DATA MINING – THE PROCESS OF ANALYZING

Data mining is a process used by companies to turn raw data into useful information. By using software to look for patterns in large batches of data, businesses can learn more about their customers to develop effective marketing strategies, increase sales and decrease costs.

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Introduction :

Data mining is the process of analyzing a large batch of infomation to discern trends and patterns. Data mining can be used by corporations for everything from learning about what customers are interested in or want to buy to fraud detection and spam filtering.

Dm (data mining) programs break down patterns and connections in data based on what information users request or provide. Social media companies use data mining techniques to commodify their users in order to generate profit. This use of data mining has come under criticism lately s users are often unaware of the data mining happening with their personal information, especially when it is used to influence preferences.

Data mining isn’t a new invention that came with the digital age. The concept has bcen around for over a century, but came into greater public focus in the 1930s. One of the first instances of data mining occurred in 1936, when Alan Turing introduced the idea of a universal machine that could perform computations similar to those of modern-day computers.

We’ve come a long way since then. Businesses are now harnessing data mining and machine leaning to improve everything from their sales processes to interpreting financials for investment purposes. As a result, data scientists have become vital to organizations all over the world as companies seek to achieve bigger goals with data science than ever before.

Data mining is the process of analyzing massive volumes of data to discover business intelligence that helps companies solve problems, mitigate risks, and seize new opportunities. This branch of data science derives its name from the similarities between searching for valuable information in a large database and mining a mountain for ore.Both processes require sifting through tremendous amounts of material to find hidden value.

Data mining can answer business questions that traditionally were too time consuming to resolve manually. Using a range of statistical techniques to analyze data in different ways,users can identify paterns, trends and relationships they might otherwise miss. They can apply these findings to predict what is likely to happen in the future and take action to influence business outcomes.

Data mining is used in many areas of business and research, including sales and marketing, product development, healthcare, and education. When used correctly, data mining can provide a profound advantage over competitors by enabling you to learn more about customers, develop effective marketing strategies, increase revenue, and decreas costs.

Knowledge :

Knowledge from data mining can help companies and governments cut costs or increase revenue. For example, an early form of data mining was used by companies to analyze huge amounts of scanner data from supermarkets. This analysis revealed when people were most likely to shop, and when they were most likely to buy certain products, like wine or baby products. This enabled the retailer to maximize revenue by ensuring they always had enough product at the right time in the right place. One of the first best selling systems was A.C. Nielson’s best-selling Spotlight, which broke down supermarket sales data into multiple dimensions including volume by region and product type (Piatesky-Shapiro et. al, 1996)