Unit-1: Foundation of Information Systems in Business:

1- Information system in business

Information Systems is the study of the interaction between people, organisations and
information technology. This discipline uses hardware and software as tools to solve business
and organisational problems. Information Systems combines principles from business (such as
accounting and management) and social sciences with the study of computing.

Foundation Concepts: Information Systems and Technologies

WHY INFORMATION SYSTEMS ARE IMPORTANT

An understanding of the effective and responsible use and management of information systems
is important for managers and other business knowledge workers in today’s global information
society. Information systems and technologies have become a vital component of successful
businesses and organizations. Information systems constitute an essential field of study in
business administration and management, as they are considered a major functional area in
business operations.

Here are some key reasons why information systems are important:
1. Efficient Data Management: Information systems enable organizations to efficiently
collect, store, and manage vast amounts of data. This helps maintain accurate records,
reduces the risk of data loss, and improves data accessibility.
2. Improved Decision-Making: Access to timely and accurate information through
information systems empowers managers and decision-makers to make well-informed
and data-driven decisions. This leads to better strategic planning and operational
choices.
3. Enhanced Communication and Collaboration: Information systems facilitate
communication and collaboration within an organization. They provide tools for
sharing documents, collaborating on projects, and communicating across departments
and geographical locations.
4. Increased Productivity: Automation of routine tasks through information systems leads
to increased productivity. Processes that used to require manual effort can now be
automated, freeing up employees to focus on higher-value tasks.
5. Competitive Advantage: Organizations that effectively leverage information systems
gain a competitive edge by responding quickly to market changes, identifying trends,
and adapting their strategies based on real-time data.
6. Efficient Resource Allocation: Information systems help organizations allocate
resources more efficiently by providing insights into where resources are needed most,
optimizing inventory levels, and managing workforce allocation.
7. Effective Customer Relationship Management: Customer-related information systems
enable organizations to manage interactions with customers, personalize services, track
customer preferences, and improve overall customer satisfaction.
8. Support for Innovation: Information systems provide a platform for innovation by
allowing organizations to experiment with new ideas, products, and services. They can
analyse customer feedback and market trends to drive innovation.
 9. Real-Time Monitoring: Many information systems offer real-time monitoring
capabilities that allow organizations to track operations, performance metrics, and
critical processes in real time. This enables proactive problem-solving and corrective
actions.
10. Global Reach: Information systems facilitate global business operations by enabling
organizations to connect with suppliers, partners, and customers across the world. This
opens up opportunities for expansion and growth.
11. Regulatory Compliance: Information systems help organizations adhere to regulatory
and legal requirements by providing tools for data security, privacy protection, and
audit trails.
12. Data Analytics and Insights: Advanced information systems with data analytics
capabilities help organizations uncover patterns, trends, and insights from their data.
This information can guide strategic planning and decision-making.
13. Risk Management: Information systems enable organizations to identify and mitigate
risks by providing tools to assess potential risks, model scenarios, and develop risk
management strategies.
14. Adaptability to Change: In today's rapidly changing business environment, information
systems allow organizations to adapt quickly to new market conditions, technological
advancements, and industry disruptions.
Overall, information systems play a pivotal role in modern business operations, enabling
organizations to streamline processes, make informed decisions, innovate, and remain
competitive in their respective industries.
An IS Framework for Business Professionals:

The above Figure illustrates a useful conceptual framework that outlines what a manager or
business professional needs to know about information systems. It emphasizes five areas of
knowledge: • Foundation Concepts • Information Technologies • Business Applications •
Development Processes • Management Challenges

What is an Information System?

An information system (IS) can be any organized combination of people, hardware, software,
communications networks, and data resources that collect, transforms, and disseminate
information in an organization.
Information Technologies: Business professionals rely on many types of information systems
that use a variety of information technologies. For example:

Types of IS

- Manual (paper-and-pencil) information systems

- Informal (word-of-mouth) information systems
- Formal (written procedures) information systems
- Computer-based information systems Computer-based information systems (IS) use
hardware, software, the Internet, and other telecommunications networks, computer-
based data resource management techniques, and other forms of information
technologies (IT) to transform data resources into a variety of information products for
consumers and business professionals.

SYSTEM CONCEPTS - A FOUNDATION

System concepts underlie the field of information systems. Understanding system concepts will
help you understand many other concepts in the technology, applications, development, and
management of information systems. System concepts help you understand:
• Technology. That computer networks are systems of information processing
components that uses a variety of hardware, software, data and telecommunication
technologies.
• Applications. That electronic business and commerce involves interconnected business
information systems.
• Development. That developing ways to use information technology n business includes
designing the basic components of information systems.
• Management. That managing information technology emphasizes the quality, strategic
business value, and security of an organization’s information systems.

What is a System?
Question: What is a system as it applies to the concept of an information system?
Answer: A system is a group of interrelated components working together toward a common
goal by accepting inputs and producing outputs in an organized transformation process.

A system (sometimes called a dynamic system) has three basic interacting components or
functions. These include:
• Input involves capturing and assembling elements that enter the system to be processed.
• Processing involves transformation processes that convert input into output.
• Output involves transferring elements that have been produced by a transformation
process to their ultimate destination.
Feedback and Control: Two additional components of the system concept include feedback and
control. A system with feedback and control components is sometimes called a cybernetic
system, that is, a self-monitoring, self-regulating system.
• Feedback is data about the performance of a system.
• Control involves monitoring and evaluating feedback to determine whether a system is
moving toward the achievement of its goals. The control function then makes necessary
adjustments to a system's input and processing components to ensure that it produces
proper output.
Other System Characteristics:
A system does not exist in a vacuum; rather, it exists and functions in an environment
containing other systems.
• Subsystem: A system that is a component of a larger system, where the larger system is
its environment.
• System Boundary: A system is separated from its environment and other systems by its
system boundary.
• Interface: Several systems may share the same environment. Some of these systems
may be connected to one another by means of a shared boundary, or interface.
• Open System: A system that interacts with other systems in its environment is called an
open system (connected to its environment by exchanges of inputs and outputs).
• Adaptive System: A system that has the ability to change itself or its environment in
order to survive is called an adaptive system.

2- The Components of an Information system

An information system model expresses a fundamental conceptual framework for the major
components and activities of information systems. An information system depends on the
resources of people, hardware, software, data, and networks to perform input, processing,
output, storage, and control activities that convert data resources into information products.
The information systems model outlined in the text emphasizes four major concepts that can
be applied to all types of information systems:
• People, hardware, software, data, and networks, are the five basic resources of
information systems.
• People resources include end users and IS specialists, hardware resources consist of
machines and media, software resources include both programs and procedures, data
resources can include data and knowledge bases, and network resources include
communications media and networks.
• Data resources are transformed by information processing activities into a variety of
information products for end users.
• Information processing consists of input, processing, output, storage, and control
activities.

INFORMATION SYSTEM RESOURCES

The basic IS model shows that an information system consists of five major resources:
• People resources
• Hardware resources
• Software resources
• Data resources
• Network resources

People Resources:
People are required for the operation of all information systems. These people resources include
end users and IS specialists.
• End Users (also called users or clients) are people who use an information system or
the information it produces. Most of us are information system end users. And most end
users in business are knowledge workers, that is, people who spend most of their time
communicating and collaborating in teams of workgroups and creating, using, and
distributing information.
• IS Specialists being people who develop and operate information systems. They
include system analysts, software developers, system operators, and other managerial,
technical, and clerical IS personnel.
Systems analysts – design information systems based on the information requirements
of end users.
Software developers – create computer programs based on the specifications of systems
analysts.
System operators – monitor and operate large computer systems and networks.

Hardware Resources:
Hardware resources include all physical devices and materials used in information processing.
• Machines - physical devices (computers, peripherals, telecommunications networks,
etc.)
• Media - all tangible objects on which data are recorded (paper, magnetic disks etc.)
Examples of hardware in computer-based information systems are:
• Computer Systems – which consist of central processing units containing
microprocessors, and a variety of interconnected peripheral devices.
• Computer peripherals – which are devices such as a keyboard or electronic mouse for
input of data and commands, a video screen or printer for output of information, and
magnetic or optical disks for storage of data resources.
Software Resources:
Software resources include all sets of information processing instructions.
• Program - a set of instructions that causes a computer to perform a particular task.
• Procedures - set of instructions used by people to complete a task.
Examples of software resources are:
• System software – such as an operating system program, that controls and supports the
operations of a computer system.
• Application software – are programs that direct processing for a particular use of
computers by end users.
• Procedures – are operating instructions for the people who will use an information
system.

