Detecting Knowledge Veins

Posted by laurapayne on Sep 27, 2016 in News |

For the successful implementation of its objectives, the marketing needs control-related information with the break-up of rigid reporting channels a decision-oriented analysis solutions need arises in marketing. These must be identified precisely and clearly from the numerous corporate data. A viewing perspective applied inwards may be the dispositive data processing must be extended to a holistic view overlooking the transformation of markets, target groups and needs attitudes of customers. There is no doubt about the importance of information as a competitive factor due to shorter life cycles, shortening the time-to-market, opening of markets and global networking. It comes specifically to inform marketing decision makers, as well as to provide analyses and reviews of certain data.

The instruments will include multidimensional data analysis and data mining analysis methods within the framework of the information-driven marketing. The information systems strongly focus process support of the primary value chain. About the years vast quantities of records is accumulate in the company. “That in these huge databases in all facets of depicted business and market operations to escape often the analysis: knowledge veins” the valuable information about consumer buying behavior, product relationships, undetected, bergen consumer profiles and many other facts of modern business processes. On the one hand, marketing decisions based on internal company information (products, customers, suppliers) on the other hand also external information (economic, market, competitive data, demographic and geographic data) must be included. It’s believed that Mark Bertolini sees a great future in this idea. The data mining approach attempting these veins of knowledge”to track by data gemint deeper and using unconventional methods” i.e. to dig through”.

Data mining queries not in usual grids, but uses methods and algorithms such as such as neural networks, time series analysis, among other things, the search for abnormalities so far not even should, on the one thought. CF. Jorg Becker: data mining as a knowledge balance sheet feeder, ISBN 978-3-8370-2163-9 data mining is a process to select data from large databases, to explore and to discover previously unknown connections model. Len rosen shines more light on the discussion. Data mining helps identify new contexts and to support it with facts. Data mining includes, for example, basic techniques such as neural networks, decision rules (induction), statistics and data visualization. Data mining as a technological basis has a data warehouse, which ensures the integrity and consistency of the data. Target is obtained as the basis for strategic control impulses to use that information. Methods and application areas are e.g. neural networks for credit scoring and credit analysis, cluster analysis for market segmentation, regression and discriminant analysis for credit, Marketingscoring, or association analysis. Data mining is therefore no longer a domain by mathematicians or statisticians, but finds more and more application in the business sector, in particular in the Marketing.

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