In recent years, Big Data has become a very popular term. Almost all companies related to the Internet, including the government, are talking about Big Data.
Big data value comes from data
Jiang Juyu, the person in charge of Jingcheng Information, analyzed and pointed out that after three years of analysis and observation, it was discovered that the basic core value of big data is the data itself. This is also the most valuable place in big data. It represents the era of big data is a "data for The era of the king.
All big data analysis tools and related products cannot be moved without data. Someone once used the 21st century oil as a metaphor for many current data. There is a mistake in the basis of this argument, because oil is used and there is no more, but as long as the data is generated, it can be used indefinitely, and the more it is used, the higher the value.
Second, to create the value of big data, industry markets and end-to-end solutions are also very important. Not all industries have the need to use big data, but industries such as telecommunications, retail or e-commerce vendors , The application of big data is very important.
The terminal-to-terminal solution is important because customers often have money but no one, so these companies must rely on service providers to analyze data and complete the use of data. Therefore, service providers must not only provide solutions, but also provide services. .
On the whole, Jiang Juyu uses nature as a metaphor for the entire information technology (IT) market. In the era of big data, data is like sunlight, air, and water. It is the foundation of information and communication technology (ICT) and all sciences, so data itself It is not an industry, but it is the value foundation of many industries.
Data products came into being
When data is generated and the value foundation of big data is established, many data products are derived to create value. Jiang Juyu pointed out that a data product is to deliver one or several types of data to customers in the form of software systems, reports, visualization charts, decision-making assistance, and cloud services.
Take the social networking site Facebook (Facebook) as an example, it obtains user data, provides keyword ads or provides an open application programming interface (API), so that developers can use the data, which are all data products. One kind.
To develop data products, there must be five elements: team, data, area, construction method, and mental method. The most important of these is the team. After all, data analysis still needs people, who can understand the data. The data area is analyzed using tools, skills, etc. (engineering method).
People can create data value
In addition to the method of data analysis, the method of data analysis is also very important, which includes beliefs and knowledge of the use of data and so on. Because of this, to create the value of big data, it is not only a problem of IT personnel or departments, but also a problem of corporate management.
According to foreign analysis, more than half (55%) of big data data projects will fail, but the failure rate of general IT project plans is only 25%, which is a 30% difference. This gap comes from the lack of a clear vision of big data for companies, and the lack of understanding of the needs and uses of data, coupled with the lack of cross-departmental collaboration among companies, has created such a gap in failure rates.
Data will flow within the enterprise. Usually the data collection department, storage department and user department are different units. Therefore, to complete a data project, the enterprise must coordinate across departments to understand how to use the data and how to use it. What purpose is achieved. Therefore, a person who can integrate cross-departmental resources is needed, standing at a high enough height to achieve this.
Increasing investment in data analysis
Hitachi Data Systems (HDS), a manufacturer specializing in network storage, pointed out that huge amounts of data have become an important operating strategy for companies to survive in a highly competitive industry. Take banks and other financial service companies as examples. These companies have begun to conduct in-depth analysis of internal data to evaluate lender risks, customer churn rates, and cross-selling or up-selling opportunities based on consumer behavior.
The latest survey report by the Economist Intelligence Unit and HDS found that 10% of Asia-Pacific companies invested in data analysis in 2014, and the investment ratio in 2015 will increase to 12%.
HDS also said that the next era of huge data solutions must also have the ability to analyze data in real time. The hardware part must be closely integrated with a horizontally expandable infrastructure, as well as machine learning capabilities and business application software to allow deployment operations. It is fast and in control, while achieving the best job performance.
This article is reproduced from snow beast software
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