Management for All

Friday, September 25, 2015

DIFFERENCE BETWEEN DSS, MIS & EIS

With the exposure to all the above information systems, let us find out the differences between DSS and MIS. Table-1 enlists some basic differences between Decision Support System, Management Information Systems and Executive Information System. As the name implies, the later two are the systems that provide information that may or may not be used for making a decision whereas the support information provided for deciding on the policy, planning or implementation is the basic component of DSS.

Let us find out the characteristics of the three systems :

DSS (DECISION SUPPORT SYSTEM) :

- DSS generally provide support for unstructured, or semi-structured decisions (decisions that cannot be described in detail).

- DSS problems are often characterized by incomplete or uncertain knowledge, or the use of qualitative data.

- DSS will often include modelling tools in them, where various alternative scenarios can be modeled and compared.

- Investment decisions are an examples of those that might be supported by DSS

MIS (MANAGEMENT INFORMATION SYSTEMS) :

- MIS is generally more sophisticated reporting systems built on existing transaction processing systems

- Often used to support structured decision making (decisions that can be described in detail before the decision is made)

- Typically will also support tactical level management, but sometimes are used at other levels

- Examples of structured decisions supported by MIS might include deciding on stock levels or the pricing of products.

Wednesday, September 16, 2015

GROUP DSS

Having basic understanding of decision-making process and DSS, let us find out what is Group Decision Support Systems (GDSS). GDSS are interactive computer-based systems that facilitate decision-makers working together as a group to arrive at a solution for unstructured problem. The group of executives analyzes problem situations and performs group decision-making tasks. The GDSS provides mechanisms to help the users to coordinate and keep track of on-going projects, allow them to work together thru computer-supported communication, collaboration, and coordination. Typical applications of GDSS include email, awareness and notification systems, videoconferencing, chat systems, multi-player games, and negotiation systems.

The group decision support system addresses the vary issue of human behaviour in a given environment along with computer science and management. It is found that a task assigned to a group is a typical information processing system that usually provides a judicious solution with alternatives. The GDSS has several implications that can be listed as follows :

- Enable all participants to work simultaneously thereby promoting broader input into the meeting process and reducing dominance of few people;

- Provide equal opportunity for participation;

- Enable larger group meetings that can effectively bring more information, knowledge, and skills for a given task;

- Provide process structure to help focus the group on key issues and discourages irrelevant digressions and non-productive behaviors;

- Support the development of an organizational memory from meeting to meeting; and

- Individual satisfaction increases with group size.

DECISION SUPPORT SYSTEM (DSS)

Concept of DSS : A decision basically is a resources allocation process that is irreversible, except that a fresh decision may reverse it or overrule the earlier one. We can also define it as a reasoned choice among alternatives. The decision maker, having authority over the resources being allocated, makes a decision. He makes the decision in order to further some objective, which is what he hopes to achieve by allocating the resources. The decision might not succeed in achieving the objective. One might spend the funds and yet, for any number of reasons, achieve no acceleration at all. For example: To accelerate an R&D program is an objective, not a decision. To allocate the funds in an effort to accelerate the program is a decision.

Simple decision is one in which there is only one decision to be made, even having many alternatives.

A decision may be goal oriented for some degree of satisfaction for a given objective. Objective may be driven by a decision but goal is always target/result oriented. A decision may employ decision analysis; a structured thought process to attain desired results. In doing this, we can distinguish three features of the situation: alternatives, uncertainties and outcomes. Decision analysis thus constructs models, logical or even mathematical, representing the relationships within and between the features of situation. The models then allow the decision maker to estimate the possible implications of each course of action that he might take, so that he can better understand the relationship between his actions and his objectives. Someone who buys a lottery ticket and wins the lottery obtains a good outcome. Yet, the decision to buy the lottery ticket may or may not have been a good decision.

Monday, September 14, 2015

BUILDING DSS / ESS IN AN ORGANISATION

To build a DSS or EIS in an organization, it is important to understand the organisational environment in which it has to be functional. The environment here can be explained as the available hardware, operating system on the computers, approach to link or network computers, users, their work and workload, the links between the departments and information or data flow, hierarchies among the different levels of human resources, their interactivity level, etc. This total setup is covered under Information System Architecture.

The architecture of an information system refers to the way its pieces are laid out, types of tasks assigned to each piece, interaction among pieces and interaction of pieces with outside system. Martin (1991) defines information system architecture as “A written expression of the desired future for information use and management in an organization, that creates the context within which people can make consistent decisions”.

Let us look at the flow diagram (Figure-1) of course development process adopted by Indira Gandhi National Open University for generating a course as an example of information system architecture.