Data Resources:
Data constitutes a valuable organizational resource. Thus, data resources must be managed
effectively to benefit all end users in an organization. The data resources of information systems
are typically organized into:
• Databases - a collection of logically related records or files. A database consolidates
many records previously stored in separate files so that a common pool of data records
serves many applications.
• Knowledge Bases - which hold knowledge in a variety of forms such as facts and rules
of inference about various subjects.
Data versus Information. The word data is the plural of datum, though data is commonly used
to represent both singular and plural forms. The term’s data and information are often used
interchangeably. However, you should make the following distinction:
Data: - are raw facts or observations, typically about physical phenomena or business
transactions. More specifically, data are objective measurements of the attributes
(characteristics) of entities, such as people, places, things, and events.
Information: - is processed data, which has been placed in a meaningful and useful context
for an end user. Data is subjected to a “value-added” process (data processing or information
processing) where:
• Its form is aggregated, manipulated, and organized.
• Its content is analysed and evaluated
• It is placed in a proper context for a human user

Network Resources:
Telecommunications networks like the Internet, intranets, and extranets have become essential
to the successful electronic business and commerce operations of all types of organizations and
their computer-based information systems. Telecommunications networks consist of
computers, communications processors, and other devices interconnected by communications
media and controlled by communications software. The concept of network resources
emphasizes that communications networks are a fundamental resource component of all
information systems. Network resources include:
• Communications media (twisted-pair wire, coaxial cable, fiber-optic cable, and
microwave, cellular, and satellite wireless systems.
• Network support (people, hardware, software, and data resources that directly support
the operation and use of a communications network).
INFORMATION SYSTEM ACTIVITIES
Information processing (or data processing) activities that occur in information system include
the following:
• Input of data resources
• Processing of data into information
• Output of information products
• Storage of data resources
• Control of system performance

Input of Data Resources:

• Data about business transactions and other events must be captured and prepared for
processing by the input activity. Input typically takes the form of data entry activities
such as recording and editing.
• Once entered, data may be transferred onto a machine-readable medium such as
magnetic disk or type, until needed for processing.

Processing of Data into Information:

• Data is typically subjected to processing activities such as calculating, comparing,
sorting, classifying, and summarizing. These activities organize, analyse, and
manipulate data, thus converting them into information for end users.
• A continual process of correcting and updating activities must maintain quality of data
stored in an information system.

Output of Information Products:

• Information in various forms is transmitted to end-users and made available to them in
the output activity. The goal of information systems is the production of appropriate
information products for end users.

Storage of Data Resources: Storage is a basic system component of information systems.

• Storage is the information system activity in which data and information are retained in
an organized manner for later use.

Control of System Performance: An important information system activity is the control of its
performance.
• An information system should produce feedback about its input, processing, output,
and storage activities.
• Feedback must be monitored and evaluated to determine if the system is meeting
established performance standards.
• Feedback is used to make adjustments to system activities to correct deficiencies.
 3- TRENDS IN INFORMATION SYSTEMS
The roles given to the information systems function have expanded significantly over the years.

1950s - 1960s - Data Processing - Electronic data processing systems Role: Transaction
processing, record keeping, and accounting, and other electronic data processing (EDP)
applications
1960s - 1970s - Management Reporting – Management information systems Role: Providing
managerial end users with predefined management reports that would give managers the
information they needed for decision-making purposes.
1970s - 1980s - Decision Support - Decision support systems Role: The new role for
information systems was to provide managerial end users with ad hoc support of their decision-
making process. This support would be tailored to the unique decision-making styles of
managers as they confronted specific types of problems in the real world.
1980s - 1990s - Strategic and End User Support Role: End users could use their own computing
resources to support their job requirements instead of waiting for the indirect support of
corporate information services departments.
• End User Computing Systems Role: Direct computing support for end user productivity
and work group collaboration.
 • Executive Information Systems (EIS) - Role: These information systems attempt to give
top executives an easy way to get the critical information they want, when they want it,
tailored to the formats they prefer.
• Expert Systems (ES) and other Knowledge-Based Systems Role: Expert systems can
serve as consultants to users by providing expert advice in limited subject areas.
• Strategic Information Systems (SIS) - Role: Information technology becomes an
integral component of business processes, products, and services that help a company
gain a competitive advantage in the global marketplace.
1990s - 2000 – Electronic business and commerce systems Role: The rapid growth of the
Internet, intranets, extranets, and other interconnected global networks has revolutionised the
operations and management of today’s business enterprises.

4- Types of information systems.

Information Systems perform important operational and managerial support roles in businesses
and other organizations. Therefore, several types of information systems can be classified
conceptually as either:
• Operations Support Systems
• Management Support Systems

1. Operations Support Systems

Information systems are needed to process data generated by and used in business operations.
Such operations support systems (OSS) produce a variety of information products for internal
and external use. However, they do not emphasize producing the specific information products
that can best be used by managers. Further processing by management information systems is
usually required. The role of a business firm’s operations support systems is to:
• Effectively process business transactions
• Control industrial processes
• Support enterprise communications and collaboration
• Update corporate databases

Transaction Processing Systems (TPS) These systems handle day-to-day transactions and are
designed to process large volumes of data quickly. (Sales, purchases, inventory changes). TPS
also produce a variety of information products for internal or external use (customer statements,
employee pay checks, sales receipts etc.). Examples include:
• Point of Sale (POS) System: Used in retail environments to process sales transactions
and manage inventory.
• Online Banking System: Handles financial transactions such as transferring funds and
paying bills.
• Reservation System: Used by airlines, hotels, and other industries to manage bookings
and reservations.
TPS process transactions in two basic ways:
• Batch Processing - transactions data is accumulated over a period of time and processed
periodically.
• Real-time (or online) processing - data is processed immediately after a transaction
occurs.

Process Control Systems (PCS) – Process control systems are systems, which make use of
computers to control ongoing physical processes. These computers are designed to
automatically make decisions, which adjust the physical production process.

An example of a basic process control system is a thermostat, a heating element, and a cooling
element within a room. As the temperature in the room fluctuates beyond set boundaries, the
thermostat turns on either the heating or cooling system to keep the room at a specific
temperature. petroleum refineries and the assembly lines of automated factories.

Enterprise Collaboration Systems - Enterprise collaboration systems are information

systems that use a variety of information technologies to help people work together. Enterprise
collaboration systems help us:
• Collaborate - to communicate ideas
• Share resources
• Co-ordinate our cooperative work efforts as members of the many formal and informal
process and project teams. The goal of enterprise collaboration systems is to use information
technology to enhance the productivity and creativity of teams and workgroups in the modern
business enterprise. Example: - emails, videoconferencing etc.

2. Management Support Systems (MSS) –

Management support systems focus on providing information and support for effective
decision making by managers. They support the decision-making needs of strategic (top)
management, tactical (middle) management, and operating (supervisory) management.
Conceptually, several major types of information systems support a variety of decision-making
responsibilities:
• Management Information Systems (MIS)
• Decision Support Systems (DSS)
• Executive Information Systems (EIS)

Management information systems are the most common form of management support
systems. They provide managerial end users with information products that support much of
their day-to-day decision-making needs. MIS provide a variety of prespecified information
(reports) and displays to management that can be used to help them make more effective,
structured types of day-to-day decisions. Information products provided to managers include
displays and reports that can be furnished:
• On demand
• Periodically, according to a predetermined schedule
• Whenever exceptional conditions occur

These systems provide middle management with reports and summaries to help them make
decisions. Examples include:
• Sales Reporting System: Generates reports on sales performance, trends, and forecasts
for the sales team.
• Inventory Management System: Tracks inventory levels, reorder points, and stock
movement for efficient inventory control.
• Employee Performance System: Provides managers with information about employee
productivity and performance.

Decision support systems provide managerial end users with information in an interactive
session on an ad hoc (as needed) basis. DSS aid in making complex decisions by providing
interactive tools for analysing and manipulating data. Examples include:
• Business Intelligence System: Collects, analyses, and presents business data to support
decision-making.
• Data Mining System: Identifies hidden patterns and trends in large datasets to support
decision-making.
• What-If Analysis Tool: Allows users to simulate different scenarios and analyse their
potential impact. Where to spend for advertisement, Product pricing etc.
• Financial forecasting system that helps managers project financial outcomes based on
different variables.

Executive information systems provide top and middle management with immediate and easy
access to selective information about key factors that are critical to accomplishing a firm’s
strategic objectives. EIS are easy to operate and understand. EIS are designed to provide senior
executives with a clear view of the organization's performance and strategic information.
Examples include:
• Dashboard Reporting System: Displays key performance indicators (KPIs) and critical
metrics in a visual format. Easy access to the information of competitors.
• Strategic Planning System: Assists executives in setting organizational goals and
making long-term strategic decisions.
Other Classifications of Information Systems: Several other categories of information systems
that support either operations or management applications include:
• Expert Systems: - These systems replicate the decision-making ability of a human expert in
a specific domain. Examples include:
• Medical Diagnosis System: Uses medical knowledge to assist doctors in diagnosing
diseases and recommending treatments. PXDES- An example of ES used to predict the
degree & type of Lung Cancer.
• Legal Advice System: Provides legal guidance based on a database of laws, regulations,
and precedents.

