadth of Database
Disadvantages
• Privacy
• Security
• Misuse of Information
Applications of data mining
• Retail Marketing
• Banking
• Insurance and health
• Transportation
• Medicine
TYPES OF IS
• Operations Support Systems
• Management Support Systems
Operations Support Systems
• TPS
• Process Control Systems
• Enterprise collaboration System
Management Support Systems
• MIS
• DSS
• ESS
Management Information System
• MIS is a planned system of collecting, processing, storing and
disseminating data in the form of information needed to carry out the
functions of management.
Characteristics of MIS
• Integrative System
• Subsytem
• Provides relevant information to Management
• Flexible
• Enhances Productivity
• Coordinated system
• Feedback System
• Management oriented
• Common database
• Distributed Data Processing
• Computerised system
• Transforms the data into information
Objectives of MIS
• Data Capturing
• Processing of data
• Storage of information
• Retrieval of information
• Dissemination of information
Functions of MIS
• Collect Data
• Store and process data
• Present the information to managers
Structure of MIS
• Structure of MIS can be divided into two:
1)Based on Management activity
2)Based on Organisational functions
Based on Management activity
FOUR LEVELS OF HIEREACHY ARE:
Strategic Planning
Management Control and tactical planning
Operations planning and control
Transaction processing
Based on organisation functions
• Sales/Marketing
• Production
• Logistics
• HR
• Finance and accounting
• Information processing subsystem
• Top Management system
Advantages of MIS
• Facilitates planning
• Minimises information overload
• Encourages decentralisation
• Brings coordination
• Makes control easier
Disadvantages
• Highly sensitive and requires frequent monitoring
• Budgeting of MIS is extremely difficult
• Lack of flexibility
• Takes into account only qualitative factors
Decision making with MIS
• Operational
• Tactical
• Strategic
Decision Making
• Decision-making is a cognitive process that results in the selection of
a course of action among several alternative scenarios.
• Decision-making is a daily activity for any human being. There is no
exception about that. When it comes to business organizations,
decision-making is a habit and a process as well.
• Effective and successful decisions result in profits, while unsuccessful
ones cause losses. Therefore, corporate decision-making is the most
critical process in any organization.
• In a decision-making process, we choose one course of action from a
few possible alternatives. In the process of decision-making, we may
use many tools, techniques, and perceptions.
• In addition, we may make our own private decisions or may prefer a
collective decision.
• Usually, decision-making is hard. Majority of corporate decisions
involve some level of dissatisfaction or conflict with another party.
• Let's have a look at the decision-making process in detail.
DECISION SUPPORT SYSTEM:

• DSS are especially useful for semi-structured problems where problem-solving

is improved by interaction between the managers and the computer system.
•  The emphasis is on small, simple models which can easily be understood and
used by the decision maker.
•  Examples of semi-structured decision are : planning a mix of investments for
a portfolio, looking at the financial implication of various way of financing a
short-term cash flow deficit, consideration of alternative production and
pricing policies, assessing the impact of potential future changes in exogenous
variables such as interest rates, analysis of the of the credit-worthiness of
corporate clients, and assessing the likely impacts of departmental
reorganisation.
OBJECTIVES OF DSS

•  Assist managers in making decisions to solve semi-structured

problems.
•  Support the manager’s judgement rather try to replace it.
•  Improve the manager’s decision-making effectiveness rather than
its efficiency.
•  These objectives correlate with three fundamental principles of the
DSS concept-problem structure, decision support, and decision
effectiveness.
COMPONENTS OF A DSS

Database Management System (DBMS)

• To solve a problem the necessary data may come from internal or
external database.
• In an organization, internal data are generated by a system such as
TPS and MIS.
• External data come from a variety of sources such as newspapers,
online data services, databases (financial, marketing, human
resources).
Model Management system
•  It stores and accesses models that managers use to make decisions
•Such models are used for designing manufacturing facility, analyzing
the financial health of an organization. Forecasting demand of a
product or service etc.
Support Tools
• Support tools like online help; pull down menus, user interfaces,
graphical analysis, error correction mechanism, facilitates the user
interactions with the system.
There are five function of a DSS facilitating managerial decision making.
They are:
•• Model building
•• What-if analysis
• • Goal seeking
•• Risk analysis
•• Graphical analysis.