Sunday, November 2, 2014

TECHNOLOGY LIFE CYCLE

TECHNOLOGICAL CHANGE

Technological growth is the result of new inventions and innovations. Every invention is something new and in most cases it is a combination of already existing technological elements. An invention becomes innovation when applied for the first time. An innovation which has little disruptive impact on behaviour pattern is called a continuous innovation (e.g. fluoride tooth paste). In such cases alteration of an existing product rather than creation of a new product is involved. There are also dynamically continuous innovations which do not involve new consumption patterns but involve the creation of a new product or the alteration of the existing one (e.g. electrical tooth brush). Further, there are discontinuous innovations, which involve the establishment of new behaviour patterns and the creation of previously unknown products such as automobiles, televisions, computers etc.

The process of technological change is clearly linked to innovation. Technological change occurs through substitution and diffusion. The simplest form of technological substitution occurs when a new technology captures over a period of time a substantial share of the market from an existing older technology. The new technology is better and economically more viable. Thus after it has gained small market share, it is likely to become more 'competitive with time. Therefore, once a substitution has begun, it is highly profitable to eventually take over the available market. This is a simple one-to-one technological substitution process.

There is a broad spectrum of factors, which can have an impact on the process of substitution and diffusion. These can be broadly classified into (a) factors affecting the demand for a technology; and (b) factors affecting the supply of a technology.

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TECHNOLOGY LIFE CYCLE

The life span of various technologies can be conveniently identified as consisting of four distinct stages, all of which taken together form the ’Technology Life Cycle'. The stages of technology life cycle are innovation, syndication, diffusion, and substitution.

Wednesday, July 23, 2014

DATA MINING PROJECT

Step 1: Define Business Objectives- This step is similar to any information system project. First of all, determine whether a data mining solution is really needed. State the objectives. Are we looking to improve our direct marketing campaigns? Do we want to detect fraud in credit card usage? Are we looking for associations between products that sell together? In this step, define expectations. Express how the final results will be presented and used.

Step 2: Prepare Data- This step consists of data selection, preprocessing of data, and data transformation. Select the data to be extracted from the data warehouse. Use the business objectives to determine what data has to be selected. Include appropriate metadata about the selected data. Select the appropriate data mining technique(s) and algorithm(s). The mining algorithm has a bearing on data selection.

Unless the data is extracted from the data warehouse, when it is assumed that the data is already cleansed, pre-processing may be required to cleanse the data. Preprocessing could also involve enriching the selected data with external data. In the preprocessing sub-step, remove noisy data, that is, data blatantly out of range. Also ensure that there are no missing values.

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DATA MINING

According to Berry and Linoff, Data Mining is the exploration and analysis, by automatic or semiautomatic means, of large quantities of data in order to discover meaningful patterns and rules. This definition, justifiably, raises the question: how does data mining differ from OLAP? OLAP (Online Analytical Processing) is undoubtedly a semiautomatic means of analyzing data, but the main difference lies in quantities of data that can be handled.

There are other differences as well. Tables 1 and 2 summarize these differences.

Table-1 : OLAP Vs Data Mining – Past Vs Future

OLAP: Report on the past	Data Mining: Predict the future
Who are our top 100 best customers for the last three years?	Which 100 customers offer the best profit potential?
Which customers defaulted on the mortgages last in two years?	Which customers are likely to be bad credit risks?
What were the sales by territory last quarter compared to the targets?	What are the anticipated sales by territory and region for next year?
Which salespersons sold more than their quota during last four quarters?	Which salespersons are expected to exceed their quotas next year?
Last year, which stores exceeded the total prior year sales?	For the next two years, which stores are likely to have best performance?
Last year, which were the top five promotions that performed well?	What is the expected return for next year’s promotions?
Which customers switched to other phone companies last year?	Which customers are likely to switch to the competition next year?

Saturday, July 19, 2014

DATA WAREHOUSE AND THE WEB

Professor Peter Drucker, the senior guru of management practice, has admonished IT executives to look outside their enterprises for information. He remarked that the single biggest challenge is to organize outside data because change occurs from the outside. He predicted that the obsession with internal data would lead to being blindsided by external forces.

The majority of data warehousing efforts result in an enterprise focusing inward; however, the enterprise should be keenly alert to its externalities. As markets become turbulent, an enterprise must know more about its customers, suppliers, competitors, government agencies, and many other external factors. The changes that take place in the external environment, ultimately, get reflected in the internal data (and would be detected by the various data analysis tools discussed in the later sections), but by then it may be too late for the enterprise. Proactive action is always better than reacting to external changes after the effects are felt. The conclusion is that the information from internal systems must be enhanced with external information. The synergism of the combination creates the greatest business benefits.

The importance of external data and the challenges faced in integrating external data with internally sourced data by Load Manager. Some externally sourced data (particularly time sensitive data), is often distributed through the internet.

