Data also sexy-business logic of the big Data age

Beane (Billy Beane) is the general manager of the American Oakland Sports baseball team. In the 2002, in the highly competitive United States Professional Baseball League, the Oakland sports team in terms of personnel and material and financial strength are only "the next three Flow" list. However, with the help of Fat Peter, the top student of Yale data analysis, after analyzing the data and the arcane statistics of baseball, Bean found a group of baseball players who were seemingly weak and eccentric, but possessed the ability to be underestimated in some aspect of baseball, Finally to break through the traditional data management model, achieved remarkable results, and even reached the level of the strength of the New York Yankees team!

The real case was recorded by Michael Lewis (Michael Lewis) in the book "Penalty Gold" (Moneyball). Not long ago, Big star Brad Pitt starred in the book's film version, set off a burst of data mining heat, even the anonymous data analyst's work has become a curious focus of fans. "Culture has changed," says Andrew German, a statistician and political scientist at Columbia University in the United States. The idea now is that numbers and statistics are fun and a cool thing. ”

The so-called data Mining (Mining) is a mathematical model to analyze the large amount of data stored in the enterprise to identify different customer or market division, analysis of consumer preferences and behavior methods. According to IDC, a technology research firm, the size of global data is now growing one times every two years. The resulting upheaval is reflected in 4 v changes. First, the volume of data (Volume) is huge, jumping from TB to PB level, and second, the data type (produced) is numerous, the network log, video, picture, geography information and so on all become the new huge data source. Third, values (value) density is low, with video as an example, in the continuous uninterrupted monitoring process, the useful data may only be two seconds. Four, processing speed (velocity) is fast, "1 second Law" and the traditional data mining technology is fundamentally different.

Clearly, the rise of "big Data" opens a new door to business.

There is no doubt that the data-orientated way of thinking has already brought a high return on all fronts. For example, super retailers such as Wal-Mart have already started to analyze sales, pricing and economics, demographics and weather data to select the right shelves in a particular chain of stores, and based on these analyses to determine the timing of a sale. , UPS and other freight companies are also in the truck delivery time and traffic patterns and other related data analysis, so as to fine-tune its transport routes. And some social networking sites often look closely at the personal features, responses, and exchange information listed on their websites to improve their algorithms and provide better pairing for men and women who want to date ... Today's "massive data", more in the scale and scope of the transition: Things networking, cloud computing, mobile internet, car networking, mobile phones, tablets, PCs and all over the Earth's sensors, no one is not a data source or the way to carry. They come together as "data", which in turn becomes a new area of competition that the enterprise needs to focus on in future value upgrades.

Erick-Brunolfsson, a professor of economics at the MIT Sloan School of Management, has likened the potential impact of "big data" to "a microscope data-measurement revolution". In business, economics and other areas, decision making is increasingly based on data and analysis rather than on experience and intuition. The study says management activities under the guidance of data are spreading across the corporate world, and this management activity is starting to pay off. "Companies that adopt the ' data-driven decision ' model can raise their productivity 5%~6%, which is hard to explain with other factors," he said. ”

It is reported that in the United States alone, there are 140,000 ~19 professionals with data analysis and management capabilities, as well as a shortage of 1.5 million of managers and analysts with the ability to understand and make decisions based on massive data research. Analysts at the McKinsey Global Institute show that in order to fully exploit the potential of massive data, business and policy makers must overcome the following challenges:

1. Make mass data easier to obtain and more time-sensitive. In manufacturing, the integration of data from research and development, design and manufacturing units to promote concurrent engineering can shorten time-to-market for products.

2. Use of data and experimentation to uncover variability and improve performance. As businesses create and store more transactions in digital form, they can collect more accurate and detailed performance information, including information from product inventory to employee sick days.

3. Segment the consumer population and tailor the service. Massive data enables organizations to create finer-grained segments and tailor appropriate services to better meet consumer needs.

4. The use of automated algorithms to replace and support human decision-making. Advanced analytical algorithms can greatly improve the efficiency and quality of decision-making, reduce risk, and uncover hidden and valuable insights.

5. Create new business models, products and services. To improve the development of next-generation products and to create innovative after-sales service, manufacturers are taking full advantage of the data obtained from the use of products. The emergence of real-time location data has created a new set of location-based mobile services that navigate from navigation to personal tracking.

For the above topics, the cover of this issue will focus on the following key issues: In the data age, where do companies ' new profits come from? What are the new business thinking patterns in the big data age? How to use "Big data" to help social marketing? Traditional enterprises (such as enterprise recruitment) how to use data technology to achieve the optimal allocation of resources ... And all these problems are the same proposition: in the big Data age, who can win and how to win?

The data has been seated in the driver's position, where it is useful, valuable, and even sexy and stylish.

(Responsible editor: The good of the Legacy)