• Knowledge Management Systems: - These systems capture, store, and distribute an

organization's knowledge and expertise. Examples include:
• Document Management System: Organizes and manages documents, making it easy
to store, search, and retrieve information.
• Collaboration Software: Enables teams to work together on projects, share
information, and collaborate in real-time.

Enterprise Resource Planning (ERP) Systems: These integrated systems manage various
aspects of a business, including finance, human resources, inventory, and more. Examples
include:
• SAP: A widely used ERP system that integrates various business processes into a single
platform.
• Oracle NetSuite: Offers ERP, CRM, e-commerce, and other business functionalities in
one system.

• Functional Business Information Systems

• Strategic Information Systems
Unit-2: Concept of MIS, Structure of MIS, MIS & Decision Support Systems, End user and
Enterprise Computing Application software and System software.

1. Concept of MIS

The MIS is an idea which is associated with man, machine, marketing and methods for
collecting information’s from the internal and external source and processing this information
for the purpose of facilitating the process of decision-making of the business. MIS is not new,
only the computerization is new, before computers MIS techniques existed to supply managers
with the information that would permit them to plan and control business operations. The
computer has added on more dimensions such as speed, accuracy and increased volume of data
that permit the consideration of more alternatives in decision-making process.

Management information system is an integrated set of component or entities that interact to

achieve a particular function, objective or goal. Therefore, it is a computer-based system that
provides information for decisions making on planning, organizing and controlling the
operation of the sub-system of the firm and provides a synergistic organization in the process
by providing timely, relevant, and accurate information from various sources within the
organization.

Key components and concepts of MIS include:

• Data Collection and Storage: MIS collects data from various sources within the
organization, such as transaction processing systems, sensors, surveys, and more. This
data is then stored in databases for future retrieval and analysis.
• Data Processing and Analysis: The collected data is processed and transformed into
meaningful information through various techniques, such as data aggregation,
summarization, calculation, and statistical analysis.
• Information Presentation: The processed information is presented in the form of reports,
charts, graphs, dashboards, and other visual representations that are easy for managers
to understand and interpret.
• Hierarchy of Decision-Making: MIS serves managers at different levels of the
organization, including operational, tactical, and strategic levels. The information
provided is tailored to the specific needs and responsibilities of each managerial level.
• Integration of Functions: MIS integrates data and information from various functional
areas of the organization, such as finance, marketing, human resources, operations, and
more. This integration provides a holistic view of organizational activities.
• Support for Planning: MIS assists managers in formulating plans for achieving
organizational goals. It provides data on current performance, historical trends, and
potential scenarios, helping managers make realistic and achievable plans.
• Support for Control: MIS enables managers to monitor ongoing operations and
compare actual performance against established targets. Deviations can be identified
early, allowing corrective actions to be taken.
• Support for Decision-Making: MIS provides managers with accurate and relevant
information needed to make informed decisions. This can range from routine decisions
to more complex strategic choices.
 • Timeliness and Accessibility: MIS emphasizes the importance of timely information
delivery. Managers need access to up-to-date data and reports to respond effectively to
changing circumstances.
• User-Friendly Interfaces: MIS systems are designed with user-friendly interfaces that
allow managers to interact with the system, generate customized reports, and conduct
ad-hoc analyses without requiring extensive technical knowledge.
• Security and Data Integrity: MIS places importance on data security and integrity.
Measures are taken to protect sensitive information and ensure that data remains
accurate and reliable.
• Continuous Improvement: MIS systems are continuously updated and improved to
align with changing organizational needs, technological advancements, and evolving
business strategies.
• Strategic Alignment: MIS is aligned with the strategic goals and objectives of the
organization. It helps managers make decisions that contribute to the overall success
and growth of the organization.
In essence, Management Information Systems play a pivotal role in bridging the gap between
raw data and meaningful information, providing managers with the insights they need to drive
effective decision-making and manage organizational activities more efficiently.

2. Structure of MIS

Structure of Management Information System: The structure of MIS can be described in terms
of its operating elements, decision support, managerial activity, and organizational function.
1. Operating elements of MIS: The operational elements of MIS are:
(a) Physical components: The physical components of an information system include:
o Hardware - which refers to the physical computer instrument and related devices
performing various functions like input, output, secondary storage, CPU and
Communication.
o Software - which refers to the instructions given to the hardware to perform
various operations.
o Database - which is the collection of logically related and centrally controlled
records containing various stored data.
o Procedures - which include the set of instructions to the users, data preparation
group, operating personnel, etc.
o Operating personal - they may be computer operators’ system analysts,
programmers, data administrators, or data preparation personnel.
(b) Processing functions: On the basis of processing functions, information system consists
of the following:
o Processing transactions - transaction is an activity, which acts as the source of
data. The information system functions include the recording and measurement
of these transactions.
o Maintaining master files - it involves the creation and maintenance of master
files for permanent storage of data.
o Producing reports - one of the major jobs of the information system is to
generate and provide reports to the user at various levels of management.
 o Processing inquiries - information systems provide responses to inquiries from
various levels.
o Process interactive support applications information systems provide interactive
facilities to end-user and facilitate system planning, analysis, and decision-
making. They enable the user to ask questions and receive immediate results.
(c) Output for users: The output provided by an information system to the end-user may take
any of the following forms:
o Transaction documents or screen - examples are purchase order, payroll, sales
invoice, etc.
o Preplanned reports containing regular contents.
o Preplanned inquiry responses.
o User-machine dialog results - which refers to the way in which a user can
interact with a system to arrive at a solution.
o Ad hoc reports and enquiry responses - which occur at regular intervals and
receive data for analyses whose format cannot be preplanned.

2. Decision support: One of the major roles of managers in an organization is decision-making;

and, as a tool to the managers, the purpose of information systems is to facilitate the decision-
making process. As a decision support tool, MIS consists of two types of decisions - structured
and unstructured.
(a) Structured decisions (Programmable decisions): These are well-defined, repetitive and
routine decisions, having predetermined decision models or rules. It does not mean that the
decisions are automated. In simple words, programmable decisions can be made within a
framework, specifying the steps to be adopted, in a flow chart, decision table, or a formula. The
decision model will specify the information requirements and can be used by the lower-level
personnel in the organization who do not possess specialized knowledge or skill. Giving
purchase order, preparation of pay rolls, etc., are examples. The guidelines and rules required
for taking such decisions are made available in the form of procedure manuals, which help the
users to understand them. The important features of these decisions are:
• These decisions can be delegated since they do not require any specialized knowledge.
• Programmable decisions can be automated,
• The cost involved is very low when compared to non-programmable decisions.

(b) Unstructured decisions (non-programmable decisions): These decisions are occasional

in nature. They have no pre-established decision models or procedures, necessitating a new
solution for each unique problem. The information requirements cannot be predicted in
advance, so that the retrieval may be ad hoc in nature. Moreover, due to the absence of decision
rules, these decisions are subject to human judgment, and involve very high risk. Capital budget
preparation, introduction of a new product in the market, etc. are examples of non-
programmable decisions.

3. Management Activity (Levels of Management and Information Requirements): On the

basis of managerial activities MIS consists of three activities, such as strategic planning,
tactical planning and operational planning, which constitute a hierarchy.
The functions of strategic planning level include the fixation of goals, policies, general
guidelines, setting up of organizational objectives, etc., which involve long-range
considerations. Decisions made at this level are connected with the choice of business
directions, market strategy, product mix, etc. Strategic level of management (top management)
requires aggregate, not much accurate, wide, future-oriented and largely external information
for decision making.
At the tactical planning level, the emphasis is on managerial control, and it is concerned with
raising and utilization of resources effectively and efficiently. The activities at these levels
include acquisition of resources, tactics, plant location, new product development,
establishment and monitoring of budgets, etc. This level of management requires information
about the targets, budgets and the actuals corresponding to the target performance, because at
this stage control measures are adopted, if the actual and targets vary significantly. The causes
for such variation are analysed and a report is submitted to managers of this level for controlling
activity. Management control and tactical planning level have a medium-term planning
horizon. It involves activities like reusing of resources, structuring of works, acquisition and
training of personnel. Tactical planning is reflected in areas like capital expenditure, budget,
and three-year staffing plan.
The responsibilities of management at the operational, planning and control levels include
effective and efficient use of resources, and the execution of the day-to-day activities of the
organization. They relate also to short-term decisions or current decisions like pricing,
production levels, stock level, etc. The pieces of information required at this level of
management are well defined and restricted. But detailed, historical, highly current, accurate,
frequent and largely internal information is also required at this stage for proper functioning.
Even though the three levels of management activity can be differentiated on the, basis of the
planning horizon, the activities and information processing for these three levels are
interrelated. For instance, the inventory control at the operational level depends on accurate
processing of transactions at the management control and tactical planning levels, which, in
turn, depend on correct summarization of results of operations at the strategic level.

