AKTU MBA II semester " Management Information System " Notes all units

 

MANAGEMENT INFORMATION SYSTEMS

Course Credit: 2 Contact Hours: 20

Course Objective

1. To help the students understand the importance of information management in business and

management

2. To provide understanding about different types of information systems in business

3. To apply the theory and concepts in practical with help of software

4. To understand various security and ethical issues with Information Systems

5. To provide hands on learning of applications on Spreadsheet and database software

UNIT -1 (6 Hours)

Management Information Systems - Need, Purpose and Objectives, Contemporary Approaches to

MIS, Information as a strategic resource, Use of information for competitive advantage, MIS as an

instrument for the organizational change. Information Technology – Characteristics and emerging

trends, IT Capabilities and their organizational impact, IT enabled services. Transaction Processing

System: Characteristics and its importance

UNIT -II (6 Hours)

Information, Management and Decision Making - Attributes of information and its relevance to

Decision Making, Types of information. Models of Decision Making - Classical, Administrative and

Herbert Simon's Models. Management Support Systems: Decision Support Systems, Group Decision

Support Systems, and Executive Information Systems.

UNIT -III (8 Hours)

Managing Data Resources- The need for data management, Challenges of data management, Data

independence, Data redundancy, Data consistency, Data administration. Database Management

System – Concepts and types of DBMS, Fields, Records, Table, View, Reports and Queries. Data

warehouse and Data mining – Characteristics and uses of Data warehouse, Techniques of Data

Mining, Business Intelligence

Database Management System (Lab): Creation of Table, View and Reports. Basics of SQL and

running queries

*************************************************************************************

UNIT I

Management Information Systems - Need, Purpose and Objectives, Contemporary Approaches to

MIS, Information as a strategic resource, Use of information for competitive advantage, MIS as an

instrument for the organizational change. Information Technology – Characteristics and emerging

trends, IT Capabilities and their organizational impact, IT enabled services. Transaction Processing

System: Characteristics and its importance

Management Information Systems - Need, Purpose and Objectives

Management Information Systems definition:

A management information system (MIS) is an information system used for decision-making, and for the coordination, control, analysis, and visualization of information in an organization. The study of the management information systems involves people, processes and technology in an organizational context.

Objectives of Management Information System:

The following are the objectives of a management information system:

 

1. MIS is very useful for efficient and effective planning and control functions of the management. Management is the art of getting things done through others. MIS will be instrumental in getting the things done by providing quick and timely information to the management.

2. Reports give an idea about the performance of men, materials, machinery, money and management. Reports throw light on the utilization of resources employed in the organization.

 

3. MIS is helpful in controlling costs by giving information about idle time, labour turnover, wastages and losses and surplus capacity.

 

4. By making comparison of actual performance with the standard and budgeted performance, variances are brought to the notice of the management by MIS which can be corrected by taking remedial steps.

 

5. MIS brings to the notice of the management strength (i.e., strong points) of the organisation, to take advantage of the opportunities available.

 

Need of Management Information System

 

An organisation must have a very clear version about requirements such as type of information required,  type of data available , type of stakeholders  etc,  at  different levels of Management. An Organisation established and MIS for the following reasons:

Efficiently storing and managing data of all business functional areas.

Fast and accurate delivery of information, as and when needed.

Processing of gathered data and developing information from it.

Information availability for production and inventory.

Providing information about current economic status of company.

Faster implementation of results available from reliable data sources.

Smooth flow of data within various levels of organisation.

Make availability of information required for planning organizing and monitoring business process.

Purpose and Objectives of Management Information System

1) Data Capturing :

MIS  gathers information from various internal and external either manually or electronically with the use of computer terminals.

2) Processing of Data :

The collected data goes through a number of process like calculation , sorting, classification and summary for its conversion information.

3) Storage of information :

The processed or the  unprocessed data  stored in the MIS  for future use by saving it as an organisation record. The data may also be used immediately.

4) Retrieval Of Information :

As per the request of different uses, retrieval of information is done by MIS  from its stores.

5) Dissemination of information:

Information is the final product of MIS which is equally accessible by all users  in the  organisation. It may be periodic or  online with the use of computer terminal.

Advantages of Management Information System

1)Facilitates  planning :

Management Information System provides relevant information for efficient decision making for stop with the increasing size and complexity of organisation, managers now work remotely rather than from the operations location. MIS proves to be a big help in such scenarios.

2) Minimises Information Overload :

MIS help in  compartmentalizing data into smaller relevant parts of decision-making. This reduces the confusion of large can organised data.

3) MIS Encourages Decentralization :

MIS enables decentralization of authority. This is possible as there and minority system at lower levels of measuring performance. This helps in making changes in organisational plans and procedures.

4) Brings Co-ordination :

MIS connect all decision making nodes in an organisation . it assist in assimilation of specialized activities whereby each department becomes aware of the problems and requirement of other departments . this ensures smooth functioning of an organisation.

5) Makes Control Easier :

MIS at as an important tool to relate managerial planning and control. MIS increases the data processing and storage capacity as well as reduces the cost with the help of computer. It enhance the managements capability to evaluate and improve performance.

Disadvantage of Management Information System

1)Highly sensitive, requires constant monitoring:

MIS content highly sensitive information about an organisation which can be used for fraudulent activities. Constant monitoring and filtering is required to avoid manipulation of data by fraudster causing harm to business.

2) Budgeting of MIS is Extremely Difficult:

MIS cannot be budgeted like activities of all other department. Hence, its expense is unpredictable. Even though it forms a very sensitive and important function of organisation but it is not possible to predict its expense.

3) Quality of Outputs Governed by Quality of Input :

The quality of the information generated through MIS is dependent on the quality of raw data used for processing.

4) Lack of Flexibility to Update Itself :

MIS Cannot update itself automatically like many other application. Updating has to be done manually by obtaining raw data and feeding it into the system for processing and updating pre existing data.

5) Effectiveness Decrease Due to Frequent Changes in Top Management :

Frequent changes in middle or top management levels reduces the effectiveness of information produced through MIS, as requirements of reports are the result of input provided by the top management level . Change in management result in chair changed information requirement because new team of management will require information on their own format.

6) Takes into Account only Qualitative Factors :

MIS takes into consideration only qualitative factors, ignoring the non qualitative factors such as morale, attitude, and motivation of workers. is the biggest limitation of MIS.

Contemporary Approaches to MIS

When an information system is being developed, much importance should be given to the structure of the organization, culture of the organization, etc. But along with these, especial attention should also be given to the technical side of MS. The various contemporary approaches to MIS development can be summarized as

 

 

 

The Socio Technical Approach

 

Behavioral Approach

 

Information systems are socio-technical systems. Although they are composed of machines, devices, and "hard" physical technology, they require substantial social, organizational, and intellectual investments to make them work properly. Since problems with information systems and their solutions are rarely all technical or behavioral, a multidisciplinary approach is needed.

 

a) In the beginning, this approach was finding it hard to survive but now it is being accepted worldwide and is also being implemented at a very large scale.

 

b) Involves key involvement of both of the above explained approaches.

 

c) Improves the performance of the information system as a whole.

 

The Behavioral Approach

 

a) Based on the impact of the behavior and also on the response of the people in the organization

 

b) Motivational Feasibility forms a very important and demanding part of such an approach towards MIS

 

development.

 

The Technical Approach

 

Computer science

 

Theories of computability Methods of computation

 

Methods of efficient data storage and access

 

Management science

 

• Models for decision making

 

Management practices

 

Operations research

 

• Mathematical techniques for optimizing selected parameters of organisations, such as

 

transportation, inventory control, etc.

 

a) Based on the mathematical and the normative models.

 

b) Physical technology forms the backbone of such an approach.

 

c) Such an approach mainly finds much needed contributions from the disciplines like computer science, management science, operations research etc.

 

Porter Millar postulates

 

Porter and Millar were the ones, who explained the affect of the information technology on the competition. According to them information technology is affecting competition in the following ways:

 

a) Causes changes in the structure of the industry and as a result of this, rule of competitions is altered.

 

b) Spawning of the whole new business takes place, and in much of the cases - it is caused from within the company's existing operations.

 

c) Competitive advantage is created usually because of the new ways; the companies get to outperform their rivals.

 

Information as a strategic resource, Use of information for competitive advantage

Business environment is prone to changes and this factor makes business planning very complex. Some factors such as the market forces, technological changes, complex diversity of business and competition have a significant impact on any business prospects. MIS is designed to assess and monitor these factors. The MIS design is supposed to provide some insight into these factors enabling the management to evolve some strategy to deal with them.

 

Since these factors are a part of the environment, MIS design is required to keep a watch on environment factors and provide information to the management for a strategy formulation. Strategy formulation is a complex task based on the strength and the weakness of the organization and the mission and goals it wishes to achieve. Strategy formulation is the responsibility of the top management and the top management relies on the MIS for information.

