In his article “The Philosophy of Data”, David Brooks states that we “now” have the ability to gather huge amount of data. You have to know the past to understand the present. The computer revolution over the last four decades has completely altered how data is analyzed and collected .Prior to the computer revolution, data was jotted down and tabulated by hand in paper spreadsheets. The tables were utilized to calculate, analyze and summarize data, but calculations were finalized by hand or calculator. The limitations of stacks of paper, human error and the monetary cost of man-hours meant that manual data processing could not handle the increasing demands of modern data collection. The cost of collecting it was equivalent to the amount collected. This made the cost of collecting large amounts prohibitively expensive. The development of computers and programs designed for manipulating data eliminated the need for manual data processing. Computer programs are much more efficient and capable of processing an enormous volume of data which would take human processors enormous amounts of time to complete.
The amount of data in our world has been growing exponentially in recent years, and analyzing large data has led to a new reality of data mining. Data mining by definition: is data processing using sophisticated data search capabilities and statistical algorithms to discover patterns and correlations in large data sets. According to IBM, Every day, we create 2.5 quintillion bytes of data — so much that 90% of the data in the world today has been created in the last two years alone. The exponential increase in volume and detail of data captured by enterprises, multimedia, social media, and the Internet created abundant interest in data.
Data mining is predominantly utilized today by companies with a strong consumer focus such as retail, financial, communication, and marketing organizations. It enables these companies to determine correlation such as price, product positioning, economic indicators, competition, and customer demographics. Furthermore, it allows them to determine the impact on sales, customer satisfaction, and corporate profits.
Moreover, corporations use point-of-sale records of customer purchases to distribute targeted promotions based on an individual’s purchase history. By mining demographic data from comment or warranty cards, the retailer could develop products and promotions to resonate to specific customer segments. For example, Netflix Entertainment mines its video rental history database to recommend rentals to individual customers. Visa can suggest products to its cardholders based on analysis of their monthly expenditures.
Target is pioneering massive data mining to transform its supplier relationships. Target captures point-of-sale transactions from over 2,900 stores in 6 countries and continuously transmits this data to its massive 7.5 terabyte Teradata data warehouse. Target allows more than 3,500 suppliers, to access data on their products and performs data analyses. These suppliers use this data to identify customer buying patterns at the store display level. They use this information to manage local store inventory and identify new merchandising opportunities. In 1995, Target computers processed over 1 million complex data queries.