4. Organizational Functions (organizational functions and information requirements):

The structure of MIS can also be explained in terms of organizational functions. These
functions do not have a standard classification, The normal functions in a manufacturing
organization include, purchase, production, marketing, personnel, finance and accounting.
Each of these functions requires unique items of information and must have a separate
information system. MIS is developed to support the functional subsystems of the organization.
With in each functional subsystem, there will be four levels of managerial activities, such as
transaction processing, operational control, managerial control, and strategic planning. The
various subsystems are:
• Purchase Subsystem The transactions to be processed consist of purchase requisition,
purchase orders, manufacturing orders, receiving reports etc. The operational control
level uses information contained in the reports, like under stock items, over stock items,
vender performance, etc. Managerial control information consists of overall
comparisons between planned and actual inventory levels, cost for purchased items,
stock outs, inventory turnover, etc. Strategic planning involves analysis of new
distribution strategies, new polices with regard to venders and making or buying
decisions.

• Production Subsystem The functions of this subsystem include planning of production,

facilities, scheduling of production activities, engineering of product, employment and
training of production personnel, and quality control and inspection. Operational
control requires detailed reports comparing actual performance with production
schedule. Management control requires summary reports comparing overall planned
performance with actual performance. StrategiC planning includes alternative
manufacturing approach or approach to automation.

• Marketing Subsystem Transactions in marketing subsystem are sales orders, promotion

orders etc. The operational control of the marketing subsystem performs hiring and
training of sales force, day to-day scheduling of sales and promotion efforts, etc. The
managerial level compares overall performance against the standard marketing plan.
Strategic planning considers the problems of new markets and new marketing
strategies. The information required at this level relates to customer analysis,
competitor analysis, income projection, etc.

• Personnel Subsystem This subsystem is concerned with employment requisitions, job

description, training specification, personnel data, pay rate changes, hours worked,
benefits, termination notices, etc. Operational control level requires decision
procedures for actions, such as hiring, training, termination, etc. Management control
level requires information about cost of recruiting, composition of skills, cost of
training, salary paid, wage rates, etc. Strategic planning requires information about
alternative strategies for recruiting, salary, training, and benefits and about retaining
personnel.

• Finance and Accounting subsystem Transactions involved in finance subsystem are

processing of credit applications, sales, billing, collection payment vouchers, cheques,
journal vouchers, ledgers, stock transfers and so on. Operational control requires
information about daily error and exception reports, records of processing delays,
records of unprocessed transactions etc. Management control requires information on
budgeted and actual resources, cost of processing accounting data, error rates, and so
forth. Strategic planning requires information to evolve alternative strategy to
adequately finance the firm, long range tax planning policy, systems for cost accounting
and budgeting, etc. Thus, the structure of MIS can be summarized as follows:
Synthesis of MIS Structure
The structure of MIS can be described in terms of operating elements, decision support,
management activity, and organizational functions. These approaches can be synthesized into
a single MIS structure consisting of a physical and conceptual structure.
Physical Structure The physical structure of an MIS consists of integrated processing
activities, software, hardware facilities, etc. It is very difficult to keep all the activities,
applications, programs, etc. completely separate. The integration activity provides various
economies and use of common modules. Integration in physical structure can be achieved by
designing various related applications as a single system, so as to simplyfy the number of
interconnections and reduce the duplication of input. The physical structure is also influenced
by the use of common modules for many operations.

Conceptual Structure It is defined as the federation of functional subsystems, which is again

divided into four information processing components like transaction processing, operational
control, managerial control and strategic planning. Each functional subsystem must have some
unique data files and they are used only by the specific functional subsystem. Certain data files
are available for general use, which are arranged into a general database and managed by a
DBMS. A common software also can be introduced for various subsystems. The analytical and
decision models used by many applications, form the model for the information system.
 3. Decision Support Systems

Decision Support Systems (DSS)

Decision Support System is a set of well-defined, integrated, user-friendly, computer-based
tool that combines internal and external data with various decision-making models, to solve
semi-structured and unstructured problems. It is a type of system, which supports the decision-
making process, and should provide easy access to databases containing relevant data and
information.
The important types of DSS are:
▪ File drawer systems allowing immediate access to data
▪ Data analysis systems permitting data manipulation.
▪ Analysis information systems providing access to data bases and small
models.
▪ Accounting models calculating the consequences of planned actions and
generating estimates of income, balance sheet, etc.
▪ Representational models estimating the consequences of actions on the
basis of various models like simulation or risk analysis model.
▪ Optimizing models providing guidelines for actions by generating
optimal solutions.
▪ Suggesting models computing a specific, suggested, structured and
repetitive decision.

The important features of a DSS are:

▪ It facilitates semi-structured and unstructured decision-making by
bringing together data, models and human judgment.
▪ DSS can provide decision support for several interdependent decisions.
▪ It supports a wide variety of decision-making models.
▪ DSS assists the decision-maker to make decisions under dynamic
business conditions.
▪ Lastly, it helps the decision-maker by answering ad hoc queries, like the
number of machines to be operated, number of materials to be required
for a particular order, etc.

Application of a DSS

Decision problems can be divided into three categories, such as independent, interrelated and
organizational. Independent problems are those problems, the solutions to which are
independent of the others. The purpose of such decisions is simply to find the best solution to
the specific problem. For interrelated problems, the solutions are also interrelated. The purpose
of decisions in such a problem is to find out the best solution to the entire set and not just to
individual problems and it requires a team effort. Organizational problems are problems, which
affect the entire organization. Such problems also require a team effort.

Components of a DSS

The data required to solve a problem may come from internal or external databases. Internal
data are obtained by way of TPS and MIS. External data comes from a variety of ways such as
periodicals, journals, etc., and include government policy, economic indicators, inflation rates,
etc. A typical DSS consists of three different parts: knowledge database, software and user
interface.

Knowledge base.
A knowledge base is an integral part of a decision support system database, containing
information from both internal and external sources. It is a library of information related to
particular subjects and is the part of a DSS that stores information used by the system's
reasoning engine to determine a course of action.

Model Management System (software system)

It is the second component of a DSS, which stores and access models that managers use to
make decisions. Models can also be used to represent and explore systems that don't yet exist,
like a proposed new technology, a planned factory or a business's supply chain. Businesses also
use models to predict the outcomes of different changes to a system -- such as policies, risks
and regulations -- to help make business decisions. The important models are:
• Statistical models: These are used to perform a wide range of statistical functions, such
as average, standard deviation, graphical analysis, regression analysis, variance
analysis, etc.
• Financial and Accounting models: They allow the decision-maker to measure and
access the financial implication of various alternatives and include analysis of profit
and loss, cost-benefit analysis, investment analysis, etc. They are also used to calculate
various ratios and other measures of financial health and performance.
• Production models: These models are mostly used on the shop-floor to make production
related decisions, such as the number of machines to be operated, manpower
requirements, etc. 4. Marketing models: Such models include product pricing models,
store allocation, advertising strategy, product design models, etc.
• Human resource models: They help the managers to make decisions involving company
personnel, job-related issues, etc. Such models include HR, Planning, model
assessment of training needs, projecting future personnel needs, labour negotiations,
etc.

Support Tools (User interface)

This is the third component of a decision support system. It involves graphical analysis, error
correction mechanism, user interfaces, etc. Interfaces are an important support tool because
middle and top managers have neither the time nor the inclination to learn difficult and
complicated procedures; in order to run a system. The better the interface, the greater the
chances that the users will accept the system. The user interface enables easy system
navigation. The primary goal of the decision support system's user interface is to make it easy
for the user to manipulate the data that is stored on it. Businesses can use the interface to
evaluate the effectiveness of DSS transactions for the end users. DSS interfaces include simple
windows, complex menu-driven interfaces and command-line interfaces.

Functions of a DSS

The DSS has five major functions facilitating managerial decision-making. They are:

1. Model building: The function helps the managers to identify and develop decision-making
models, by considering input variables and their interrelationships, model assumptions and
constraints. A model builder uses a structured framework to identify all the variables in the
forecasting of the model, to analyse the relationship among these variables, to identify the
assumptions, if any and to identify constraints. The system then integrates all this information
into a decision-making model, which can be updated and modified whenever necessary.
2. What-if analysis: It involves the process of assessing the impact of changes in model
variation of these. For example, how much is the profit if 10 per cent increase in raw materials
cost and 5 per cent reduction in,sales effected?
3. Goal seeking: It allows the decision-maker to identify the course of action to be undertaken
to achieve a specific goal. The system addresses the question: what should be the value of the
input variables if a certain goal is to be achieved.
4. Risk analysis: It helps to calculate the risk associated with various alternatives with the help
of probabilities and various other statistical techniques. If the decision-maker prefers high risk;
then the recommendations of the system are likely to be high risk- oriented.
5. Graphical analysis: It is a display of data in an easy-to-understand format, using graphs,
charts, tables and figures. It helps managers to quickly digest large volume of data and visualize
the impact of various courses of action.