 

Learn a language without trying.

 

Meet Toucan, the simple tool that helps you learn a new language while browsing the web.

 

There are various business strategies such as overall company growth, product, market, financing and so on. MIS should provide the relevant information that would help the management in deciding the type of strategies the business needs. Every business may not require all the strategies all the time.

 

Information is Strategic Resources. Because Information helps in taking Strategic, Tactical and operational Decisions. It is one of critical and importance resource.

 

1. It helps us understand Cost, Quality, price, technology, productivity and product.

 

2. It helps to smooth following of business process and there by smooth managing of business operation. 3. It helps to maintain the business standards like ISO, QS, CMMI, Six Sigma etc.

 

4. It helps to be ahead in the competition

 

5. It helps company in analyzing their own SWOT

 

6. It helps in maintaining its own profitability.

 

7.It will help in taking new business decisions like new plant, new product, new business line etc. 8. It protects company from business cycles.

 

9.It provides future direction to the organization.

 

10. It provides the competitive edge.

 

Use of information for Competitive Advantages

 

the study of information management entails an understanding of information and communication technology also. However, information management is a distinct subject not related (other than the practical considerations of providing the output information timely and accurately) to information and communication technology. Let us now delve deeper into the subject to get a clear understanding of the basic concepts that drive management information systems.

 

According Porter Miller: Information helps in following:

 

1. Change in industry structure: This includes Five Forces:

 

• Customers' bargaining power

 

Suppliers' bargaining power

 

.Threats of new entrant in market

 

• Pressure from substitute products and services and

 

Existing Industry competitors

 

2. Birth of new business/new business initiatives

 

3. New ways of doing business

 

Functional Use:

 

• Lower the cost

 

• Information and information system facilitate value chain e.g. product delivery, quality In increased the speed, accuracy and timeliness of the organization

 

.

It helps in simplifying the business processes

 

It helps organization in meeting the standards and benchmarks

 

Strategic Use:

New way of doing the work

 

New way of dealing in Product differentiation

 

It helps in new way of developing strategies, planning, forecasting and monitoring

 

It helps in problem solving and decision making by extensive internal and external data analysis. It improves the ability to perform

 

It helps in getting advantages of market situation and keeps ahead in the competition.

 

It helps in eliminating waste, inefficiencies and gaps in the business operations

 

• Provides the flexibility and helps manage the uncertainties

 

Analysis external information and making use of business

 

Strategic Information System can offer competitive advantage to an organization:

 

1) Generating databases to improve marketing: An information system also provides companies an edge over their competition by generating databases to improve their sales and marketing strategies. Such systems treat existing information as a resource. For example, an organization may use its databases to monitor the purchase made by its customers, to identify different segments of the market, etc.

 

2) Creating barriers to competitor's entry: In this strategy, an organization uses information systems to provide products or services that are difficult to duplicate or that are used to serve highly specialized markets. This prevents the entry of competitors as they find the cost for adopting a similar strategy very high.

 

3) 'Locking in' customers and suppliers: Another way of gaining competitive advantage is by locking in customers and suppliers. In this concept, information systems are used to provide such advantages to a customer or a supplier, that it becomes difficult for them to switch over to a competitor. For example, an organization may develop its information system and give many benefits to its customers, like reliable order filling, reduced transaction costs, increased management support and faster delivery service.

 

4) Leveraging technology in the value chain: This approach pinpoints specific activities in the business where competitive strategies can be best applied and where information systems are likely to have a greater strategic impact. This model advocates that information technology can best be used to gain competitive advantages by identifying specific, critical leverage points.

 

5) Lowering the costs of the products: strategic information systems may also help organizations lower their internal costs, allowing them to deliver products and services at a lower price than their competitors can provide. Thus, such information systems can contribute to the survival and growth of the organization. For example, airlines use information systems strategically to lower costs so that they may counter competitor's discount fares.

 

 

MIS as an instrument for the organizational change

 

 

MIS can deliver facts, data and trends to businesses with lightning speed. Having this information allows companies to react quickly to market changes, regardless of the type (positive or negative) of volatility.

 

MIS acts as an agent or a catalyst to bring about organisational change that is needed to cope up with the changing

 

business environment and the effect of external forces. MIS has shifted from back office to front office. Information system

 

professionals are conversant with constant change and rapid rate of technological obsolescence, offers a "Cockpit view" to

 

managers.

 

The role of the MIS in an organization can be compared to the role of heart in the body. The information is the blood and MIS is the heart in the body, the heart plays the role of supplying pure blood to all the elements of the body including the

 

brain.

 

The heart works faster and supplies more blood when needed. It regulates and controls the incoming impure blood,

 

processes it and sends it to the destination in the quantity needed, it fulfills the needs of blood supply to human body in normal course and also in crisis. The MIS plays exactly the same role in the organization. The system ensures that an appropriate data is collected from the

 

various sources, processed, and sent further to all the needy destinations. The system is expected to fulfill the information

 

needs of an individual, a group of individuals, the management functionaries the managers and the top management.

 

The MS satisfies the diverse needs through a variety of systems such as Query Systems, Analysis Systems, Modelling

 

Systems and Decision Support Systems. The MIS helps in Strategic Planning Management Control, Operational Control

 

and Transaction Processing

 

The MS helps the clerical personnel in the transaction processing and answers their queries on the data pertaining to the transaction, the status of a particular record and references on a variety of documents The MS helps the juniori management personnel by providing the operational data for planning, scheduling and Controlling and helps them further in decision making at the operations level to correct an out of control situation. The MS helps the middle management in short-term planning, target setting and controlling the business functions, it is supported by the use of the management tools of planning and control. The M/S helps the top management in goal setting, strategic planning and evolving the business plans and their implementation

 

The MIS plays the role of information generation, communication, problem identification and helps in the process of

 

decision making The MIS, therefore, plays a vital role in the management, administration and operations of an

 

organization.

 

External Change

 

1. MIS has made world smaller

 

2. Worldwide reorganization environment and attempt to control the calamity 3. Health conscious among

 

the group leading less sufferings 3.Change in the work lifestyle for better result

 

4. Creating Knowledge is an asset

 

Internal Change

 

1. MIS will change the Business Process 2. MIS will change the old standards and set new standards

 

3. MIS key for Continuous improvement Process

 

4. MIS will reduce the hierarchy and hence less operation cost 5. MIS focus on "Shared information"

 

6.MIS will acceleraterestructure work flow for both line and staff functions.

 

7. MIS will bring change in Authority and power by merit and not by age or number of years of experience.

 

8. MIS brings cultural change.

 

9. MIS measures the results and performance.

 

10. MIS brings Continuous addition to Organizational knowledge base.

 

Characteristics of Information Technology

 

Following are the major features as well as advantages of Information Technology −

·        The development of Information Technology has made education system simpler, easier, and widespread. Now, people of remote areas can also use technology for their children’s education and also avail the benefits of adult education.

·        Diffusion of e-governance on a large scale.

·        Participation of public in governance and policy making.

·        Fast economic development.

·        Development of remote areas.

·        Technology helps the police in nabbing the criminals.

·        The judiciary and other administrative services can also take the help of technology to make work easier and faster.

·        Highly beneficial for the common people, as they can access their rights and can take legal action against the person who violates his/her rights.

·        It increases the happiness and prosperity of not only an individual, but rather the society as a whole.

Emerging Trends in Information Technology

1.    Cloud Computing

One of the most talked about concept in information technology is the cloud computing. Clouding computing is defined as utilization of computing services, i.e. software as well as hardware as a service over a network. Typically, this network is the internet.

Cloud computing offers 3 types of broad services mainly Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS).

Some of the benefit of cloud computing is as follows:

§  Cloud computing reduces IT infrastructure cost of the company.

§  Cloud computing promotes the concept of virtualization, which enables server and storage device to be utilized across organization.

§  Cloud computing makes maintenance of software and hardware easier as installation is not required on each end user’s computer.

Some issues concerning cloud computing are privacy, compliance, security, legal, abuse, IT governance, etc.

2.    Mobile Application

Another emerging trend within information technology is mobile applications (software application on Smart phone, tablet, etc.)

Mobile application or mobile app has become a success since its introduction. They are designed to run on Smartphone, tablets and other mobile devices. They are available as a download from various mobile operating systems like Apple, Blackberry, Nokia, etc. Some of the mobile app are available free where as some involve download cost. The revenue collected is shared between app distributor and app developer.

3.    User Interfaces

User interface has undergone a revolution since introduction of touch screen. The touch screen capability has revolutionized way end users interact with application. Touch screen enables the user to directly interact with what is displayed and also removes any intermediate hand-held device like the mouse.

Touch screen capability is utilized in smart phones, tablet, information kiosks and other information appliances.

4.    Analytics

The field of analytics has grown many folds in recent years. Analytics is a process which helps in discovering the informational patterns with data. The field of analytics is a combination of statistics, computer programming and operations research.

