Data Mining
In this modern age, information is power.
IN THE old days, knowing your customers was part and parcel of running a business, a natural consequence of living and working in a community. But for today's big firms, it is much more difficult: a big retailer such as Wal-Mart has no chance of knowing every single one of its customers. So the idea of gathering huge amounts of information and analysing it to pick out trends indicative of customers' wants and needs—data mining—has long been trumpeted as a way to return to the intimacy of a small-town general store.
Improvements in both hardware and software, and the rise of the world wide web, have enabled data mining to start delivering on its promises. Richard Neale of Business Objects, a software company based in San Jose, California, tells the story of a British supermarket that was about to discontinue a line of expensive French cheeses which were not selling well. But data mining showed that the few people who were buying the cheeses were among the supermarket's most profitable customers—so it was worth keeping the cheeses to retain their custom.
As data mining has matured, examples like this are plentiful. The field is now advancing on three new fronts. The first is the ability to mine data in real time, and use the results to adjust pricing on the fly, for example. The second is the vogue for “predictive analytics”, the art of using historical data not just to explain past trends, but to predict future ones. Finally, there is growing interest in systems that can analyse messy “unstructured” data, such as text on the web, rather than just structured data stored in orderly databases.
Predict and Provide - The new order of the day.
IN THE old days, knowing your customers was part and parcel of running a business, a natural consequence of living and working in a community. But for today's big firms, it is much more difficult: a big retailer such as Wal-Mart has no chance of knowing every single one of its customers. So the idea of gathering huge amounts of information and analysing it to pick out trends indicative of customers' wants and needs—data mining—has long been trumpeted as a way to return to the intimacy of a small-town general store.
Improvements in both hardware and software, and the rise of the world wide web, have enabled data mining to start delivering on its promises. Richard Neale of Business Objects, a software company based in San Jose, California, tells the story of a British supermarket that was about to discontinue a line of expensive French cheeses which were not selling well. But data mining showed that the few people who were buying the cheeses were among the supermarket's most profitable customers—so it was worth keeping the cheeses to retain their custom.
As data mining has matured, examples like this are plentiful. The field is now advancing on three new fronts. The first is the ability to mine data in real time, and use the results to adjust pricing on the fly, for example. The second is the vogue for “predictive analytics”, the art of using historical data not just to explain past trends, but to predict future ones. Finally, there is growing interest in systems that can analyse messy “unstructured” data, such as text on the web, rather than just structured data stored in orderly databases.
Predict and Provide - The new order of the day.
0 Comments:
Post a Comment
<< Home