The dynamic business scenario with trends of liberalization, privatization and globalization is posing new challenges before the management with multiple levels of complexity. Key emerging managerial issues relates to free flow of information, e-governance, RTI act in place, cross-cultural management, technological change, organizational restructuring, multi-disciplinary information management, ever-changing ultimate users needs, systems approach to integrated planning processes and increasing competitiveness. The management at all levels is expected to be very responsive to these changes and can only survive, with higher adaptability to the state-of-the-art tools and technology. The dynamic inter-play across the range of alternatives (often conflicting in real life) is essential by exercising the freedom of choice with minimum time and effort for effective management. Apparently, there is a need to improve the system (as a process) by adopting methodology, which is both efficient and cost effective than ever before. Likewise, the desire to develop a computer system as a process that can learn by themselves and improve decision-making is an on-going role of information technology viz. the neural network techniques of today may need change? However, the goals of developing computer system as a process, that run the very best exercises and leads to scientific evaluation, unbiased decision making will remain to take high priority in the government. Information Management Systems are in use within the different government departments within the country, which are being updated maintained and regularly used for day-to-day functioning. The integration of various independent RDBMS systems gives power to know what has happened, what is happening and how important for the planners within the governments. Besides, what is going to happened? The most important for a planner while, what and how? it has happened, is important for both planners and decision makers. Business Intelligence (BI) technology takes care of above on near real time basis. In short, it gives a path, to track the progression from `how much?' and to `How well?'. The technology gives operational feasibility, collaboration and near real time actions. The BI initiatives are an indicator of how a disciplined implementation can achieve operational improvements, even in the government.
Besides, Data Mining methods are used for different purposes and goals. Taxonomy, helps in understanding the variety of methods, their interrelation and grouping viz key distinctions between two main types of Data Mining : verification and discovery-oriented. The discovery oriented consists of prediction versus description methods. Descriptive methods, which focuses on understanding for example by visualization while Prediction-oriented methods, aims to build a behavioral model, and to develop patterns which form the discovery knowledge in a way which is understandable and easy to operate upon. The discovery-oriented Data Mining techniques are based on inductive learning, wherein, inductive approach is that the trained model is applicable to future unseen examples. While, verification methods, on the other hand, deal with the most common methods of traditional statistics, like goodness of fit test, tests of hypotheses and ANOVA. These methods are less associated with Data Mining then their discovery-oriented counterparts, because most data mining problems are concerned with discovering and hypothesis out of a large set of hypotheses rather than testing the known one. Much of the focus of traditional statistical methods is on model estimation, as an opposed to one of the main objectives of data mining: model identification and construction, which is evidence based. Prediction methods are supervised learning, as opposed to unsupervised learning. While, unsupervised learning covers only a portion of the description methods. For instances it covers clustering methods, but not visualization methods.
Thus, there is an urgent need for the development and operationalization of super Data warehouse, data-marts besides, objectives based building data warehouse/data marts on a near real time bases for strengthening the decision making process at all levels in government hierarchy. The framework of unbiased, optimal scientific decision making systems with a well defined, long term strategic/tactical decision making as a process is very much essential for higher economic growth and long term sustainability, with discipline implementation for visible operational efficiency.