The field of analytics has shown growth in the field of data analytics, predictive analytics and social analytics.

Data analytics is tool used to support decision-making process. It converts raw data into meaningful information.

Predictive analytics is tool used to predict future events based on current and historical information.

Social media analytics is tool used by companies to understand and accommodate customer needs.

 

 

 

 IT Capabilities and their organizational impact

 

Information technology (IT) is dramatically changing the business landscape. Although organization cultures and business strategies shape the use of IT in organizations, more often the influence is stronger the other way round. IT significantly affects strategic options and creates opportunities and issues that managers need to address in many aspects of their business. This page outlines some of the key impacts of technology and the implications for management on:

• Business strategy - collapsing time and distance, enabling electronic commerce
• Organization Culture - encouraging the free flow of information
• Organization Structures - making networking and virtual corporations a reality
• Management Processes - providing support for complex decision making processes
• Work - dramatically changing the nature of professional, and now managerial work
• The workplace - allowing work from home and on the move, as in telework

 

 IT enabled services.

 

IT enabled Services (ITeS), also called web enabled services or remote services or Tele-working, covers the entire gamut of operations which exploit information technology for improving efficiency of an organization. These services provide a wide range of career options that include opportunities in call Centre, medical transcription, medical billing and coding, back office operations, revenue claims processing, legal databases, content development, payrolls, logistics management, GIS (Geographical Information System), HR services, web services etc.^[1]

Information Technology that enables the business by improving the quality of service is IT enabled services. The most important aspect is the Value addition of IT enabled service. The value addition could be in the form of - Customer relationship management, improved database, improved look and feel, etc. The outcome of an IT enabled service is in the two forms:

·         Direct Improved Service

·         Indirect Benefits.

 

 

 

 

Transaction Processing

System: Characteristics and its importance

A transaction process system (TPS) is an information processing system used in business transactions that involves the collection, retrieval and modification of the entire transaction data. The characteristics of a TPS include rapid processing, reliability, standardization and control access. TPS is also known as transaction processing or real-time processing.

Characteristics of transaction processing systems

 

Below is all the necessary information to fully understand the concepts required in focus point 1 -Transaction processing systems(TPS) collect, store, modify and retrieve the transactions -Transaction is an event that generates or modifies data to be stored in an information system -Examples: Point of Sale, credit card payments, -Designed in conjunction with the organisation's procedures -Main processes are collecting and storing   -ACID (Atomicity, Consistency, Isolation, Durability) is a set of properties of database transactions. In the context of databases, a single logical operation on the data is called a transaction.   The four important characteristics include ·         Rapid response -Fast performance is critical -Turnaround time from transaction input to the production output must be a few seconds or less   ·         Reliability -Breakdowns disturb operations -Failure rates must be low -If failure occurs, recovery must be quick and accurate ·         Inflexibility -Every transaction must be processed in the same way -Flexibility results in too many opportunities for non standard operations, resulting in problems due to different transaction data ·         Controlled processing -Must support an organisation's operations -If roles and responsibilities are allocated, the TPS should maintain these requirements   -TPS systems reduce costs by reducing number of times data must be handled -Two types, Batch and real time

 

Importance of the transaction processing system

1. The TPS keeps a stable database and reduces risk of loss of user information in the occurrence of terminal or network failure.
2. The TPS is able to effectively recover from operating system failure and also handle system failures depending on what stage the transaction was in when the system failure occurred.
3. The TPS can process large amount of data in real time or batches.
4. The use of TPS in organizations is a key feature in improving customer service and satisfaction.
5. A TPS allows for the user/customer to have a level of reliability and confidence during transactions.
6. TPS is swift and cost-effective.
7. The use of TPS in businesses minimizes the occurrence of error during data transactions.
8. TPS is available in both batch and real time process
9. The TPS is designed to be user friendly.
10. It is versatile as it encourages the use of online payment system in real time and increases more payment methods.
11. TPS can function anywhere. This means that location, geography, language, or methods are a barrier to using a transaction processing system.

 

 

 

 

 

 

 

 

 

 

*************************************************************************************

UNIT -II (6 Hours)

Information, Management and Decision Making - Attributes of information and its relevance to

Decision Making, Types of information. Models of Decision Making - Classical, Administrative and

Herbert Simon's Models. Management Support Systems: Decision Support Systems, Group Decision

Support Systems, and Executive Information Systems.

------------------------------------------------------------------------------------------------------------------------------------------

Attributes of information and its relevance to

Decision Making

 

There are five traits that you’ll find within information quality:

 Accuracy, completeness, reliability, relevance, and Accuracy

• Completeness
• Reliability
• Relevance
• Timeliness

 

Characteristic          How it’s measured

Accuracy                              Is the information correct in every detail?

Completeness                  How comprehensive is the information?

Reliability                           Does the information contradict other trusted resources?

Relevance                           Do you really need this information?

Timeliness                          How up- to-date is information? Can it be used for real-time reporting?

 

Accuracy

As the name implies, this data quality characteristic means that information is correct. To determine whether data is accurate or not, ask yourself if the information reflects a real-world situation. For example, in the realm of financial services, does a customer really have $1 million in his bank account?

Accuracy is a crucial data quality characteristic because inaccurate information can cause significant problems with severe consequences. We’ll use the example above – if there’s an error in a customer’s bank account, it could be because someone accessed it without his knowledge.

Completeness

“Completeness” refers to how comprehensive the information is. When looking at data completeness, think about whether all of the data you need is available; you might need a customer’s first and last name, but the middle initial may be optional.

Why does completeness matter as a data quality characteristic? If information is incomplete, it might be unusable. Let’s say you’re sending a mailing out. You need a customer’s last name to ensure the mail goes to the right address – without it, the data is incomplete.

Reliability

In the realm of data quality characteristics, reliability means that a piece of information doesn’t contradict another piece of information in a different source or system. We’ll use an example from the healthcare field; if a patient’s birthday is January 1, 1970 in one system, yet it’s June 13, 1973 in another, the information is unreliable. 

Reliability is a vital data quality characteristic. When pieces of information contradict themselves, you can’t trust the data. You could make a mistake that could cost your firm money and reputational damage. 

Relevance

When you’re looking at data quality characteristics, relevance comes into play because there has to be a good reason as to why you’re collecting this information in the first place. You must consider whether you really need this information, or whether you’re collecting it just for the sake of it.

Why does relevance matter as a data quality characteristic? If you’re gathering irrelevant information, you’re wasting time as well as money. Your analyses won’t be as valuable. 

Timeliness

Timeliness, as the name implies, refers to how up to date information is. If it was gathered in the past hour, then it’s timely – unless new information has come in that renders previous information useless. 

The timeliness of information is an important data quality characteristic, because information that isn’t timely can lead to people making the wrong decisions. In turn, that costs organizations time, money, and reputational damage.

“Timeliness is an important data quality characteristic – out-of-date information costs companies time and money”

In today’s business environment, data quality characteristics ensure that you get the most out of your information. When your information doesn’t meet these standards, it isn’t valuable. Precisely provides data quality solutions to improve the accuracy, completeness, reliability, relevance, and timeliness of your data.

Types of information

Information, as required at different levels of manage­ment can be classified as operational, tactical and strategic.

1. Operational information:

Operational information relates to the day-to-day operations of the organisation and thus, is useful in ex­ercising control over the operations that are repetitive in nature. Since such activities are controlled at lower levels of management, operational information is needed by the lower management.

For example, the information regarding the cash position on day-to-day basis is monitored and controlled at the lower levels of manage­ment. Similarly, in marketing function, daily and weekly sales in­formation is used by lower level manager to monitor the perform­ance of the sales force.

It may be noted that operational informa­tion pertains to activities that are easily measurable by specific standards. The operational information mainly relates to current and historical performance, and is based primarily on internal sources of data. The predictive element in operational information is quite low and if at all it is there, it has a short term horizon.

2. Tactical information:

Tactical information helps middle level man­agers allocating resources and establishing controls to implement the top level plans of the organisation. For example, information regarding the alternative sources of funds and their uses in the short run, opportunities for deployment of surplus funds in short- term securities, etc. may be required at the middle levels of man­agement.

The tactical information is generally predictive, focusing on short-term trends. It may be partly current and partly histori­cal, and may come from internal as well as external sources.

3. Strategic information:

While the operational information is needed to find out how the given activity can be performed better, strategic information is needed for making choices among the busi­ness options.

The strategic information helps in identifying and evaluating these options so that a manager makes informed choices which are different from the competitors and the limita­tions of what the rivals are doing or planning to do. Such choices are made by leaders only.

Strategic information is used by man­agers to define goals and priorities, initiate new programmes and develop policies for acquisition and use of corporate resources. For example, information regarding the long-term needs of funds for on-going and future projects of the company may be used by top level managers in taking decision regarding going public or approaching financial institutions for term loan.

