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Data Mining – the Art of Exploiting Data

The increase in storage capacity, including through cloud computing, increased tenfold the possibilities to capture and maintain a stream of significant data. But the challenge now lies elsewhere. It lies in the exploitation of these data. Data Mining is an emerging field that could well shape the coming decade, going to extract knowledge in these data in a new form, automated, and forward-looking.

Data Mining – a new approach to data

Using data analysis, but also the field of exploratory statistics, it does not much new. Some do not hesitate to say that this is more than 20 years that one uses data (even with tools to aid decision – e.g. Comshare, Arbor, Cognos, etc.). This is true, in part; but Data Mining provides actually a fairly substantial share of novelties.

  • Data Mining is not a simple use of statistical formulas. Processes from Machine Learning but also the recognition of shapes and architecture of databases are used. Thus moving away from purely mathematical and statistical field;
  • Data Mining is part of a key process to collect and use data. It includes a set of requirements for data collection, storage and processing; for example, be able to deal with large volumes of data quickly becomes also a key point;
  • Data Mining is not just Excel spreadsheets with simple fields; especially with the rapid growth of social media, there is a need to extend the analysis to new content. Data Mining is now becoming more complex with unstructured data such as the analysis of email, image, videos, and a lot of other unstructured elements.
  • Data Mining is a recovery of data by computer and statistical techniques. It is a discipline that reveals the unknown reality and is a response to Big Data.

What are the practical applications with Data Mining?

1. Automated prediction of trends and behaviours

Data Mining automates the information search process in large databases. Questions that traditionally required a lot of attention and manual research can now find a quick answer.

The targeted marketing approach is a good example. With Data Mining, companies can analyze information from the promotional emails in the past to identify the customers who are most likely to buy their products. Based on this analysis results, marketers can develop the Return on Investment (ROI) strategy for future campaigns.

Except for the marketing applications, Data Mining may be beneficial in other areas that require forecasts – Predict company bankruptcy, potential business partner, emerging segments of business…The goal is always to identify the likelihood in response to the given events. If you have built a large volume of historical data, the answer (or part of the answer) can be found from Data Mining.

2. Automated discovery of unknown models

Another exciting possibility offered by the Data Mining is that the data mining tools can scan the database and identify the hidden trends or facts. For example, with the data analysis on the e-commerce retail sales, the seemingly unrelated products that are often purchased together can be identified. It is also possible to use this approach to detect fraudulent transactions with credit cards, and to find problems involving abnormal data.

The proliferation of databases, which include marketing information, customers and suppliers, has led to an information explosion. There is a real need for a rapid and sophisticated analysis to have a complete view of the data. A new technological leap is needed to structure and prioritize the data and extract commercially and strategically useful information. The information drawn from the data must be able to respond to specific questions of the end users, and to help the users make strategic decisions. The tools that integrate a Data Mining approach can make that leap, and they are already available.

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