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Reliability of Web Content

Many question the reliability of web content, as they should. However, few analyze the reliability issue to any depth. The Web is a global bulletin board on which both the wise and foolish have equal space. Acquiring content from the Web should not reflect positively or negatively on its quality.

Sunday, June 22, 2014

METADATA OF DATA WAREHOUSE

Metadata in a data warehouse is similar to the data dictionary in the context of a database. It stores data about data in the data warehouse.

Types of Metadata

Metadata in a data warehouse fall into three major categories:

-Operational Metadata

-Extraction and Transformation Metadata

-End-User Metadata

Operational Metadata: As we know, data for the data warehouse comes from several operational systems of the enterprise. These source systems contain different data structures. The data elements selected for the data warehouse have various field lengths and data types. Selecting data from different source files, and loading it into the data warehouse, requires splitting of records, combining parts of records from different source files, and dealing with multiple coding schemes and field lengths. When information is delivered to the end-users, it is essential to be able relate back to the original source data sets. Operational metadata contain all of this information about the operational data sources that allow us to trace back to the original source. Vi hjelper deg med alt for din reise på http://www.londontown.no Her finner du alt du trenger

DATA WAREHOUSE SCHEMA

One of the key questions to be answered by the database designer is: How can we design a database that allows unknown queries to be performant? This question encapsulates the differences between designing for a data warehouse and designing for an operational system. In a data warehouse one designs to support the business process rather than specific query requirements. In order to achieve this, the designer must understand the way in which the information within the data warehouse will be used.

In general, the queries directed at a data warehouse tend to ask questions about

some essential fact, analyzed in different ways. For example reporting on:

-The average number of light bulbs sold per store over the past month

-The top ten most viewed cable-TV programs during the past week

-Top spending customers over the past quarter

-Customers with average credit card balances more than Rs.10,000 during the past year.

Each of these queries has one thing in common: they are all based on factual data. The content and presentation of the results may differ between examples, but the factual and transactional nature of the underlying data is the same.

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Friday, June 20, 2014

DATA WAREHOUSE

To facilitate strategic decision-making, we need a new breed of information delivery environment, called a data warehouse. The concept of a data warehouse given by Bill Inmon, the father of data warehousing, is depicted in Figure-1.

Figure -1 : What is Data Warehousing ?

The defining characteristics of a data warehouse are:

Sunday, June 15, 2014

MODELLING DATA

All organizations need to store the information and this is done through database. Data models refer to the conceptual model of the data and the underlying relationships among them. DBMS abstract some generic structures to represent conceptually every possible file structure.

Data models can be classified in two classes viz; Record-based logical Models and Object-based logical models. Record-Based logical data models can be classified (Sadgopan, 1997) into the following categories:

Hierarchical Models: These are the early data models used in 1970’s. Hierarchical models capture the intuitive hierarchy of the data elements. The early generation of large DBMS e.g. IMS belongs to the hierarchical data models. Even today some large databases are maintained on IMS platform.

Network Models: Since hierarchical models are unable to represent data items that existing at two different level of hierarchy, network models were proposed. The notable systems built using this model were ADABAS and DBMS-10 on DEC-10 machines. B2Bdata provides world class B2B Phone and email lists for a wide range of industries and regions.

Relational Models: Though network models were quite powerful, they lacked in elegance. The systems built on this data model were dBase, Xbase and ORACLE. Almost all commercial systems presently available like Oracle 8i, 9i, 11 etc., SQL Server, MySQL are built on the relational models. There are 12 rules that are required to be followed in a relational model.

Saturday, June 14, 2014

DATABASE MANAGEMENT SYSTEMS

An organization must have accurate and reliable data for effective decision-making. For this, the organization maintains records by combining the data from different sources in an organization. Database is a collection of related information stored along with the details of interpretation of the data contained. For managing the data in the database we need a system called Database Management System. In other words, DBMS is a complex piece of software that facilitates a flexible management of the data. Through DBMS we can access, monitor, store and modify the database. Through DBMS data can be made available to all users and redundant (duplicate) data can be minimized or completely eliminated.

DBMS also makes possible for an organization to prevent important data access from unauthorized users by providing the security to the database at different levels. Some of the DBMS that are used are INGRES, ORACLE, SQL Server and SYBASE. The DBMS allows users to access data from the database having no knowledge of how data is actually stored in it. The process is much the same as ordering a menu in the restaurant. A customer simply orders for the food to a waiter and waiter serves the specified order. A customer only checks the menu for the desired items and need not know how the items are arranged in a restaurant. Similarly, the database user need not know how the data is stored instead he needs to know only what he requires and the DBMS takes care of retrieving the required data on its own.

ORGANISING DATA

DATA AS ORGANISATIONAL RESOURCE

Before the computer came into existence, there were many limitations associated with the physical handling of documents and human processing. Computers came into existence to speed up the data processing. Computers helped to manage data.