Strategic infor­mation is predictive in nature, relies heavily on external sources of data, has a long-term perspective, and is mostly in summary form. It may sometimes include ‘what if’ scenarios. However, the strategic information is not only external information.

For long, it was believed that strategic information are basically information regarding the external environment. However, it is now well rec­ognised that the internal factors are equally responsible for suc­cess or failures of strategies and thus, internal information is also required for strategic decision making.

Figure 1.2 represents the types of information required at different levels of managerial hierarchy.

It may be remembered that each type of information has its role to play in managerial effectiveness. Each type of information is needed with varying degree by the managers at all levels. Thus, a part of operational information may be used even by the chief executive of­ficer of a company.

The difference lies in the proportion of each type of information in the total information needs of managers at different levels of managerial hierarchy.

 

 

 

 

 

Models of Decision Making - Classical, Administrative and

Herbert Simon's Models

The decision-making process though a logical one is a difficult task. All decisions can be categorized into the following three basic models.

 

(1) The Rational/Classical Model.

 

(2) The Administrative or Bounded Rationality Model.

(3) The Retrospective Decision-Making Model.

 

All models are beneficial for understanding the nature of decision-making processes in enterprises or organisations. All models are based on certain assumptions on which the decisions are taken.

 

1. The Rational/Classical Model:

The rational model is the first attempt to know the decision-making-process. It is considered by some as the classical approach to understand the decision-making process. The classical model gave various steps in decision-making process which have been discussed earlier.

 

Features of Classical Model:

 

1. Problems are clear.

 

2. Objectives are clear.

 

3. People agree on criteria and weights.

4. All alternatives are known.

5. All consequences can be anticipated.

6. Decision makes are rational.

 

i. They are not biased in recognizing problems.

ii. They are capable of processing ail relevant information

iii. They anticipate present and future consequences of decisions.

iv. They search for all alternatives that maximizes the desired results.

 

2. Bounded Rationality Model or Administrative Man Model:

Decision-making involve the achievement of a goal. Rationality demands that the decision-maker should properly understand the alternative courses of action for reaching the goals.

 

He should also have full information and the ability to analyse properly various alternative courses of action in the light of goals sought. There should also be a desire to select the best solutions by selecting the alternative which will satisfy the goal achievement.

 

Herbert A. Simon defines rationality in terms of objective and intelligent action. It is characterised by behavioural nexus between ends and means. If appropriate means are chosen to reach desired ends the decision is rational.

 

Bounded Rationality model is based on the concept developed by Herbert Simon. This model does not assume individual rationality in the decision process.

 

Instead, it assumes that people, while they may seek the best solution, normally settle for much less, because the decisions they confront typically demand greater information, time, processing capabilities than they possess. They settle for “bounded rationality or limited rationality in decisions. This model is based on certain basic concepts.

 

a. Sequential Attention to alternative solution:

 

Normally it is the tendency for people to examine possible solution one at a time instead of identifying all possible solutions and stop searching once an acceptable (though not necessarily the best) solution is found.

 

b. Heuristic:

These are the assumptions that guide the search for alternatives into areas that have a high probability for yielding success.

 

c. Satisficing:

Herbert Simon called this “satisficing” that is picking a course of action that is satisfactory or “good enough” under the circumstances. It is the tendency for decision makers to accept the first alternative that meets their minimally acceptable requirements rather than pushing them further for an alternative that produces the best results.

 

Satisficing is preferred for decisions of small significance when time is the major constraint or where most of the alternatives are essentially similar.

 

 

Thus, while the rational or classic model indicates how decisions should be made (i.e. it works as a prescriptive model), it falls somewhat short concerning how decisions are actually made (i.e. as a descriptive model).

 

3. Retrospective decision model (implicit favourite model):

This decision­-making model focuses on how decision-makers attempt to rationalise their choices after they have been made and try to justify their decisions. This model has been developed by Per Soelberg. He made an observation regarding the job choice processes of graduating business students and noted that, in many cases, the students identified implicit favorites (i.e. the alternative they wanted) very early in the recruiting and choice process. However, students continued their search for additional alternatives and quickly selected the best alternative.

 

The total process is designed to justify, through the guise of scientific rigor, a decision that has already been made intuitively. By this means, the individual becomes convinced that he or she is acting rationally and taking a logical, reasoned decision on an important topic.

 

Some Common Errors in Decision-Making:

Since the importance of the right decision cannot be overestimated enough for the quality of the decisions can make the difference between success and failure. Therefore, it is imperative that all factors affecting the decision be properly looked into and fully investigated.

 

In addition to technical and operational factors which can be quantified and analyzed, other factors such as personal values, personality traits, psychological assessment, perception of the environment, intuitional and judgmental capabilities and emotional interference must also be understood and credited.

 

Some researchers have pinpointed certain areas where managerial thinking needs to be re-assessed and where some common mistakes are made. These affect the decision-making process as well as the efficiency of the decision, and must be avoided

Herbert Simon Model on Decision Making

Herbert Simon, the Nobel Prize winning researcher, showed that humans went through three essential stages in the act of problem solving. He called these the Intelligence, Design, and Choice stages.

Decision making can also be considered as a type of problem solving. In the first stage, that of intelligence, they collect information about the issue from the environment and the surrounding context.

For example, if a person is faced with the problem of traveling from Bangalore to New Delhi, a distance of about 2000 km, then in the intelligence stage the person will seek all possible information of how to travel – by air, by train, by bus, or by a personal vehicle. This inquiry is open-ended and will involve searching for all possible avenues by which the problem can be solved.

The question addressed at this stageis as follows: What criteria should be used to decide between the alternative possible solutions to the problem? This question requires the decision maker to settle on the criteria that are important, and then select or rank-order them. For example, the choice of cost and time may be the most important criteria for the decision-making process.

At the next stage, that of choice, the criteria are applied to select the best answer from the available choices. For example, based on the criteria of cost and time available, it may be best to travel to Delhi from Bangalore by train. The criteria may be weighted and these weights are applied in a formal manner, often with the help of a mathematical model. Once a solution is available, the decision maker may be satisfied with the answer or may return to earlier stages to redo the process.

At the choice stage, the criteria and parameters for the decision help curtail the amount of search required to arrive at a decision. If the criteria are not specified sharply then the number of alternatives to be considered to arrive at a decision may be very large.

This stage may also require returning to the intelligence gathering activity, and then to the design stage to change or modify the criteria and the weights used to apply them. In his seminal work, Herbert
DSS are designed to support mainly the choice stage of the decision-making process.

Managers can enter the relevant data into the system, select or prioritise their criteria and let the system decide on the final solution.

Management Support Systems: Decision Support Systems, Group Decision Support Systems, and Executive Information Systems.

Decision support systems (DSS) are interactive software-based systems intended to help managers in decision-making by accessing large volumes of information generated from various related information systems involved in organizational business processes, such as office automation system, transaction processing system, etc.

DSS uses the summary information, exceptions, patterns, and trends using the analytical models. A decision support system helps in decision-making but does not necessarily give a decision itself. The decision makers compile useful information from raw data, documents, personal knowledge, and/or business models to identify and solve problems and make decisions.

Attributes of a DSS

• Adaptability and flexibility
• High level of Interactivity
• Ease of use
• Efficiency and effectiveness
• Complete control by decision-makers
• Ease of development
• Extendibility
• Support for modeling and analysis
• Support for data access
• Standalone, integrated, and Web-based

Characteristics of a DSS

·        Support for decision-makers in semi-structured and unstructured problems.

·        Support for managers at various managerial levels, ranging from top executive to line managers.

·        Support for individuals and groups. Less structured problems often requires the involvement of several individuals from different departments and organization level.

·        Support for interdependent or sequential decisions.

·        Support for intelligence, design, choice, and implementation.

·        Support for variety of decision processes and styles.

·        DSSs are adaptive over time.

Benefits of DSS

·        Improves efficiency and speed of decision-making activities.

·        Increases the control, competitiveness and capability of futuristic decision-making of the organization.

·        Facilitates interpersonal communication.

·        Encourages learning or training.

·        Since it is mostly used in non-programmed decisions, it reveals new approaches and sets up new evidences for an unusual decision.

·        Helps automate managerial processes.

Components of a DSS

Following are the components of the Decision Support System −

·        Database Management System (DBMS) − To solve a problem the necessary data may come from internal or external database. In an organization, internal data are generated by a system such as TPS and MIS. External data come from a variety of sources such as newspapers, online data services, databases (financial, marketing, human resources).

·        Model Management System − It stores and accesses models that managers use to make decisions. Such models are used for designing manufacturing facility, analyzing the financial health of an organization, forecasting demand of a product or service, etc.

Support Tools − Support tools like online help; pulls down menus, user interfaces, graphical analysis, error correction mechanism, facilitates the user interactions with the system.

