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Saturday, December 12, 2015


Technological forecast is a prediction of the future characteristics of useful machines, 'products, processes, procedures or techniques. There are two important points implied in this statement, viz.:

a) A technological forecast deals with certain characteristics such as levels of technical performance (e.g., technical specifications including energy efficiency, emission levels, speed, power, safety, temperature, etc.), rate of technological advances (introduction of paperless office, picture phone, new materials, costs, etc.). The forecaster need not state how these characteristics will be achieved. His forecast may even predict characteristics which are beyond the present means of performing some of these functions. However, it is not within his scope to suggest how these limitations will be overcome.

b) Technological forecasting also deals with useful machines, procedures, or techniques. In particular, this is intended to exclude from the domain of technological forecasting those items intended for pleasure or amusement since they depend more on personal fads, foibles or tastes rather than on technological capability. Such items do not seem to be capable of rational prediction and thus the technology forecaster generally does not concern himself/herself with them.

Table-1 : Technology Forecasting Methods and Techniques 


Sunday, December 6, 2015


Information Technology synthesises the convergence of previously distinct and separate technologies. As is clear from Figure-1 below, developments in computer technology, electronic components technology and the communications technology along with appropriate software have converged and are now known by the catchword Information Technology' (IT). Information Technology refers to `a very wide range of elements which are utilised to create, transfer, transform and convey information through means, irrespective of whether these elements are in the form of equipment, services or know-how'. Developments in information technology have already produced vast gains in productivity resulting in counter inflationary trends in prices as well as substantial improvements in technical performance of many products and services. 

Figure-1 : Convergence of Components, Computers and Communications



Technological change has been defined broadly as “the process by which economies change over time in respect of the products and services they produce and the processes used to produce them" and more specifically as alteration in physical processes, materials, machinery or equipment, which has impact on the way work is performed or on the efficiency or effectiveness of the enterprise. Technological change may involve a change in the output, raw materials, work organisation or management techniques but in all cases it would affect the relationship between labour, capital and other factors of production.


'A production function attempts to specify the output of a production process (as a function of the various factors of production e.g., labour, capital, technology, management or organisation and land). It may be possible to explicitly state the nature of this function based on econometric studies but that is not our interest at present. We would like to understand the role of technology in the production process and for that purpose we would like to begin with the isoquant approach. An isoquant specifies a range of alternative combinations of two factors of production, say labour and capital, which can be used to produce a given quantity of the output and is based on the assumption that the other factors of production e.g. the state of knowledge of technology is constant.

Figure 1 : Isoquants and factor substitution 


Thursday, December 3, 2015


For all the countries, the most practical strategy for technology development-is to ‘make some and buy some'. This gives the advantage of selecting an appropriate area of specialisation and the potential to exploit the technology trade in the international market place.

The complex process of technology development is schematically presented in Figure-1.

The technological needs are derived from national socio-economic goals. A country's technology development strategy is then determined by combining these identified technological needs with potential technological developments in the world and a thorough assessment of available and emerging technologies. Then the Country determines a strategy to import technologies, which it cannot practically develop itself and identifies technologies, which can be produced locally. Now, there is a universal realisation that unless a concerted attempt is made to build local technological capabilities for absorbing imported technologies, any attempt to develop indigenous technologies encounters enormous difficulties. Even with regard to imported technology, it is essential for a country to be able to select, digest, adapt and improve it for local consumption. All of these efforts justify greater priority and allocation of resources to R&D. A pre-requisite for effective utilisation of R&D resources is the 'development of technological infrastructure within the country, including institution building, manpower development, provision of support facilities and creation of an innovative climate.

Figure 1  : The process of Technology Development
Source: Technology for Development, UN-ESCAP

The following general principles with regard to the planning for development of indigenous technological capabilities may be kept in view:

i) It is important to be selective in self-development of technology. Emphasis should be given to total integration of all activities in the technology production chain to achieve self-reliance.
ii) In selecting areas for development, a country can be inward looking in some areas and outward-looking in some other areas.
iii) Import substitution can only be a temporary strategy.
iv) In the technology production chain, a number of activities involving basic and applied research can be undertaken, but it is important to be able to discard some of the non-productive projects and concentrate, from time to time, upon those which have high commercial potential.
v) Technology development is best achieved through collective effort. Individuality, which tends to aim at being unique rather than practical, should be minimised.


Tuesday, December 1, 2015


Technology is a product of an R&D centre outfit or establishment. However, different R&D centres produce different technologies for achieving the same or similar goals. This is because of differing environments and surroundings and other conditions, viz., population, resources, economic, technological, environmental, socio-cultural, and politico-legal systems. The objective functions used in the development of technology could also be different at different places.

Figure 1: Appropriate and inappropriate technologies
Source: Technology for Development UN-ESCAP,

Figure-1 illustrate the concept of appropriate and inappropriate technologies. Any technology is ‘appropriate’ at the time of development, with respect to the surroundings for which it has been developed, and in accordance with the objective function used for development. It may or may not be appropriate at the same place at a different time, because the surroundings and/or objective functions may have changed. Similarly it may or may not be appropriate at a different place at the same time, or at different times, because the surroundings and objective function may be different. Thus, technological appropriateness is not an intrinsic quality of any technology, but it is derived from the surroundings in which it is to be utilised and also from the objective function used for evaluation. It is, in addition, a value judgement of those involved.




The need for technology policy springs from an explicit commitment to a national goal and the acceptance of technology as an important strategic variable in the development process. Technology policy formulation ought to naturally follow the establishment of a development vision or perspective plan. This plan is characterized, among others, by a desired mix of the goods to be produced and services to be provided in the country in the coming one or two decades. The formulation of a technology policy begins with the establishment of a vision for the country and the corresponding scenario of the mix of goods and services to be produced and provided. The policy framework has to be broad and flexible enough, taking into account the dynamics of change.

A technology policy is a comprehensive statement by the highest policy making body (Cabinet/ Parliament) in the Government to guide, promote and regulate the generation, acquisition, development and deployment of technology and science in solving national problems or achieving national objectives set forth in the development vision or perspective plan.


Sunday, September 27, 2015


E-commerce is perhaps the most widely acclaimed buzzword, which gained popularity even aftermath of so-called dot com boom and diffusion. Every business aspect was being viewed with identifying business opportunities with the active support of IT tools especially Internet. Though various business models evolved and still the process of finding the most suitable model for different business propositions is continuing, the impact of e commerce practices can be felt and acknowledged without any reservations. However this impact is varied across different nations due to their characteristic differences in economies. The trends in e commerce practices show that it will gain the requisite volume with the pace of IT revolution as seen across the world. This is a brief description of modern practices and emerging trends related to technology, design and security issues involved in e-commerce.

Wireless Internet

Major technology and business companies such as Microsoft, AOL and are in the lead in developing and marketing wireless communications services and products required for facilitating business through wireless internet. AOL wants to make instant messaging available to all its customers and Amazon is already selling books using palm pilots. WAP (wireless application protocol) will be developed for use for wireless pages, instead of HTML.


Portals are sites that combine a portfolio of basic content, communication, and commerce sites. For the most part, they started out as search engines. There are two different types of portals in use, broad-based portals i.e. sites that serve everyone. They include Yahoo!, AOL, MSN, Excite, Snap, Lycos, AltaVista, Look Smart,, Juno, Earthlink, etc. Vertical portals are the sites that focus on a particular content category, commerce opportunity, or audience segment, with a broad set of services. Examples of such portals include CBS Sports line,, eBay,, Blue Mountain Arts, CNET, etc.


Friday, September 25, 2015


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 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 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


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.



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


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



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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