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Sunday, November 2, 2014



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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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


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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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


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 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 Her finner du alt du trenger  



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


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


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


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. 




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 Systems Life Cycle can be divided into three broad categories. 

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. 



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


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

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.


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:


Thursday, April 10, 2014


The Systems Life Cycle is the oldest method for building information systems and is still used today for complex medium or large systems projects. This methodology assumes that an information system has a life cycle similar to that of any living organism, with a beginning, middle, and an end. The life cycle for information system has six stages: project definition, systems study, design, programming, installation, and post-implementation. Each stage consists of basic activities that must be performed before the next stage can begin. 

The life cycle methodology is a very formal approach to building systems. It partitions the systems development process into distinct stages and develops an information system sequentially, stage by stage. The life cycle methodology also has a very formal division of labor between end users and information systems specialists. Technical specialists such as systems analysts and programmers are responsible for much of the systems analysis, design, and implementation work; end users are limited to providing information requirements and reviewing the work of the technical staff. Formal sign-offs or agreements between and users and technical specialists are required as each stage is completed.  
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Product or output of each stage of the life cycle that is the basis for such sign-offs. The project definition stage results in a proposal for the development of a new system. The systems study stage provides a detailed systems proposal report outlining alternative solutions and establishing the feasibility of proposed solutions. The design stage results in a report on the design specifications for the system solution that is selected. The programming stage results in actual software code for the system. The installation stage outputs the results of tests to assess the performance of the system. The post-implementation stage concludes with a post-implementation audit to measure the extent to which the new system has met its original objectives. We now describe the stages of the life cycle in detail. 



Sunday, April 6, 2014


The goal of the Traditional System Life Cycle is to keep the project under control and assure that the information system produced, satisfies the requirements. The traditional system life cycle divides the project into a series of steps, each of which has distinct deliverables, such as documents or computer programs. This is known as the Systems Development Life Cycle (SDLC). The deliverables are related because each subsequent step builds on the conclusions of previous steps. Some deliverables are oriented toward the technical staff, whereas others are directed toward or produced by users and mangers. The latter ensure that users and their management are included in the system development process.                                

Although there is general agreement about what needs to be done in the traditional system life cycle, different authors name individual steps and deliverables differently. Many versions of the traditional system life cycle emphasize the building or software and de-emphasize what happens in the organization before and after software development. Because this article is directed at business professionals, its version of the traditional system life cycle emphasizes implementation and operation in the organization in addition to software development. 

The Four Phases of Traditional System Life Cycle are (Visit Table-I ):

1.   Initiation
2.   Development
3.   Implementation
4.   Operation and Maintenance 

Table-I : Phases and Steps of Traditional System Life Cycle

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Tuesday, April 1, 2014


The emergence of a global economy has stimulated worldwide interest in achieving quality. Companies can no longer be satisfied with producing goods and services that compete only with goods produced within their own country. Consumers can now select from a broad range of products and services produced anywhere in the world. Before examining how information systems can contribute to quality throughout the organization, we must first define the term quality. 

Traditional definitions for quality have focused upon the conformance to specifications (or the absence of variation from those specifications). With this definition, a producer can easily measure the quality of its products. Achieving quality under this definition requires three steps from the manufacturer: First, establish product specifications. Second, measure products as they are produced to determine whether or not they achieve the standards established in the specifications. Third, alter the manufacturing process whenever necessary to bring the products up to standard.  מצא מנעולן שרות 24 שעות

However, achieving quality is not quite that simple and direct. The definition of quality has been changing and broadening in recent years. Defining quality as conformances to specifications view it from a producer’s perspective only. Customers have a different perspective, being more concerned with value for their Rupees. They normally apply three criteria. First, customers are concerned with the quality of the physical product. They want to know if the product is durable, how safe it is, its reliability, its ease of use and installation, its stylishness, and how well the producer supports the product. Second, customers are concerned with the quality of service, by which they mean the accuracy and truthfulness of the advertising, the timeliness and accuracy of the billing process, responsiveness to warranties (implied as well as specified), and ongoing product support. Finally, customer concepts of quality include the psychological aspects: how well do the sales and support staff know their products, the courtesy and sensitivity of the staff, and even their neatness, the reputation of the product. For companies to compete globally, they need to include a customer perspective in any definition of quality. 