Classification of DSS

There are several ways to classify DSS. Hoi Apple and Whinstone classifies DSS as follows −

·        Text Oriented DSS − It contains textually represented information that could have a bearing on decision. It allows documents to be electronically created, revised and viewed as needed.

·        Database Oriented DSS − Database plays a major role here; it contains organized and highly structured data.

·        Spreadsheet Oriented DSS − It contains information in spread sheets that allows create, view, modify procedural knowledge and also instructs the system to execute self-contained instructions. The most popular tool is Excel and Lotus 1-2-3.

·        Solver Oriented DSS − It is based on a solver, which is an algorithm or procedure written for performing certain calculations and particular program type.

·        Rules Oriented DSS − It follows certain procedures adopted as rules.

·        Rules Oriented DSS − Procedures are adopted in rules oriented DSS. Export system is the example.

·        Compound DSS − It is built by using two or more of the five structures explained above.

Types of DSS

Following are some typical DSSs −

·        Status Inquiry System − It helps in taking operational, management level, or middle level management decisions, for example daily schedules of jobs to machines or machines to operators.

·        Data Analysis System − It needs comparative analysis and makes use of formula or an algorithm, for example cash flow analysis, inventory analysis etc.

·        Information Analysis System − In this system data is analyzed and the information report is generated. For example, sales analysis, accounts receivable systems, market analysis etc.

·        Accounting System − It keeps track of accounting and finance related information, for example, final account, accounts receivables, accounts payables, etc. that keep track of the major aspects of the business.

·        Model Based System − Simulation models or optimization models used for decision-making are used infrequently and creates general guidelines for operation or management.

GROUP-DECISION-SUPPORT-SYSTEMS

The DSS we have just described focus primarily on individual decision making. However, so much work is accomplished in groups within firms that a special category of systems called group decision-support systems (GDSS) has been developed to support group and organizational decision making.

what is Group decision support system

A group decision-support system (GDSS) is an interactive computer-based system used to facilitate the solution of unstructured problems by a set of decision makers working together as a group (DeSanctis and Gallupe, 1987). Groupware and Web-based tools for videoconferencing and electronic meetings described earlier in this text can support some group decision processes, but their focus is primarily on communication. GDSS, however, provide tools and technologies geared explicitly toward group decision making and were developed in response to a growing concern over the quality and effectiveness of meetings. The underlying problems in group decision making have been the explosion of decision-maker meetings, the growing length of those meetings, and the increased number of attendees. Estimates on the amount of a manager’s time spent in meetings range from 35 to 70 percent.

components of GDSS

GDSS make meetings more productive by providing tools to facilitate planning, generating, organizing, and evaluating ideas; establishing priorities; and documenting meeting proceedings for others in the firm. GDSS consist of three basic elements: hardware, software tools, and people. Hardware refers to the conference facility itself, including the room, the tables, and the chairs. Such a facility must be physically laid out in a manner that supports group collaboration. It also must include some electronic hardware, such as electronic display boards, as well as audiovisual, computer, and networking equipment.

Electronic questionnaires aid the organizers in premeeting planning by identifying issues of concern and by helping to ensure that key planning information is not overlooked.

·         Electronic brainstorming tools enable individuals, simultaneously and anonymously, to contribute ideas on the topics of the meeting.

·         Idea organizers facilitate the organized integration and synthesis of ideas generated during brainstorming.

·         Questionnaire tools support the facilitators and group leaders as they gather information before and during the process of setting priorities.

·         Tools for voting or setting priorities make available a range of methods from simple voting, to ranking in order, to a range of weighted techniques for setting priorities or voting.

·         Stakeholder identification and analysis tools use structured approaches to evaluate the impact of an emerging proposal on the organization and to identify stakeholders and evaluate the potential impact of those stakeholders on the proposed project.

·         Policy formation tools provide structured support for developing agreement on the wording of policy statements.

·         Group dictionaries document group agreement on definitions of words and terms central to the project.

           People refers not only to the participants but also to a trained facilitator and often to a staff that supports the hardware and software. Together, these elements have led to the creation of a range of different kinds of GDSS, from simple electronic boardrooms to elaborate collaboration laboratories. In a collaboration laboratory, individuals work on their own desktop PCs or workstations. Their input is integrated on a file server and is viewable on a common screen at the front of the room; in most systems the integrated input is also viewable on the individual participant’s screen.

Executive information system (EIS)

 

 

An executive information system (EIS), also known as an executive support system (ESS), is a type of management support system that facilitates and supports senior executive information and decision-making needs. It provides easy access to internal and external information relevant to organizational goals. It is commonly considered a specialized form of decision support system 

EIS emphasizes graphical displays and easy-to-use user interfaces. They offer strong reporting and drill-down capabilities. In general, EIS are enterprise-wide DSS that help top-level executives analyze, compare, and highlight trends in important variables so that they can monitor performance and identify opportunities and problems. EIS and data warehousing technologies are converging in the marketplace.

Advantages of EIS

·         Easy for upper-level executives to use, extensive computer experience is not required in operations

·         Provides strong drill-down capabilities to better analyze the given information.

·         Information that is provided is better understood

·         EIS provides timely delivery of information. Management can make decisions promptly.

·         Improves tracking information

·         Offers efficiency to decision makers

Disadvantages of EIS

·         System dependent

·         Limited functionality, by design

·         Information overload for some managers

·         Benefits hard to quantify

·         High implementation costs

·         System may become slow, large, and hard to manage

·         Need good internal processes for data management

·         May lead to less reliable and less secure data

·         Excessive cost for small company

 

 

 

 

 

UNIT -III (8 Hours)

Managing Data Resources- The need for data management, Challenges of data management, Data

independence, Data redundancy, Data consistency, Data administration. Database Management

System – Concepts and types of DBMS, Fields, Records, Table, View, Reports and Queries. Data

warehouse and Data mining – Characteristics and uses of Data warehouse, Techniques of Data

Mining, Business Intelligence

Database Management System (Lab): Creation of Table, View and Reports. Basics of SQL and

running queries

*************************************************************************************

Managing Data Resources- The need for data management, Challenges of data management, Data

independence, Data redundancy, Data consistency, Data administration.

What is data management?

Data management is the practice of collecting, organizing, protecting, and storing an organization’s data so it can be analyzed for business decisions. As organizations create and consume data at unprecedented rates, data management solutions become essential for making sense of the vast quantities of data. Today’s leading data management software ensures that reliable, up-to-date data is always used to drive decisions. The software helps with everything from data preparation to cataloging, search, and governance, allowing people to quickly find the information they need for analysis.

Need of data management

Data management is a crucial first step to employing effective data analysis at scale, which leads to important insights that add value to your customers and improve your bottom line. With effective data management, people across an organization can find and access trusted data for their queries. Some benefits of an effective data management solution include:

Visibility

Data management can increase the visibility of your organization’s data assets, making it easier for people to quickly and confidently find the right data for their analysis. Data visibility allows your company to be more organized and productive, allowing employees to find the data they need to better do their jobs.

Reliability

Data management helps minimize potential errors by establishing processes and policies for usage and building trust in the data being used to make decisions across your organization. With reliable, up-to-date data, companies can respond more efficiently to market changes and customer needs.

Security

Data management protects your organization and its employees from data losses, thefts, and breaches with authentication and encryption tools. Strong data security ensures that vital company information is backed up and retrievable should the primary source become unavailable. Additionally, security becomes more and more important if your data contains any personally identifiable information that needs to be carefully managed to comply with consumer protection laws.

Scalability

Data management allows organizations to effectively scale data and usage occasions with repeatable processes to keep data and metadata up to date. When processes are easy to repeat, your organization can avoid the unnecessary costs of duplication, such as employees conducting the same research over and over again or re-running costly queries unnecessarily.

Challenges of data management

Because data management plays a crucial role in today’s digital economy, it’s important that systems continue to evolve to meet your organization’s data needs. Traditional data management processes make it difficult to scale capabilities without compromising governance or security. Modern data management software must address several challenges to ensure trusted data can be found.

Challenge 1: Increased data volumes

Every department within your organization has access to diverse types of data and specific needs to maximize its value. Traditional models require IT to prepare the data for each use case and then maintain the databases or files. As more data accumulates, it’s easy for an organization to become unaware of what data it has, where the data is, and how to use it.

Challenge 2: New roles for analytics

As your organization increasingly relies on data-driven decision-making, more of your people are asked to access and analyze data. When analytics falls outside a person’s skill set, understanding naming conventions, complex data structures, and databases can be a challenge. If it takes too much time or effort to convert the data, analysis won’t happen and the potential value of that data is diminished or lost.

Challenge 3: Compliance requirements

Constantly changing compliance requirements make it a challenge to ensure people are using the right data. An organization needs its people to quickly understand what data they should or should not be using—including how and what personally identifiable information (PII) is ingested, tracked, and monitored for compliance and privacy regulations.