Types of decision support systems

Decision support systems can be broken down into categories, each based on their primary
sources of information.

• Data-driven DSS A data-driven DSS is a computer program that makes decisions

based on data from internal databases or external databases. Typically, a data-driven
DSS uses data mining techniques to discern trends and patterns, enabling it to predict
future events. Businesses often use data-driven DSSes to help make decisions about
inventory, sales and other business processes. Some are used to help make decisions in
the public sector, such as predicting the likelihood of future criminal behavior.
• Model-driven DSS Built on an underlying decision model, model-driven decision
support systems are customized according to a predefined set of user requirements to
help analyse different scenarios that meet these requirements. For example, a model-
driven DSS may assist with scheduling or developing financial statements.
• Communication-driven and group DSS A communication-driven and group decision
support system uses a variety of communication tools -- such as email, instant
messaging or voice chat -- to allow more than one person to work on the same task. The
goal behind this type of DSS is to increase collaboration between the users and the
system and to improve the overall efficiency and effectiveness of the system.
• Knowledge-driven DSS In this type of decision support system, the data that drives
the system resides in a knowledge base that is continuously updated and maintained by
a knowledge management system. A knowledge-driven DSS provides information to
users that is consistent with a company's business processes and knowledge.
• Document-driven DSS A document-driven DSS is a type of information management
system that uses documents to retrieve data. Document-driven DSSes enable users to
search webpages or databases, or find specific search terms. Examples of documents
accessed by a document-driven DSS include policies and procedures, meeting minutes
and corporate records.
Decision support system examples
Organizations use decision support systems in several different contexts, including the
following:
• GPS routing. GPS route planning is an example of a typical DSS. It compares different
routes, taking into account factors such as distance, driving time and cost. The GPS
navigating system also enables users to choose alternative routes, displaying them on a
map and providing step-by-step instructions.
• ERP dashboards. ERP (enterprise resource planning) dashboards can use a decision
support system to visualize changes in production and business processes, monitor
current business performance against set goals and identify areas for improvement. ERP
dashboards let business owners see a snapshot of their company's most important
numbers and metrics.
• Clinical decision support system. A clinical decision support system (CDSS) is a
software program that uses advanced decision-making algorithms to help physicians
make the best medical decisions. Healthcare professionals often use these to interpret
patient records and test results, and to calculate the best treatment plan. CDSS in
healthcare can help providers identify abnormalities during specific tests, as well as
 monitor patients after certain procedures to determine if they are having any adverse
reactions.

4. End user and Enterprise Computing Application software and System

software.

End user and Enterprise Computing Application software

End user and enterprise computing application software are two broad categories of software
applications that serve different purposes and target different user groups within the realm of
information technology. Here's an overview of each category:
1. End User Computing Application Software:
End user computing application software, also known as personal productivity software or
consumer software, is designed for individual users or small groups of users. These applications
are intended for personal or casual use and typically run on personal computers, smartphones,
tablets, and other consumer devices. They serve a wide range of purposes and are often used
for entertainment, communication, productivity, and personal tasks. Examples of end user
computing application software include:
• Word Processing Software: Such as Microsoft Word, Google Docs, or LibreOffice
Writer, used for creating and editing text documents.
• Spreadsheet Software: Like Microsoft Excel, Google Sheets, or LibreOffice Calc,
used for creating and working with spreadsheets.
• Presentation Software: Such as Microsoft PowerPoint, Google Slides, or LibreOffice
Impress, used for creating slideshows and presentations.
• Web Browsers: Like Google Chrome, Mozilla Firefox, Microsoft Edge, used for
accessing and browsing the internet.
• Email Clients: Such as Microsoft Outlook, Gmail, or Mozilla Thunderbird, used for
sending and receiving emails.
• Media Players: Like VLC Media Player, Windows Media Player, or iTunes, used for
playing audio and video files.
These applications are typically user-friendly, have intuitive interfaces, and are not typically
used for complex business operations.
2. Enterprise Computing Application Software:
Enterprise computing application software, on the other hand, is designed to support the needs
of large organizations, businesses, and institutions. These applications are often complex,
scalable, and built to handle various business processes, data management, and collaboration
requirements. Enterprise software is used to manage and automate various aspects of an
organization's operations. Examples of enterprise computing application software include:
• Enterprise Resource Planning (ERP) Software: Such as SAP, Oracle ERP, or
Microsoft Dynamics, used for managing and integrating various business processes like
finance, HR, supply chain, and manufacturing.
 • Customer Relationship Management (CRM) Software: Like Salesforce, Microsoft
Dynamics CRM, or HubSpot, used for managing customer relationships and sales
activities.
• Enterprise Content Management (ECM) Software: Such as SharePoint,
Documentum, or OpenText, used for document and content management within
organizations.
• Business Intelligence (BI) Software: Like Tableau, Power BI, or QlikView, used for
analysing and visualizing data to make informed business decisions.
• Enterprise Collaboration Software: Such as Microsoft Teams, Slack, or SharePoint,
used for facilitating communication and collaboration among teams and departments.
• Supply Chain Management (SCM) Software: Such as Oracle SCM, SAP SCM, or
Kinaxis, used for optimizing and managing the supply chain.
Enterprise software often requires significant customization and integration with existing IT
infrastructure, and it is critical for streamlining business processes and increasing efficiency
within large organizations.
In summary, end user computing application software caters to individual or small group needs,
focusing on personal tasks and productivity, while enterprise computing application software
addresses the complex and integrated requirements of large organizations, enhancing their
operational efficiency and competitiveness.
System software.
System software is a category of computer software that serves as a bridge between the
hardware components of a computer system and the application software that runs on it. It
plays a crucial role in managing and controlling the hardware resources, providing a platform
for running applications, and ensuring the overall functionality and security of the computer
system. Here are some key aspects and components of system software:
• Operating System (OS): The operating system is the core component of system
software. It acts as an intermediary between the hardware and application software,
managing tasks such as memory management, file system management, process
scheduling, device management, and user interface interaction. Common examples of
operating systems include Microsoft Windows, macOS, Linux, Android, and iOS.
• Device Drivers: Device drivers are software components that facilitate communication
between the operating system and specific hardware devices (e.g., printers, graphics
cards, network adapters). They enable the OS to control and use these devices,
translating high-level commands into instructions that the hardware can understand.
• Firmware: Firmware is a type of software that is permanently stored in hardware
components like BIOS (Basic Input/Output System) in computers or firmware in
embedded systems. It provides essential instructions for hardware initialization and
booting processes.
• Utilities: System software includes a variety of utility programs that help manage and
optimize the computer system. Examples of system utilities include disk cleanup tools,
antivirus software, backup software, and diagnostic tools.
• Middleware: Middleware is a layer of software that sits between the operating system
and application software, facilitating communication and data exchange between
 different software components. It is often used in distributed computing environments
and for building networked applications.
• Language Translators: Compilers and interpreters are essential system software tools
that convert high-level programming languages (such as C++, Java, or Python) into
machine code that can be executed by the computer's CPU.
• System Libraries: System libraries provide a collection of pre-written code and
functions that can be used by application developers to simplify tasks like input/output
operations, memory management, and more.
• Security Software: Security-related system software includes firewalls, antivirus
programs, and intrusion detection systems. These tools help protect the computer
system from malware, unauthorized access, and other security threats.
• Virtualization Software: Virtualization software, such as VMware and VirtualBox,
enables the creation and management of virtual machines, allowing multiple operating
systems to run on the same physical hardware simultaneously.
• File Management: System software includes file management utilities that help users
organize, access, and manipulate files and directories on storage devices.
System software is essential for ensuring the proper functioning of computer systems and
providing a stable and secure environment for running application software. It abstracts the
complexities of hardware and provides a user-friendly interface for interacting with the
computer. Without system software, application software would have to directly manage
hardware resources, which would be highly impractical and inefficient.
Unit-3: Managerial Overview: Database Management: Managing Data Resources, Technical
foundation of database management Resources. Fundamentals of strategic advantage, Using
Information for strategic advantage.

Database Management

Data is a collection of a distinct small unit of information. It can be used in a variety of forms
like text, numbers, media, bytes, etc. it can be stored in pieces of paper or electronic memory,
etc. Word 'Data' is originated from the word 'datum' that means 'single piece of information.' It
is plural of the word datum. A database management system (DBMS) is a software tool that
helps organize, store and retrieve data from a database. It manipulates the data format, field
name, file structure, data and record structure. Apart from managing databases, a DBMS
provides a centralized view of the data accessible to different users and different locations. As
the DBMS handles all data requests, the users do not worry about the physical location of data
or the type of media in which it resides. It involves a number of functions that collectively work
together to ensure that the data is accurate, available and accessible.