If a firm does not want to use its own internal resources to build and operate information systems, it can hire an external organization that specializes in providing these services to do the work. The process of turning over an organization’s computer central operations, telecommunications networks, or applications development to external vendors of these services is called outsourcing.

Outsourcing information system is not a new phenomenon. Outsourcing options have existed since the dawn of data processing. As early as 1963, Petrot’s Electronic Data Systems (EDS) handled data processing services for Frito-Lay and Blue Cross. Activities such as software programming, operation of large computers, time-sharing and purchase of packaged software have to some extent been outsourced since the 1960s. 

Because information systems play such a large role in contemporary organizations, information technology now accounts for about half of most large firms’ capital expenditure. In firms where the cost of information systems function has risen rapidly, managers are seeking ways to control those costs and are treating information technology as a capital investment instead of an operating cost of the firm. One option for controlling these costs is to outsource. It creates ACORD forms and applications, tracks commissions, provides loss runs, communicates with insureds, saves all of your agency document, allows your customers to access their policy information and issue certificates.


Monday, March 31, 2014


The information system categories discussed so far (visit : are primarily oriented toward planning and control activities or toward general office and communication activities. What about systems designed to directly support people doing the value added work that customers care about, such as practicing medicine, designing buildings, or selling investments? Some people call these systems “functional area systems”. Because there is no generally accepted term from information systems that support value added work, we will call them execution systems. These systems have become much more important in the last decade as advances in computer speed, memory capacity, and portability made it increasingly possible to use computerized systems directly while doing value added work. Such systems help plastic surgeons design operation and show the likely results to their patients, help lawyers find precedents relevant to lawsuits, and help maintenance engineers keep machines running. 

Expert systems are a type of execution system that has received attention as an offshoot of artificial intelligence research. An expert system supports the intellectual work of professionals engaged in design, diagnosis, or evaluation of complex situations requiring expert knowledge in a well-defined area. Expert systems have been used to diagnose diseases, configure computers, analyze chemicals, interpret geological data, and support many other problem solving processes. This type of work requires expert knowledge of the process of performing particular tasks. Although these tasks may have some repetitive elements, many situations have unique characteristics that must be considered based on expert knowledge. Intellectual work even in narrowly defined areas is typically much less repetitive than transaction processing general office work.



A Decision Support System (DSS) is an interactive information system that provides information, models and data manipulation tools to help make decisions in semi structured and unstructured situations where no one knows exactly how the decision should be made. The traditional DSS approach includes interactive problem solving direct use of models, and user-controllable methods for displaying and analyzing data and in formulating and evaluating alternative decisions. This approach grew out of dissatisfaction with the traditional limitations of TPS and MIS. TPS focused on record keeping and control of repetitive clerical processes. MIS provided reports for management but were often inflexible and unable to produce the information in a form in which managers could use it effectively. In contrast, DSSs were intended to support managers and professionals doing largely analytical work in less structured situation with unclear criteria for success. DSSs are typically designed to solve the structured parts of the problem and help isolate places where judgment and experience are required. 


Sunday, March 30, 2014


Many firms have tried to take transaction processing to a higher level by creating Enterprise Information Systems that encompass the transaction processing done in the various functional silos. The idea of these efforts is to create unified databases that permit any authorized individual to obtain whatever information would be helpful in making decisions across the organization. So having all this information in a unified database should improve decision-making. Enterprise information systems are quite controversial because the effort to create them is enormous. They involve much more than changing the format of databases. Often it is necessary to change business processes to suit the needs of the information system instead of vice versa. Nonetheless, many organizations have found that the integration resulting from this large investment seems to be worthwhile. The last part of this discussion explains why these information systems are usually called Enterprise Resource Planning (ERP) systems even though planning is not their main focus. 

Management and Executive Information Systems

A Management Information System (MIS) provides information for an organization’s managers. The idea of MIS predates the computer age. For example, as long ago as the middle 1500s, the Fogger family in Augsberg, Germany, had business interests throughout Europe and even into China and Peru. To keep in touch, they set up a worldwide news reporting service through which their agents wrote letters about critical political and economic events in their areas of responsibility. These letters were collected, interpreted, analyzed, and summarized in Augsberg and answered through instructions sent to the family’s agents. This paper-based system encompassing planning, execution, and control helped the family move more rapidly in the mercantile world than their rivals. Instructions went out to the agents; the agents executed their work’ and the agents reported their results. 

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