Data independence

Data independence  is the type of data transparency that matters for a centralized DBMS. It refers to the immunity of user applications to changes made in the definition and organization of data. Application programs should not, ideally, be exposed to details of data representation and storage. The DBMS provides an abstract view of the data that hides such details.

There are two types of data independence: physical and logical data independence.

 

Data redundancy

Data redundancy refers to the practice of keeping data in two or more places within a database or data storage system. Data redundancy ensures an organization can provide continued operations or services in the event something happens to its data -- for example, in the case of data corruption or data loss. The concept applies to areas such as databases, computer memory and file storage systems.

Benefits and drawbacks of data redundancy

Data redundancy has benefits or risks depending on the implementation. Potential benefits include the following:

·         Helps protect data. When data cannot be accessed, redundant data can help replace or rebuild missing data.

·         Data accuracy. Hosting multiple locations for the same data means that a data management system can evaluate any differences, meaning data is assured to be accurate.

·         Access speed. Some locations for data may be easier to access than others for an organization that spans different physical areas. A person within an organization may access data from redundant sources to have faster access to the same data.

However, some possible downsides include the following:

·         Increase in database sizes. More storage space is needed for a redundant copy of a large amount of data. A larger database may also cause longer load times or create confusion if employees do not know where certain data is stored.

·         Cost. More need for storage also means an increased cost in addition to any extra overhead or resources needed to maintain and update redundant data.

·         Data discrepancies. Storing data in multiple locations can cause discrepancies such as missing records or incorrect values if the data is not continually updated.

·         Corruption. Storing multiple copies of the same data increases the chance of data corruption. Damaged data could result from errors in writing, reading, storage or processing of data.

Data redundancy vs. backup

Data redundancy and backups are both intended to prevent data loss, but the two technologies are slightly different. Data redundancy often takes the form of a synchronized copy of the organization's data. For example, an organization might create a redundant VM or storage volume.

Data redundancy can help to prevent service outages. For example, if a VM were to fail, a replica VM could quickly be brought online to minimize the service disruption.

Backups, on the other hand, are copies of data and other resources. Backups are specifically for creating copies of data in case an organization experiences an incident where data loss occurs, such as to buggy software, data corruption, hardware failure, malicious hacking, user error or other unforeseen events. Where redundancy is about making sure a business is able to provide continuity in services, backups are more about bringing a system back to a previous state.

But there is an overlap between redundancy and backups. Backups and some data redundancy products offer point-in-time recovery capabilities, but redundancy products generally have fewer recovery point options. Backups are also a good choice for granular recovery, which enables an organization to use a single backup operation to recover both files and images. In contrast, redundant systems are better suited to situations in which the organization needs to keep critical systems online and cannot tolerate a long recovery period.

Data consistency

Data consistency is the process of keeping information uniform as it moves across a network and between various applications on a computer. There are typically three types of data consistency: point in time consistency, transaction consistency, and application consistency. Ensuring that a computer network has all three elements of data consistency covered is the best way to ensure that data is not lost or corrupted as it travels throughout the system. In the absence of data consistency, there are no guarantees that any piece of information on the system is uniform across the breadth of the computer network.

Data administration

Data administration or data resource management is an organizational function working in the areas of information systems and computer science that plans, organizes, describes and controls data resources. Data resources are usually stored in databases under a database management system or other software such as electronic spreadsheets. In many smaller organizations, data administration is performed occasionally, or is a small component of the database administrator’s work.

 

In the context of information systems development, data administration ideally begins at system conception, ensuring there is a data dictionary to help maintain consistency, avoid redundancy, and model the database so as to make it logical and usable, by means of data modeling, including database normalization techniques.

 

Database Management

System – Concepts and types of DBMS, Fields, Records, Table, View, Reports and Queries. Data

warehouse and Data mining – Characteristics and uses of Data warehouse, Techniques of Data

Mining, Business Intelligence

 

 

 

DBMS concepts

A database intends to have a collection of data stored together to serve multiple applications as possible. Hence a database is often conceived of as a repository of information needed for running certain functions in a corporation or organization. Such a database would permit not only the retrieval of data but also the continuous modification of data needed for control of operations. It may be possible to search the database to obtain answers to queries or information for planning purposes.

A database management system stores data in such a way that it becomes easier to retrieve, manipulate, and produce information. Following are the important characteristics and applications of DBMS.

·        ACID Properties − DBMS follows the concepts of Atomicity, Consistency, Isolation, and Durability (normally shortened as ACID). These concepts are applied on transactions, which manipulate data in a database. ACID properties help the database stay healthy in multi-transactional environments and in case of failure.

·        Multiuser and Concurrent Access − DBMS supports multi-user environment and allows them to access and manipulate data in parallel. Though there are restrictions on transactions when users attempt to handle the same data item, but users are always unaware of them.

·        Multiple views − DBMS offers multiple views for different users. A user who is in the Sales department will have a different view of database than a person working in the Production department. This feature enables the users to have a concentrate view of the database according to their requirements.

·        Security − Features like multiple views offer security to some extent where users are unable to access data of other users and departments. DBMS offers methods to impose constraints while entering data into the database and retrieving the same at a later stage. DBMS offers many different levels of security features, which enables multiple users to have different views with different features. For example, a user in the Sales department cannot see the data that belongs to the Purchase department. Additionally, it can also be managed how much data of the Sales department should be displayed to the user. Since a DBMS is not saved on the disk as traditional file systems, it is very hard for miscreants to break the code.

 

Types of DBMS

Types of DBMS

The types of DBMS based on data model are as follows −

• Relational database.
• Object oriented database.
• Hierarchical database.
• Network database.

Relation Database

A relational database management system (RDBMS) is a system where data is organized in two-dimensional tables using rows and columns.

This is one of the most popular data models which is used in industries. It is based on SQL.

Every table in a database has a key field which uniquely identifies each record.

This type of system is the most widely used DBMS.

Relational database management system software is available for personal computers, workstation and large mainframe systems.

For example − Oracle Database, MySQL, Microsoft SQL Server etc.

Std ID	Name	City
201	Bob	Hyderabad
204	Lucky	Chennai
205	Pinky	Bangalore

In the above student table Std ID, Name and city are called as attributes and their values. Std ID is a primary key attribute which uniquely identifies each record in the student table.

Object Oriented Database

It is a system where information or data is represented in the form of objects which is used in object-oriented programming.

• It is a combination of relational database concepts and object-oriented principles.
• Relational database concepts are concurrency control, transactions, etc.
• OOPs principles are data encapsulation, inheritance, and polymorphism.
• It requires less code and is easy to maintain.

For example − Object DB software.

The object oriented database is represented in diagram format below −

 

Hierarchical Database

It is a system where the data elements have a one to many relationship (1: N). Here data is organized like a tree which is similar to a folder structure in your computer system.

• The hierarchy starts from the root node, connecting all the child nodes to the parent node.
• It is used in industry on mainframe platforms.

For example− IMS(IBM), Windows registry (Microsoft).

An example of a hierarchical database is given below −

Network database

A Network database management system is a system where the data elements maintain one to one relationship (1: 1) or many to many relationship (N: N).

It also has a hierarchical structure, but the data is organized like a graph and it is allowed to have more than one parent for one child record.

Example

Teachers can teach in multiple departments. This is shown below –

Fields, Records, Table

A relational database like Access usually has several related tables. In a well-designed database, each table stores data about a particular subject, such as employees or products. A table has records (rows) and fields (columns). Fields have different types of data, such as text, numbers, dates, and hyperlinks.

1.      A record: Contains specific data, like information about a particular employee or a product.

2.      A field: Contains data about one aspect of the table subject, such as first name or e-mail address.

3.      A field value: Each record has a field value. For example, Contoso, Ltd. or someone@example.com.

View, Reports and Queries

Views in SQL are considered as a virtual table. A view also contains rows and columns. To create the view, we can select the fields from one or more tables present in the database. A view can either have specific rows based on certain condition or all the rows of a table.

A database report is the formatted result of database queries and contains useful data for decision-making and analysis. Most good business applications contain a built-in reporting tool; this is simply a front-end interface that calls or runs back-end database queries that are formatted for easy application usage.

A database query is either an action query or a select query. A select query is one that retrieves data from a database. An action query asks for additional operations on data, such as insertion, updating, deleting or other forms of data manipulation.

Data warehouse and Data mining

Data Warehouse:

A Data Warehouse refers to a place where data can be stored for useful mining. It is like a quick computer system with exceptionally huge data storage capacity. Data from the various organization's systems are copied to the Warehouse, where it can be fetched and conformed to delete errors. Here, advanced requests can be made against the warehouse storage of data.

Data warehouse combines data from numerous sources which ensure the data quality, accuracy, and consistency. Data warehouse boosts system execution by separating analytics processing from transnational databases. Data flows into a data warehouse from different databases. A data warehouse works by sorting out data into a pattern that depicts the format and types of data. Query tools examine the data tables using patterns.

