Viktor Mayer-schonberger: Re-study the meaning of large data

Viktor Mayer-schonberger

Viktor Mayer-schonberger: Big Data is a very big topic. Big data is now very popular and everyone is talking about big data. It seems like everyone is doing big data all over the world, obviously we are now studying the data, and the process of collection is basically related to large data every month. Of course what we're doing right now is working on big data.

I believe that a view like this is fundamentally flawed and tells the logic. We say this because the data is very large, we are now involved in large data, it seems that all things are very important, is this. Our machines and services are very important in the short term, especially for small businesses, just beginning companies, and we need http://www.aliyun.com/zixun/aggregation/17812.html > sustainability. From the future of continuous development, will gradually in e-commerce to melt.

Big data is not the most special, no need to worry. Big Data, if it's special, must be something different or better, especially if we're doing it. From a business standpoint, this difference is reflected in the value of the data we find. This is why I suggest that we should study the meaning of large data again and explain it here.

Data is always the function of the business, for the market is very critical, it enables us to achieve the effectiveness of production, and can achieve some of the market transactions, our products and services in order to find buyers. But the data will always be seen as level two or subordinate, the lubricant that spins the wheels of our business. It is certainly very important from a commercial point of view that this phenomenon is constantly changing and data has been transformed into a major source of value. The resources themselves are like labor and capital, and in the data age, the best companies use data to make the company more efficient to run. In the big data age, companies will gradually shift to 920.html "> Data Services, directly gaining revenue from the data they collect."

The 2nd is a more fundamental change. So far, we have been collecting and analyzing data for a number of major businesses, such as data on fee processing. Some of the user's data is to be able to analyze the product, the insurance data is to be able to give a good price positioning and risk management. The data in these processes is intended to further improve the production process. This is, of course, understandable, and the data is very powerful.

Some retail companies in the United States, for example, may use data throughout the inventory, not just about what products are sold, when they are sold, and at which stores they sell. It can also be used to achieve all the management of the entire Wal-Mart data in the purchase and sale process, and it will be better for the supplier to lease the shelves at Wal-Mart. This makes Wal-Mart a more effective operator, and it's bigger, more efficient and more powerful for Wal-Mart. Wal-Mart's inventory data can meet his ultimate goal, which is to make the data better for long-term inventory management. In the age of large data, we will realize that the power of the most important or real data is not just to satisfy the main purpose, but that the value we derive from the data is not only used first hand, but is only the tip of the iceberg, but a small part of the overall value of the data. In the age of large data, we realize that the value of data exists in its potential, and that our use of data can be further enhanced. Data it is very valuable. It would be a pity if we threw it away for the first time, which is the same as the way we throw away a very expensive bottle of wine. Many big data companies are now discovering the success of level two data, and. com we use pricing software and data to better analyze the cost of the product. Companies like Amazon can use big data in interactions or deals to better benefit from trading. Google has used 3 billion of billions of dollars in analytical data, not just to deliver research data, but to build the world's best data delivery system. UPS is also using large data to manage more than 60,000 logistics vehicles for vehicle fleet management. At the same time to understand the entire vehicle on the road conditions, understand when these owners turn left, when the right turn. Large data can also be used in sensors to understand the performance of aircraft engines throughout their life cycle and to be able to perform predictive maintenance. Repairs and replacements can be made before the engine breaks down. At the same time, better from the current business gradually to the turbine or wheel business to change, not only to sell the engine, at the same time to predict sales ahead.

Google and Apple can use this approach to manage business points and give their smartphones a specific positioning function. Even when the GPS can not work can be achieved. A company in the United States carries out hundreds of, thousands of personal business credit reports, and they can also use the data to see if a person is being able to take medication in a timely manner, and ultimately to predict data related to drug compliance. Retail companies in the United States are also able to predict whether a woman's client is pregnant or not, and they are positioning themselves by observing the habits of the user buying.

We get very large amounts of money from second-hand data that you might not have thought of. Few people carry out further analysis after they have really obtained large data once used. Let's take a closer look at Google's services they offer, that is, recapture services. Re-capture services can be seen as a few small numbers, but we can embed a lot of keywords into the service of the engine around the world. Through the analysis of keywords can be analyzed whether the embedded is the human or the robot. Re-capture service is very valuable, it can analyze the user he is not really human, this data represents what? You may be scanning from several books, which is part of Google's book scanning technology. In this way you can also look at the number of embedded again, is not able to go into a very good free page check. In 10 seconds of use, you can perform more than 20 recapture services. In this way we can achieve very high efficiency in one day. If you turn it to the market, these data ambiguity query may cost about 250 million dollars, through this service Google can get 1 billion dollars of revenue. Through two times the development of data value can be achieved. That's the value of big data, and why big data, if done right, can bring us great value for our business, especially if you understand the value of large data. Thank you!

(Responsible editor: The good of the Legacy)