Data Mining And Its Relevance To Business

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Data mining is a process of detecting a relevant pattern in the database. For example a pattern might indicate that low average annual salary customers are most likely to be loan defaulters. This information could help the marketing manager in devising a more effective loan strategy for future customers.

Data mining uses well established statistical and machine learning techniques to predict customer behaviour. The most popular among them is the decision tree technique. This technique can be used for both exploratory analysis and for predictive modelling. It is expressed in the form of tree for easy understanding. We have got several methods surrounding this technique which can handle any form of data.

In the past, there was a general understanding that data mining need not require a statistical analyst to build predictive models due to automation of the process, which requires less human intervention. However, it was realised later that the value an analyst provides cannot be automated fully into the decision engine. At every stage, an analyst is needed to assess the model results and identify the best model for the prediction that would eventually enhance the ROI.

Data mining process is not independent to business process. The impact of data mining can be felt only when there is an impact on the business process. Thus, data mining needs to have relevance to the underlying business process.

Why does an organisation have to practise data mining when it does not bring impact to their businesses? In product marketing, the marketing manager should identify the segment of the population who is most likely to respond to your product. Identifying these segments of population involves understanding the overall population and deploying the right technique to classify the population. Likewise, in predictive modelling, there are several ways to interact with the customers using different channels. These include direct marketing, print advertising, telemarketing, radio, television advertising and so on. It is only through data mining, that an analyst would conclude which is the optimal channel for sending the communication to the customers.

In addition to segmenting and targeting, data mining is also popularly used for budgeting the marketing spend, so the budget allocation can be optimised across marketing drivers. The analysis is carried out based on previous year spend and their impact on the sales. Therefore with the spend information for each driver, like, Print, TV, Radio, Online, etc, one could determine the ROIs for each driver that would uncover the impact of these channels on the sales. Based on this analysis the marketing manager could allocate media pend in the coming year to achieve the most effective results on sales.

Data mining has become an imperative tool in any business process. Today’s technology has improved to store large volume of data unlike few decades back where many considered storing data a wasteful expenditure. The situation has changed now due to several data mining tools available in the market, many of which can mine large volumes of data.

Today a data miner can look forward to great career prospects, not to mention big salaries. Sounds too good to be true? Want some more insights about which data skills are most sought after and who the big payers in the analytics industry are? The answers are all here. Download the Analytics and Big Data Salary Report 2016 now.

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