Data warehouses and databases both are relative data systems, but both are made to serve different purposes. A data warehouse is built to store a huge amount of historical data and empowers fast requests over all the data, typically using Online Analytical Processing (OLAP). A database is made to store current transactions and allow quick access to specific transactions for ongoing business processes, commonly known as Online Transaction Processing (OLTP).

Data Mining:

Data mining refers to the analysis of data. It is the computer-supported process of analyzing huge sets of data that have either been compiled by computer systems or have been downloaded into the computer. In the data mining process, the computer analyzes the data and extract useful information from it. It looks for hidden patterns within the data set and try to predict future behavior. Data mining is primarily used to discover and indicate relationships among the data sets.

Data mining aims to enable business organizations to view business behaviors, trends relationships that allow the business to make data-driven decisions. It is also known as knowledge Discover in Database (KDD). Data mining tools utilize AI, statistics, databases, and machine learning systems to discover the relationship between the data. Data mining tools can support business-related questions that traditionally time-consuming to resolve any issue.

Data Mining	Data Warehousing
Data mining is the process of determining data patterns.	A data warehouse is a database system designed for analytics.
Data mining is generally considered as the process of extracting useful data from a large set of data.	Data warehousing is the process of combining all the relevant data.
Business entrepreneurs carry data mining with the help of engineers.	Data warehousing is entirely carried out by the engineers.
In data mining, data is analyzed repeatedly.	In data warehousing, data is stored periodically.
Data mining uses pattern recognition techniques to identify patterns.	Data warehousing is the process of extracting and storing data that allow easier reporting.
One of the most amazing data mining technique is the detection and identification of the unwanted errors that occur in the system.	One of the advantages of the data warehouse is its ability to update frequently. That is the reason why it is ideal for business entrepreneurs who want up to date with the latest stuff.
The data mining techniques are cost-efficient as compared to other statistical data applications.	The responsibility of the data warehouse is to simplify every type of business data.
The data mining techniques are not 100 percent accurate. It may lead to serious consequences in a certain condition.	In the data warehouse, there is a high possibility that the data required for analysis by the company may not be integrated into the warehouse. It can simply lead to loss of data.
Companies can benefit from this analytical tool by equipping suitable and accessible knowledge-based data.	Data warehouse stores a huge amount of historical data that helps users to analyze different periods and trends to make future predictions.

Data warehouse refers to the process of compiling and organizing data into one common database, whereas data mining refers to the process of extracting useful data from the databases. The data mining process depends on the data compiled in the data warehousing phase to recognize meaningful patterns. A data warehousing is created to support management systems.

Characteristics and uses of Data warehouse

The Important features of Data Warehouse are given below:

1. Subject Oriented

A data warehouse is subject-oriented. It provides useful data about a subject instead of the company's ongoing operations, and these subjects can be customers, suppliers, marketing, product, promotion, etc. A data warehouse usually focuses on modeling and analysis of data that helps the business organization to make data-driven decisions.

2. Time-Variant:

The different data present in the data warehouse provides information for a specific period.

3. Integrated

A data warehouse is built by joining data from heterogeneous sources, such as social databases, level documents, etc.

4. Non- Volatile

It means, once data entered into the warehouse cannot be change.

Advantages of Data Warehouse:

• More accurate data access
• Improved productivity and performance
• Cost-efficient
• Consistent and quality data

 

Techniques of Data Mining

 

10 Data Mining Techniques

1. Clustering

Clustering is a technique used to represent data visually — such as in graphs that show buying trends or sales demographics for a particular product.

What Is Clustering in Data Mining?

Clustering refers to the process of grouping a series of different data points based on their characteristics. By doing so, data miners can seamlessly divide the data into subsets, allowing for more informed decisions in terms of broad demographics (such as consumers or users) and their respective behaviors. 

Methods for Data Clustering

§ Partitioning method: This involves dividing a data set into a group of specific clusters  for evaluation based on the criteria of each individual cluster. In this method, data points belong to just one group or cluster.

§ Hierarchical method: With the hierarchical method, data points are a single cluster, which are grouped based on similarities. These newly created clusters can then be analyzed separately from each other. 

§ Density-based method: A machine learning method where data points plotted together are further analyzed, but data points by themselves are labeled “noise” and discarded.

§ Grid-based method: This involves dividing data into cells on a grid, which then can be clustered by individual cells rather than by the entire database. As a result, grid-based clustering hase a fast processing time.

§ Model-based method: In this method, models are created for each data cluster to locate the best data to fit that particular model.

2. Association

Association rules are used to find correlations, or associations, between points in a data set. 

What Is Association in Data Mining?

Data miners use association to discover unique or interesting relationships between variables in databases. Association is often employed to help companies determine marketing research and strategy.

Methods for Data Mining Association

Two primary approaches using association in data mining are the single-dimensional and multi-dimensional methods.

§ Single-dimensional association: This involves looking for one repeating instance of a data point or attribute. For instance, a retailer might search its database for the instances a particular product was purchased. 

§ Multi-dimensional association: This involves looking for more than one data point in a data set. That same retailer might want to know more information than what a customer purchased — such as their age, method of purchase (cash or credit card), or age.

3. Data Cleaning

Data cleaning is the process of preparing data to be mined.

What Is Data Cleaning in Data Mining?

Data cleaning involves organizing data, eliminating duplicate or corrupted data, and filling in any null values. When this process is complete, the most useful information can be harvested for analysis.

Methods for Data Cleaning

§ Verifying the data: This involves checking that each data point in the data set is in the proper format (e.g, telephone numbers, social security numbers). 

§ Converting data types: This ensures data is uniform across the data set. For instance, numeric variables only contain numbers, while string variables can contain letters, numbers, and characters. 

§ Removing irrelevant data: This clears useless or inapplicable data so full emphasis can be placed on necessary data points. 

§ Eliminating duplicate data points: This helps speed up the mining process by boosting efficiency and reducing errors.

§ Removing errors: This eliminates typing mistakes, spelling errors, and input errors that could negatively affect analysis outcomes. 

§ Completing missing values: This provides an estimated value for all data and reduces missing values, which can lead to skewed or incorrect results.

4. Data Visualization

Data visualization is the translation of data into graphic form to illustrate its meaning to business stakeholders. 

What Is Data Visualization in Data Mining?

Data can be presented in visual ways through charts, graphs, maps, diagrams, and more. This is a primary way in which data scientists display their findings. 

Methods for Data Visualization

Many methods exist for representing data visually. Here are a few:

§ Comparison charts: Charts and tables express relationships in the data, such as monthly product sales over a one-year period.

§ Maps: Data maps are used to visualize data pertaining to specific geographic locations. Through maps, data can be used to show population density and changes; compare populations of neighboring states, counties, and countries; detect how populations are spread over geographic regions; and compare characteristics in one region to those in other regions. 

§ Heat maps: This is a popular visualization technique that represents data through different colors and shading to indicate patterns and ranges in the data. It can be used to track everything from a region’s temperature changes to its food and pop culture trends. 

§ Density plots: These visualizations track data over a period of time, creating what can look like a mountain range. Density plots make it easy to represent occurrences of single events over time (e.g., month, year, decade). 

§ Histograms: These are similar to density plots but are represented by bars on a graph instead of a linear form.

§ Network diagrams: These diagrams show how data points relate to each other by using a series of lines (or links) to connect objects together.

§ Scatter plots: These graphs represent data point relationships on a two-variable axis. Scatter plots can be used to compare unique variables such as a country’s life expectancy or the amount of money spent on healthcare annually.

§ Word clouds: These graphics are used to highlight specific word or phrase instances appearing in a body of text; the larger the word’s size in the cloud, the more frequent its use.

5. Classification

Classification is a fundamental technique in data mining and can be applied to nearly every industry. It is a process in which data points from large data sets are assigned to categories based on how they’re being used.

What Is Classification in Data Mining?

In data mining, classification is considered to be a form of clustering — that is, it is useful for extracting comparable points of data for comparative analysis. Classification is also used to designate broad groups within a demographic, target audience, or user base through which businesses can gain stronger insights. 

Methods for Data Mining Classification

§ Logistic regression: This algorithm attempts to show the probability of a specific outcome within two possible results. For example, an email service can use logistic regression to predict whether or not an email is spam.

§ Decision trees: Once data is classified, follow-up questions can be asked, and the results diagrammed into a chart called a decision tree. For example, if a computer company wants to predict the likelihood of laptop purchases, it may ask, Is the potential buyer a student? The data is classified into “Yes” and “No” decision trees, with other questions to be asked afterward in a similar fashion. 

§ K-nearest neighbors (KNN): This is an algorithm that tries to identify an unknown object by comparing it to others. For instance, grocery chains might use the K-nearest neighbors algorithm to decide whether to include a sushi or hot meals station in their new store layout based on consumer habits in the local marketplace.

§ Naive Bayes: Based on the Bayes Theorem of Probability, this algorithm uses historical data to predict whether similar events will occur based on a different set of data.