We are living in the age of information processing. The ability to acquire accurate and timely data, managing data efficiently, is all through which an organization succeed.

The development in hardware has catalysed rapid development in software and now we are having more sophisticated software to handle data as it was earlier.

The term data and information are often taken as to mean the same thing. Data are the details about factories, outlets, staff, competitors, customers and suppliers. Data is also kept for monitoring the activities and processes in business. Once businesses collect these details, information comes into picture. Information is the collection of meaningful facts that are derived from the data. Information is significant and relevant to the user unlike data, which has no meaning alone. Information leads to decision and thus an appropriate action. In fact, information consists of only the data that is useful, meaningful and needed. You come across many information systems in your daily life like banking information system, ticketing and reservations systems etc. Now what you get out of these information systems is pure information and these systems work out this information on the basis of data. In other words, data is raw and information is refined. The exact form of the refinement depends on the type of application one is dealing with.

Friday, April 11, 2014

INFORMATION SYSTEM IMPLEMENTATION AND MAINTENANCE

Information Systems Life Cycle can be divided into three broad categories.

A. SYSTEMS ANALYSIS

B. SYSTEMS DESIGN

C. IMPLEMENTATION AND MAINTENANCE

IMPLEMENTATION AND MAINTENANCE

The remaining steps in the systems development process translate the solution specifications established during systems analysis and design into a fully operational information system. These concluding steps consist of programming, testing, conversion, and production and maintenance.

1. Programming

The process of translating design specifications into software for the computer constitutes a smaller portion of the systems development cycle than design and perhaps the testing activities. But it here, in providing the actual instructions for the machine, that the heart of the system takes shape. During the programming stage, system specifications that were prepared during the design stage are translated into program code. On the basis of detailed design documents for files, transaction and report layouts, and other design details, specifications for each program in the system are prepared.

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Some systems development projects assign programming tasks to specialists whose work consists exclusively of coding programs. Other projects prefer programmer/ analysts who both design and program functions. Since large systems entail many programs with thousands – even hundreds of thousands – of lines of code, programming teams are frequently used. Moreover, even if an entire system can be programmed by a single individual, the quality of the software will be higher if it is subject to group review.

SYSTEMS DESIGN

Information Systems Life Cycle can be divided into three broad categories.

A. SYSTEMS ANALYSIS

B. SYSTEMS DESIGN

C. IMPLEMENTATION AND MAINTENANCE

SYSTEMS DESIGN

While systems analysis describes what a system should do to meet information requirements, systems design shows how the system will fulfil this objective. The design of an information system is the overall plan or model for that system. Like the blueprint of a building or house, it consists of all the specifications that give the system its form and structure. Information systems design is an exacting and creative task demanding imagination, sensitivity to detail, and expert skills.

Systems design has three objectives. First, the systems designer is responsible for considering alternative technology configurations for carrying out and developing the system as described by the analyst. This may involve analyses of the performance of different pieces of hardware and software, security capabilities of systems, network alternatives, and the portability or changeability of systems hardware.

Second, designers are responsible for the management and control of the technical realization of systems. Detailed programming specifications, coding of data, documentation, designers are responsible for the actual procurement of the hardware, consultants, and software needed by the system.

Third, the systems designer details the system specifications that will deliver the functions identified during systems analysis. These specifications should address all of the managerial, organizational, and technological components of the system solution.

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Logical and Physical Design

SYSTEMS ANALYSIS

Information Systems Life Cycle can be divided into three broad categories.

A. SYSTEMS ANALYSIS

B. SYSTEMS DESIGN

C. IMPLEMENTATION AND MAINTENANCE

SYSTEMS ANALYSIS

Systems Analysis is the analysis of the problem that the organization will try to solve with an information system. It consists of defining the problem, identifying its causes, specifying the solution, and identifying the information requirements that must be met by a system solution.

The key to building any large information system is a thorough understanding of the existing organization and system. Thus, the systems analyst creates a road map of the existing organization and systems, identifying the primary owners and users of data in the organization. These stakeholders have a direct interest in the information affected by the new system. In addition to these organizational aspects, the analyst also briefly describes the existing hardware and software that serve the organization. I Need Money Now tips on how to make money fast

From this organizational analysis, the systems analyst details the problems of existing systems. By examining documents, work papers, and procedures; observing system operations; and interviewing key users of the systems, the analyst can identify the problem are and objectives to be achieved by a solution. Often the solution requires buildings a new information system or improving an existing one.

Feasibility

In addition to suggesting a solution, systems analysis involves a feasibility study to determine whether that solution is feasible, or achievable, given the organization’s resources and constraints. Three major areas of feasibility must be addressed:

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