A database management system consists of three main elements:

1. A physical database that contains the data.

2. A database engine that helps to access the data and modify its contents.
3. A database scheme which provides the logical structure of the data stored in the database.

A DBMS has several components, such as:

• Data: DBMS allows data access and helps an end-user perform various functions on
the data.
• Database access language: End-users use the database access language to access the
data to and from the database. A DBMS performs many functions such as updating
existing data, adding new data and retrieving required data from the database.
• Query language: Databases require query languages to issue commands. Structured
query language (SQL) is one such database language for operating a DBMS.
• Management resources: For running a database, a DBMS requires a database manager
and run-time database manager. The database managers help maintain the data
without a run-time requirement, whereas a run-time database manager performs an
issued query.
• Query processing: Query processing is at the core of DBMS because queries tell the
DBMS what to do with the data. The DBMS processes the query issued by the coding
language and responds by performing the command on data.
 Evolution of Database Management System
The evolution of Database Management Systems (DBMS) has been a continuous process
driven by technological advancements, changing business needs, and evolving data
management paradigms. Here's an overview of the major milestones and stages in the evolution
of DBMS:
Navigational DBMS (1960s):
• Early database systems were primarily navigational, meaning data was accessed by
following explicit links or paths between records.
• Hierarchical and network DBMS were prominent examples during this period.

Relational DBMS (1970s):

• Edgar F. Codd's groundbreaking work introduced the concept of the relational model.
• The relational model represented data as tables (relations) with rows (tuples) and
columns (attributes).
• IBM's System R and Oracle's first commercial release in 1979 marked the beginning of
the relational era.
Structured Query Language (SQL) (1970s):
• SQL, a standardized query language for relational databases, was developed.
• SQL became the de facto language for interacting with relational DBMS.
Client-Server Architecture (1980s):
• The advent of client-server architecture separated the database server from application
clients.
• This architecture improved scalability and performance, and it remains a common
design today.

Object-Oriented DBMS (1980s-1990s):

• Object-oriented DBMS (OODBMS) emerged to handle more complex data structures.
• They supported modeling real-world objects and their relationships directly.
NoSQL Databases (2000s):
• As web applications and big data became prevalent, NoSQL databases (Not Only SQL)
emerged.
• NoSQL databases provided flexibility, scalability, and better handling of unstructured
and semi-structured data.
• Types include document-oriented (e.g., MongoDB), key-value (e.g., Redis), column-
family (e.g., Cassandra), and graph databases (e.g., Neo4j).

NewSQL Databases (2010s):

 • NewSQL databases aimed to combine the scalability of NoSQL with the ACID
(Atomicity, Consistency, Isolation, Durability) properties of traditional SQL databases.
• Examples include Google Spanner and CockroachDB.

Distributed Databases (2010s):

• Distributed databases became essential for handling data at scale.
• Technologies like Apache Hadoop and Apache Spark allowed distributed processing of
big data.
Cloud Databases (2010s-2020s):
• Cloud providers (e.g., AWS, Azure, Google Cloud) began offering managed database
services.
• These services simplified database deployment, scaling, and management in the cloud.

Graph Databases (2010s-2020s):

• Graph databases gained popularity for modeling and querying complex relationships in
data.
• Neo4j and Amazon Neptune are examples of graph databases.
Multi-Model Databases (2020s):
• Multi-model databases emerged to support multiple data models (e.g., document, graph,
relational) within a single system.
Blockchain Databases (2020s):
• Blockchain technology introduced distributed, tamper-resistant ledgers for various
applications, including secure data management.
AI and Machine Learning Integration (2020s):
• DBMS are increasingly integrating AI and machine learning capabilities for data
analytics, optimization, and automation.

DBMS continue to evolve in response to the growing complexity of data management

challenges, including the proliferation of data types, the need for real-time processing, and the
demand for greater scalability and security. The future of DBMS is likely to be shaped by
advancements in technologies like quantum computing, edge computing, and continued
developments in AI and machine learning.

Types of Databases
Database Management Systems (DBMS) are software applications that manage and organize
data in a structured way. There are several types of DBMS, each designed to meet specific data
management needs. Here are some common types of DBMS:
1) Centralized Database
It is the type of database that stores data at a centralized database system. It comforts the users
to access the stored data from different locations through several applications. These
applications contain the authentication process to let users access data securely. An example of
a Centralized database can be Central Library that carries a central database of each library in
a college/university.
Advantages of Centralized Database
• It has decreased the risk of data management, i.e., manipulation of data will not affect
the core data.
• Data consistency is maintained as it manages data in a central repository.
• It provides better data quality, which enables organizations to establish data standards.
• It is less costly because fewer vendors are required to handle the data sets.
Disadvantages of Centralized Database
• The size of the centralized database is large, which increases the response time for
fetching the data.
• It is not easy to update such an extensive database system.
• If any server failure occurs, entire data will be lost, which could be a huge loss.
2) Distributed Database
Unlike a centralized database system, in distributed systems, data is distributed among different
database systems of an organization. These database systems are connected via communication
links. Such links help the end-users to access the data easily. Examples of the Distributed
database are Apache Cassandra, HBase, Ignite, Oracle RAC, Google Cloud Bigtable.
We can further divide a distributed database system into:
 o Homogeneous DDB: Those database systems which execute on the same operating
system and use the same application process and carry the same hardware devices.
o Heterogeneous DDB: Those database systems which execute on different operating
systems under different application procedures, and carries different hardware devices.

Advantages of Distributed Database

• Modular development is possible in a distributed database, i.e., the system can be
expanded by including new computers and connecting them to the distributed system.
• One server failure will not affect the entire data set.

3) Relational Database

This database is based on the relational data model, which stores data in the form of rows(tuple)
and columns(attributes), and together forms a table(relation). A relational database uses SQL
for storing, manipulating, as well as maintaining the data. E.F. Codd invented the database in
1970. Each table in the database carries a key that makes the data unique from
others. Examples of Relational databases are MySQL, Microsoft SQL Server, Oracle, etc.

Properties of Relational Database

There are following four commonly known properties of a relational model known as ACID
properties, where:
• A means Atomicity: This ensures the data operation will complete either with success
or with failure. It follows the 'all or nothing' strategy. For example, a transaction will
either be committed or will abort.
• C means Consistency: If we perform any operation over the data, its value before and
after the operation should be preserved. For example, the account balance before and
after the transaction should be correct, i.e., it should remain conserved.
• I mean Isolation: There can be concurrent users for accessing data at the same time
from the database. Thus, isolation between the data should remain isolated. For
example, when multiple transactions occur at the same time, one transaction effects
should not be visible to the other transactions in the database.
• D means Durability: It ensures that once it completes the operation and commits the
data, data changes should remain permanent.
4) NoSQL Database
Non-SQL/Not Only SQL is a type of database that is used for storing a wide range of data sets.
It is not a relational database as it stores data not only in tabular form but in several different
ways. It came into existence when the demand for building modern applications increased.
Thus, NoSQL presented a wide variety of database technologies in response to the demands.
We can further divide a NoSQL database into the following four types:
• Key-value storage: It is the simplest type of database storage where it stores every
single item as a key (or attribute name) holding its value, together. Examples: Redis,
Amazon DynamoDB.
 • Document-oriented Database: A type of database used to store data as JSON-like
document. It helps developers in storing data by using the same document-model format
as used in the application code. Examples: MongoDB, Couchbase.
• Graph Databases: It is used for storing vast amounts of data in a graph-like structure.
Most commonly, social networking websites use the graph database. Examples: Neo4j,
Amazon Neptune
• Wide-column stores: It is similar to the data represented in relational databases. Here,
data is stored in large columns together, instead of storing in rows. Examples: Apache
Cassandra, HBase.
Advantages of NoSQL Database
• It enables good productivity in the application development as it is not required to
store data in a structured format.
• It is a better option for managing and handling large data sets.
• It provides high scalability.
• Users can quickly access data from the database through key-value.
5) Cloud Database
A type of database where data is stored in a virtual environment and executes over the cloud
computing platform. It provides users with various cloud computing services (SaaS, PaaS,
IaaS, etc.) for accessing the database. There are numerous cloud platforms, but the best options
are:
• Amazon Web Services (AWS)
• Microsoft Azure
• Kamatera
• PhonixNAP
• ScienceSoft
• Google Cloud SQL, etc.
6) Object-oriented Databases
The type of database that uses the object-based data model approach for storing data in the
database system. The data is represented and stored as objects which are similar to the objects
used in the object-oriented programming language. Examples: ObjectStore, db4o.
7) Hierarchical Databases
It is the type of database that stores data in the form of parent-children relationship nodes. Here,
it organizes data in a tree-like structure. Data get stored in the form of records that are connected
via links. Each child record in the tree will contain only one parent. On the other hand, each
parent record can have multiple child records.
8) Network Databases
It is the database that typically follows the network data model. Here, the representation of data
is in the form of nodes connected via links between them. Unlike the hierarchical database, it
allows each record to have multiple children and parent nodes to form a generalized graph
structure.
9) In-Memory Database Management System:
• In-memory databases store data entirely in RAM for faster data access.
• They are well-suited for applications requiring low-latency data retrieval.
• Examples: SAP HANA, Redis (can also be used as an in-memory store).
10) Columnar Database Management System:
• Columnar databases store data in columns rather than rows, which can optimize query
performance for analytics and reporting.
• Examples: Amazon Redshift, Google BigQuery.
11) NewSQL Database Management System:
• NewSQL databases aim to combine the scalability of NoSQL with the ACID properties
of traditional SQL databases.
• They are designed for high availability and fault tolerance.
• Examples: Google Spanner, CockroachDB.
12) Time-Series Database Management System:
• Time-series databases specialize in storing and querying time-stamped data points.
• They are commonly used in IoT and monitoring applications.
• Examples: InfluxDB, Prometheus.
13) Spatial Database Management System (GIS):
• Spatial databases manage geospatial data and support spatial queries and analysis.
• They are used in geographic information systems (GIS) and location-based services.
• Examples: PostGIS (extension for PostgreSQL), Oracle Spatial.
14) Blockchain Database Management System:
• Blockchain databases use distributed ledger technology to provide immutable and
transparent data storage.
• They are commonly used for applications like cryptocurrencies and supply chain
management.
• Examples: Bitcoin (public blockchain), Hyperledger Fabric (enterprise blockchain).
These are some of the primary types of DBMS, and in practice, hybrid systems may combine
features from multiple types to meet specific requirements. The choice of DBMS depends on
factors such as data structure, scalability, performance, and the specific needs of the application
or organization.

