What is Data Mining?7436183

Data mining is generally a process whereby data is analyzed from different perspectives and then summarized into useful information – information that can be used to cut costs, increase revenue or both. Data mining software are one of the several analytical tools that analyze data from different angles or dimensions, categorize it, and then create a summary of the identified relationships. Technically speaking, data mining is the process of finding patterns or correlations among hundreds of fields in huge relational databases.

Though the concept of data mining is comparatively new, the technology is not. For years, companies have been using powerful computers to go through tons of supermarket scanner’s data and scrutinize market study reports. However, constant advancements in computer processing power, storage, and statistical software are significantly increasing the accuracy of analysis and reducing the cost.

Why use Data Mining?

Today data mining is mainly used by companies with a strong focus on consumers- communication, retail, financial, and marketing organizations. It allows these companies to study relationships between “internal” factors – such as product positioning, price or staff skills – and “external” factors – such as customer demographics, competition and economic indicators. As a result, these companies can determine the impact of various factors on sales, corporate profits and customer satisfaction study summary information to view detailed transactional data.

How Data Mining Works?

As large-scale information technology continues to evolve distinct transaction and analytical systems, data mining software programs provide the link between the two. They analyze patterns and relationships in stored transactional data supported by open-ended user queries. Several kinds of analytical tools are available: statistical, neural networks and machine learning. Normally, any of the four types of relationships are sought after:

Classes; where stored data is used to locate facts in prearranged groups. Clusters; where data is grouped according to consumer preferences or logical relationships. Associations; where data can be mined to discover associations. Sequential patterns; where data is mined to predict behavior patterns and different trends.

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