§ Support Vector Machine (SVM): This machine learning algorithm is often used to define the line that best divides a data set into two classes. An SVM can help classify images and is used in facial and handwriting recognition software.

6. Machine Learning

Machine learning is the process by which computers use algorithms to learn on their own. An increasingly relevant part of modern technology, machine learning makes computers “smarter” by teaching them how to perform tasks based on the data they have gathered.  

What Is Machine Learning in Data Mining?

In data mining, machine learning’s applications are vast. Machine learning and data mining fall under the umbrella of data science but aren’t interchangeable terms. For instance, computers perform data mining as part of their machine learning functions.

Methods for Machine Learning

§ Supervised learning: In this method, algorithms train machines to learn using pre-labeled data with correct values, which the machines then classify on their own. It’s called supervised because the process trains (or “supervises”) computers to classify data and predict outcomes. Supervised machine learning is used in data mining classification.

§ Unsupervised learning: When computers handle unlabeled data, they engage in unsupervised learning. In this case, the computer classifies the data itself and then looks for patterns on its own. Unsupervised models are used to perform clustering and association.

§ Semi-supervised learning: Semi-supervised learning uses a combination of labeled and unlabeled data, making it a hybrid of the above models. 

§ Reinforcement learning: This is a more layered process in which computers learn to make decisions based on examining data in a specific environment. For example, a computer might learn to play chess by examining data from thousands of games played online.

Top of Form

Bottom of Form

7. Neural Networks

Computers process large amounts of data much faster than human brains but don’t yet have the capacity to apply common sense and imagination in working with the data. Neural networks are one way to help computers reason more like humans.

What Are Neural Networks in Data Mining?

Artificial neural networks attempt to digitally mimic the way the human brain operates. Neural networks combine many computer processors (similar to the way the brain uses neurons) to process data, make decisions, and learn as a human would — or at least as closely as possible.

Neural Network Methods

Neural networks consist of three main layers: input, “hidden,” and output. Data enters through the input layer, is processed in the hidden layer, and is resolved in the output layer where any relevant action based on the data is then taken. The hidden layer can consist of many processing layers, depending on the amount of data being used and learning taking place.

Supervised and unsupervised learning also apply to neural networks; neural networks use these types of algorithms to “train” themselves to function in ways similar to the human brain.

Examples of Neural Networks in Business

Neural networks have a wide range of applications. They can help businesses predict consumer buying patterns and focus marketing campaigns on specific demographics. They can also help retailers make accurate sales forecasts and understand how to use dynamic pricing. Furthermore, they help to improve diagnostic and treatment methods in healthcare, improving care and performance. 

8. Outlier Detection

Outlier detection is a key component of maintaining safe databases. Companies use it to test for fraudulent transactions, such as abnormal credit card usage that might suggest theft. 

What Is Outlier Detection in Data Mining?

While other data mining methods seek to identify patterns and trends, outlier detection looks for the unique: the data point or points that differ from the rest or diverge from the overall sample. Outlier detection finds errors, such as data that was input incorrectly or extracted from the wrong sample. Natural data deviations can be instructive as well.

Methods for Outlier Detection

§ Numeric outlier: Outliers are detected based on the Interquartile Range, or the middle 50 percent of values. Data points outside that range are considered outliers. 

§ Z-score: The Z-Score denotes how many standard deviations a data point is from the sample’s mean. This is also known as extreme value analysis.

§ DBSCAN: This stands for “density-based spatial clustering of applications with noise” and is a method that defines data as core points, border points, and noise points, which are the outliers.

§ Isolation forest: This method isolates anomalies in large sets of data (the forest) with an algorithm that searches for those anomalies instead of profiling normal data points.

Examples of Outlier Detection in Business

Almost every business can benefit from understanding anomalies in their production or distribution lines and how to fix them. Retailers can use outlier detection to learn why their stores witness an odd increase in purchases, such as snow shovels being bought in the summer, and how to respond to such findings. 

Generally, outlier detection is employed to enhance logistics, instill a culture of preemptive damage control, and create a smoother environment for customers, users, and other key groups. 

9. Prediction

Predictive modeling seeks to turn data into a projection of future action or behavior. These models examine data sets to find patterns and trends, then calculate the probabilities of a future outcome.

What Is Prediction in Data Mining?

Predictive modeling is among the most common uses of data mining and works best with large data sets that represent a broad sample size.

Methods for Prediction

Predictive modeling uses some of the same techniques and terminology as other data mining processes. Here are four examples:

 

§ Forecast modeling: This is a common technique in which the computer answers a question (for instance, How much milk should a store have in stock on Monday?) by analyzing historical data.

§ Classification modeling: Classification places data into groups where it can be used to answer direct questions. 

§ Cluster modeling: By clustering data into groups with shared characteristics, a predictive model can be used to study those data sets and make decisions.

§ Time series modeling: This model analyzes data based on when the data was input. A study of sales trends over a year is an example of time series modeling.

Examples of Prediction in Business

Predictive modeling is a business imperative that impacts nearly every corner of the public and private sectors. According to MicroStrategy, 52 percent of global businesses consider advanced and predictive modeling their top priority in analytics.

 

Predictive models can be built to determine sales projections and predict consumer buying habits. They help manufacturers forecast distribution needs and determine maintenance schedules. Government agencies use census data to map population trends and project spending needs while baseball teams use predictive models to determine contracts and build rosters.

10. Data Warehousing

Data warehousing is the process by which data is collected and stored before it is evaluated. 

What Is Data Warehousing in Data Mining?

Data miners collect data from multiple sources into a common archive before it can be used in business analysis. This process, called data warehousing, typically occurs before the data mining process.

Methods for Data Warehousing

Data goes through a three-stage process known as ETL before being loaded into a data warehouse. ETL stands for extract, transform, and load:

§ Extract: Data is copied and moved from its source to a warehouse staging area. Data can be structured (names, dates, credit card numbers, etc.) or unstructured (photos, videos, audio files, social media posts).

§ Transform: In this step, the data is filtered and cleaned — errors are removed and the data is validated. The data is also formatted to fit the warehouse.

§ Load: In the final step, the transformed data is uploaded to the data warehouse. These steps can be repeated as data is updated.

 

 

 

 

Business intelligence

 

Business intelligence (BI) combines business analytics, data mining, data visualization, data tools and infrastructure, and best practices to help organizations to make more data-driven decisions. In practice, you know you’ve got modern business intelligence when you have a comprehensive view of your organization’s data and use that data to drive change, eliminate inefficiencies, and quickly adapt to market or supply changes.

It’s important to note that this is a very modern definition of BI—and BI has had a strangled history as a buzzword. Traditional Business Intelligence, capital letters and all, originally emerged in the 1960s as a system of sharing information across organizations. It further developed in the 1980s alongside computer models for decision-making and turning data into insights before becoming specific offering from BI teams with IT-reliant service solutions. Modern BI solutions prioritize flexible self-service analysis, governed data on trusted platforms, empowered business users, and speed to insight. This article will serve as an introduction to BI and is the tip of the iceberg.

Much more than a specific “thing,” business intelligence is rather an umbrella term that covers the processes and methods of collecting, storing, and analyzing data from business operations or activities to optimize performance. All of these things come together to create a comprehensive view of a business to help people make better, actionable decisions. Over the past few years, business intelligence has evolved to include more processes and activities to help improve performance. These processes include:

• Data mining: Using databases, statistics and machine learning to uncover trends in large datasets.
• Reporting: Sharing data analysis to stakeholders so they can draw conclusions and make decisions.
• Performance metrics and benchmarking: Comparing current performance data to historical data to track performance against goals, typically using customized dashboards.
• Descriptive analytics: Using preliminary data analysis to find out what happened.
• Querying: Asking the data specific questions, BI pulling the answers from the datasets.
• Statistical analysis: Taking the results from descriptive analytics and further exploring the data using statistics such as how this trend happened and why.
• Data visualization: Turning data analysis into visual representations such as charts, graphs, and histograms to more easily consume data.
• Visual analysis: Exploring data through visual storytelling to communicate insights on the fly and stay in the flow of analysis.
• Data preparation: Compiling multiple data sources, identifying the dimensions and measurements, preparing it for data analysis.

Why is business intelligence important?

 

Great BI helps businesses and organizations ask and answer questions of their data.

Business intelligence can help companies make better decisions by showing present and historical data within their business context. Analysts can leverage BI to provide performance and competitor benchmarks to make the organization run smoother and more efficiently. Analysts can also more easily spot market trends to increase sales or revenue. Used effectively, the right data can help with anything from compliance to hiring efforts. A few ways that business intelligence can help companies make smarter, data-driven decisions:

• Identify ways to increase profit
• Analyze customer behavior
• Compare data with competitors
• Track performance
• Optimize operations
• Predict success
• Spot market trends
• Discover issues or problems