Relational Database Management System

RDBMS stands for Relational Database Management System. All modern database
management systems like SQL, MS SQL Server, IBM DB2, ORACLE, My-SQL, and
Microsoft Access are based on RDBMS. It is called Relational Database Management System
(RDBMS) because it is based on the relational model introduced by E.F. Codd.
How it works: - Data is represented in terms of tuples (rows) in RDBMS. A relational database
is the most commonly used database. It contains several tables, and each table has its primary
key. Due to a collection of an organized set of tables, data can be accessed easily in RDBMS.

Following are the various terminologies of RDBMS:

Difference between DBMS and RDBMS

Although DBMS and RDBMS both are used to store information in physical database but there
are some remarkable differences between them.
The main differences between DBMS and RDBMS are given below:

No. DBMS RDBMS

1) DBMS applications store data as RDBMS applications store data in a tabular form.
file.

2) In DBMS, data is generally In RDBMS, the tables have an identifier called

stored in either a hierarchical primary key and the data values are stored in the
form or a navigational form. form of tables.

3) Normalization is not present in Normalization is present in RDBMS.

DBMS.

4) DBMS does not apply any RDBMS defines the integrity constraint for the
security with regards to data purpose of ACID (Atomocity, Consistency,
manipulation. Isolation and Durability) property.

5) DBMS uses file system to store in RDBMS, data values are stored in the form of
data, so there will be no relation tables, so a relationship between these data values
between the tables. will be stored in the form of a table as well.
 6) DBMS has to provide some RDBMS system supports a tabular structure of the
uniform methods to access the data and a relationship between them to access the
stored information. stored information.

7) DBMS does not support RDBMS supports distributed database.

distributed database.

8) DBMS is meant to be for small RDBMS is designed to handle large amount of

organization and deal with small data. it supports multiple users.
data. it supports single user.

9) Examples of DBMS are file Example of RDBMS are mysql, postgre, sql
systems, xml etc. server, oracle etc.

After observing the differences between DBMS and RDBMS, you can say that RDBMS is an
extension of DBMS. There are many software products in the market today who are compatible
for both DBMS and RDBMS. Means today a RDBMS application is DBMS application and
vice-versa.

DBMS vs. File System

File System Approach: - File based systems were an early attempt to computerize the manual
system. It is also called a traditional based approach in which a decentralized approach was
taken where each department stored and controlled its own data with the help of a data
processing specialist. The main role of a data processing specialist was to create the necessary
computer file structures, and also manage the data within structures and design some
application programs that create reports based on file data.
In the above figure:
Consider an example of a student's file system. The student file will contain information
regarding the student (i.e. roll no, student name, course etc.). Similarly, we have a subject file
that contains information about the subject and the result file which contains the information
regarding the result. Some fields are duplicated in more than one file, which leads to data
redundancy. So, to overcome this problem, we need to create a centralized system, i.e.,
DBMS approach.

DBMS: A database approach is a well-organized collection of data that are related in a

meaningful way which can be accessed by different users but stored only once in a system. The
various operations performed by the DBMS system are: Insertion, deletion, selection, sorting
etc.

In the above figure,

In the above figure, duplication of data is reduced due to centralization of data.
There are the following differences between DBMS and File systems:

Basis DBMS Approach File System Approach

Meaning DBMS is a collection of data. In The file system is a collection of data. In

DBMS, the user is not required this system, the user has to write the
to write the procedures. procedures for managing the database.
Sharing of Due to the centralized approach, Data is distributed in many files, and it
data data sharing is easy. may be of different formats, so it isn't
easy to share data.

Data DBMS gives an abstract view of The file system provides the detail of the
Abstraction data that hides the details. data representation and storage of data.

Security and DBMS provides a good It isn't easy to protect a file under the file
Protection protection mechanism. system.

Recovery DBMS provides a crash The file system doesn't have a crash
Mechanism recovery mechanism, i.e., mechanism, i.e., if the system crashes
DBMS protects the user from while entering some data, then the content
system failure. of the file will be lost.

Manipulation DBMS contains a wide variety The file system can't efficiently store and
Techniques of sophisticated techniques to retrieve the data.
store and retrieve the data.

Concurrency DBMS takes care of Concurrent In the File system, concurrent access has
Problems access of data using some form many problems like redirecting the file
of locking. while deleting some information or
updating some information.

Where to use Database approach used in large File system approach used in large
systems which interrelate many systems which interrelate many files.
files.

Cost The database system is The file system approach is cheaper to

expensive to design. design.

Data Due to the centralization of the In this, the files and application programs
Redundancy database, the problems of data are created by different programmers so
and redundancy and inconsistency that there exists a lot of duplication of
Inconsistency are controlled. data which may lead to inconsistency.

Structure The database structure is The file system approach has a simple
complex to design. structure.

Data In this system, Data In the File system approach, there exists
Independence Independence exists, and it can no Data Independence.
be of two types.
Logical Data Independence
Physical Data Independence

Integrity Integrity Constraints are easy to Integrity Constraints are difficult to

Constraints apply. implement in file system.
 Data Models In the database approach, 3 In the file system approach, there is no
types of data models exist: concept of data models exists.
Hierarchal data models
Network data models
Relational data models

Flexibility Changes are often a necessity to The flexibility of the system is less as
the content of the data stored in compared to the DBMS approach.
any system, and these changes
are more easily with a database
approach.

Examples Oracle, SQL Server, Sybase etc. Cobol, C++ etc.

DBMS Architecture
• The DBMS design depends upon its architecture. The basic client/server architecture is
used to deal with a large number of PCs, web servers, database servers and other
components that are connected with networks.
• The client/server architecture consists of many PCs and a workstation which are
connected via the network.
DBMS architecture depends upon how users are connected to the database to get their request
done.
Types of DBMS Architecture

Database architecture can be seen as a single tier or multi-tier. But logically, database
architecture is of two types like: 2-tier architecture and 3-tier architecture.
1-Tier Architecture
• In this architecture, the database is directly available to the user. It means the user can
directly sit on the DBMS and uses it.
 • Any changes done here will directly be done on the database itself. It doesn't provide a
handy tool for end users.
• The 1-Tier architecture is used for development of the local application, where
programmers can directly communicate with the database for the quick response.
2-Tier Architecture
• The 2-Tier architecture is same as basic client-server. In the two-tier architecture,
applications on the client end can directly communicate with the database at the server
side. For this interaction, API's like: ODBC, JDBC are used.
• The user interfaces and application programs are run on the client-side.
• The server side is responsible to provide the functionalities like: query processing and
transaction management.
• To communicate with the DBMS, client-side application establishes a connection with
the server side.

