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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. 

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