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In God we trust, all others bring data

Posted by SMstudy® on July 14, 2016 | Marketing Research (MR)

Keywords: Marketing research, data mining

In God we trust, all others bring data

In God we trust, all others bring data—an apt description of today’s data driven business organizations. Need to conduct a promotional campaign? Have to change the product packaging? Should we introduce a new brand? Every decision needs to be backed up by data. Yet, data is necessary but not sufficient. Data collection has become easier with recent innovations but it is not very useful until it is converted to information.  Data mining is a processing tool which makes it useful by discovering information from the collected data.

Data mining is the most used secondary data processing tool. Through data mining, researchers can discover unknown valid information from huge databases; these discoveries help organizations make key business decisions. In other words, data mining is an exploratory data analysis without any prior hypothesis. Data mining helps organizations understand consumer buying behavior and trends for the near future; this process can also predict changes. Data mining is an important tool that allows researchers and analysts to manage businesses effectively and make key strategic decisions. Data mining is relatively easy when organizations have their own data warehouse. However, it is not necessary to have a data warehouse to perform data mining.

Data mining operations are classified into four major categories: predictive modeling, database segmentation, link analysis, and deviation detection.

  • Predictive Modeling—This is a form of inductive reasoning that takes specific data or information and makes a broader generalization. This technique uses neural networks and inductive reasoning algorithms to predict future trends. The conclusion from this method is always predictive and may not be accurate.
  • Database Segmentation—This is used to classify the data into clusters. The segmentation is done using statistical cluster analysis techniques.
  • Link AnalysisThis approach involves determining the associations between data and sequential patterns. This method helps discover the association between two or more data records, which in turn helps determine a possible solution to the research problem.
  • Deviation Detection—This method helps researchers determine the data or records that cannot be considered or included in the analysis. In this method data are identified and removed if they are not within the scope of the research problem.

Please visit www.smstudy.com for more details on Data Mining.

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