Fig: 2-tier Architecture

3-Tier Architecture
• The 3-Tier architecture contains another layer between the client and server. In this
architecture, client can't directly communicate with the server.
• The application on the client-end interacts with an application server which further
communicates with the database system.
• End user has no idea about the existence of the database beyond the application server.
The database also has no idea about any other user beyond the application.
• The 3-Tier architecture is used in case of large web application.
 Fig: 3-tier Architecture

Managing Data Resources

Database management involves the efficient and organized storage, retrieval, and management
of data within an organization. It is essential for businesses, institutions, and organizations to
make informed decisions, maintain records, and streamline processes. Here are some key
aspects of managing data resources and the technical foundations of database management:
Data Resource Management (DRM):
• Definition: Data resource management (DRM) is the practice of planning, controlling,
and protecting data assets within an organization to ensure their integrity, availability,
and security.
• Objectives: The primary goals of DRM include data quality assurance, data security,
data governance, and compliance with data-related regulations.
• Data Governance: Establishing policies, procedures, and standards for data
management to ensure data accuracy, consistency, and reliability.
• Data Security: Implementing security measures, access controls, and encryption to
protect data from unauthorized access or breaches.
• Data Quality Management: Ensuring that data is accurate, complete, and consistent
through data validation, cleaning, and normalization processes.
• Master Data Management (MDM): Managing critical data entities (e.g., customers,
products) centrally to ensure data consistency across the organization.

Technical Foundations of Database Management

• Relational Database Management System (RDBMS): An RDBMS is a software
system used to manage relational databases. It stores data in structured tables with
predefined schemas and supports SQL (Structured Query Language) for querying and
manipulation.
 • Data Modeling: The process of defining the structure and relationships of data
elements within a database. Common data models include Entity-Relationship
Diagrams (ERD) and UML diagrams.
• Normalization: The process of organizing data in a database to reduce data redundancy
and improve data integrity. Normal forms (e.g., 1NF, 2NF, 3NF) are used to achieve
this.
• Indexing: Creating indexes on database tables to speed up data retrieval operations.
Indexes help optimize query performance by allowing for faster data lookups.
• Transaction Management: Ensuring the integrity of data during database operations.
This involves implementing features like ACID properties (Atomicity, Consistency,
Isolation, Durability) to maintain data consistency.
• Data Backup and Recovery: Implementing regular data backups and recovery
procedures to protect against data loss due to hardware failures or other unforeseen
events.
• Data Security: Implementing security mechanisms such as access control, encryption,
and authentication to protect sensitive data from unauthorized access.
• Scalability: Designing databases to handle growing data volumes and user loads.
Scalability can be achieved through techniques like sharding, clustering, and
partitioning.
• Data Warehousing: Storing and managing large volumes of historical data for
analytical purposes. Data warehousing involves techniques like ETL (Extract,
Transform, Load) and OLAP (Online Analytical Processing).
• Big Data Management: Handling and analysing massive datasets using technologies
like Hadoop, NoSQL databases, and distributed computing frameworks.
• Cloud Database Services: Leveraging cloud-based database platforms (e.g., AWS
RDS, Azure SQL Database, Google Cloud SQL) for scalable, cost-effective database
management.
Effective database management is crucial for organizations to extract valuable insights from
their data, improve decision-making, enhance customer experiences, and ensure data security
and compliance with regulatory requirements. It requires a combination of technical expertise,
sound data governance practices, and the right tools and technologies.
 Fundamentals of strategic advantage of DBMS

A Database Management System (DBMS) is a critical component of modern organizations'

information systems. Leveraging a DBMS effectively can provide several strategic advantages.
Let's explore the fundamentals of these advantages and how organizations can use information
for strategic gain:
1. Data Centralization:
• Efficient Data Storage: A DBMS centralizes data storage, allowing organizations to
store and manage vast amounts of data efficiently. This reduces redundancy and data
inconsistencies.
• Data Accessibility: Centralized data can be accessed by authorized users from various
locations, promoting collaboration and information sharing.
2. Data Integrity and Security:
• Data Consistency: A DBMS enforces data integrity constraints, ensuring data accuracy
and consistency.
• Access Control: DBMS systems provide robust security features, allowing
organizations to control who can access, modify, and delete data.
3. Data Analytics and Decision Support:
• Data Analysis: DBMS systems enable organizations to run complex queries and
perform data analytics to gain insights into business operations, customer behavior, and
market trends.
• Data Warehousing: Data can be transformed into a data warehouse, facilitating strategic
decision-making based on historical and real-time data.
4. Scalability and Performance:
• Scalability: Organizations can scale their DBMS to handle growing volumes of data
and users.
• Performance Optimization: DBMS systems are designed to optimize query
performance, ensuring that data retrieval is swift, critical for real-time decision-making.
5. Business Process Automation:
• Integration: DBMS systems can integrate with other software and systems, automating
various business processes.
• Workflow Management: They can facilitate workflow management, streamlining
operations and reducing manual tasks.
6. Competitive Advantage:
• Customer Insights: Analysing customer data stored in a DBMS helps in understanding
customer preferences, enabling personalized marketing and product development.
• Market Trends: Analysing industry data can help organizations stay ahead of market
trends and respond to changes more effectively.
7. Compliance and Reporting:
• Regulatory Compliance: DBMS systems can assist in adhering to data privacy
regulations by providing audit trails and access controls.
• Reporting: They enable organizations to generate accurate and timely reports for
internal and external stakeholders, supporting compliance and strategic decision-
making.
8. Disaster Recovery and Business Continuity:
• Data Backups: DBMS systems facilitate data backups and disaster recovery planning,
ensuring data availability in case of unexpected events.
9. Competitive Intelligence:
• Market Research: By analysing external data sources, organizations can gather
competitive intelligence, helping them make informed decisions in a competitive
landscape.
10. Innovation and Agility:
• Rapid Development: DBMS systems enable the rapid development of new applications
and services, fostering innovation and agility in responding to changing market
dynamics.
In summary, a well-implemented DBMS serves as a strategic asset for organizations by
centralizing data, ensuring its integrity and security, facilitating data analysis, and supporting
critical decision-making processes. Leveraging these advantages allows organizations to stay
competitive, compliant, and responsive to market changes. However, it's essential to
continually invest in technology and expertise to maximize the strategic advantages of a
DBMS.

Using Information for strategic advantage

Using information strategically is a key driver for organizations seeking a competitive edge in
today's data-driven world. Leveraging information effectively can lead to improved decision-
making, innovation, cost reduction, and overall business success. Here are some ways
organizations can use information for strategic advantage:
1. Data-driven Decision Making: Organizations can use data and analytics to inform
their decision-making processes. This includes making strategic decisions related to
product development, marketing campaigns, resource allocation, and more.
2. Customer Insights: By analysing customer data, organizations can gain valuable
insights into customer preferences, behavior, and needs. This information can be used
to tailor products, services, and marketing efforts to specific customer segments.
3. Competitor Analysis: Gathering and analysing data on competitors' strategies, market
share, and performance can help organizations identify opportunities and threats,
allowing them to make informed strategic decisions.
4. Market Trends and Forecasting: Organizations can use historical and real-time data
to identify market trends and forecast future demand for their products or services. This
 information can guide inventory management, production planning, and pricing
strategies.
5. Operational Efficiency: Information can be used to optimize internal processes and
improve operational efficiency. This includes streamlining supply chains, reducing
waste, and enhancing productivity.
6. Innovation and Product Development: Data can drive innovation by providing
insights into areas where new products or services are needed. It can also guide the
development of existing products based on customer feedback and market trends.
7. Personalization and Customer Experience: Organizations can use data to personalize
customer experiences. This includes tailoring marketing messages, product
recommendations, and user interfaces to individual preferences.
8. Risk Management: Information can help organizations identify and mitigate risks.
This includes financial risk assessment, cybersecurity threat detection, and compliance
monitoring.
9. Cost Reduction: Data can be used to identify cost-saving opportunities by analyzing
areas of inefficiency, optimizing resource allocation, and reducing waste.
10. Supply Chain Optimization: Information can be used to optimize the supply chain by
improving inventory management, demand forecasting, and supplier relationships.
11. Strategic Partnerships: Data can help identify potential strategic partners or alliances
that can provide access to new markets, technologies, or resources.
12. Employee Productivity and Engagement: Organizations can use data to assess
employee performance, identify training needs, and enhance employee engagement,
leading to a more productive workforce.
13. Compliance and Risk Mitigation: Ensuring compliance with regulatory requirements
and proactively addressing potential risks based on data analysis can protect the
organization from legal and reputational issues.
14. Feedback Loops: Continuous collection and analysis of feedback from customers,
employees, and other stakeholders can drive iterative improvements and keep the
organization responsive to changing needs.
15. Long-term Strategic Planning: Information can inform long-term strategic planning
by providing insights into emerging technologies, market disruptions, and evolving
customer preferences.
In conclusion, using information strategically is a multifaceted approach that involves
collecting, analysing, and acting on data to gain a competitive advantage. Organizations that
can harness the power of information effectively are better positioned to adapt to changing
environments, make informed decisions, and thrive in their respective industries.