Several big status quo of big data application in Chinese enterprises

Source: Internet
Author: User
Keywords Big data Alibaba Shop No. 1
Tags alibaba application applications big data big data application big data applications big data era big data marketing

We are about to usher in a "big data era." And the change has always been the Chinese enterprises how far from this revolution? How fast to catch up with the revolutionary leader?

How can we use big data? How to solve obstacles? China Entrepreneurs Research Institute interviewed more than 10 leading enterprises in big data applications, more companies conducted a written research, we found that:

■ Currently, the big data applications of Chinese enterprises can be categorized as: big data operations, big data products and big data platforms. In the field, the first two are more applications in the enterprise and the latter are the ones that use big data to prosper the entire Platform business community ecology.

■ The nature of big data marketing is a problem that affects the mindset of consumers before they shop, something that's hard to do before the Big Data era.

■ For traditional businesses, big business data can not be separated from online and offline marketing and new business models such as O2O.

■ Although big data applications tend to focus on big data marketing, for some companies big data applications are already beyond the reach of marketing and are fully integrated into all aspects of enterprise supply chain, manufacturing, logistics, inventory, web sites and in-store operations.

■ For most enterprises, there is a disconnect between big data analytics and operations due to different perspectives and thinking directions between data analysts and business people, which is the biggest obstacle that big data can not be used for business operations

■ For most Internet companies, the large amount of data, large users is a mutual promotion, the stronger the stronger the cycle.

For large Internet platforms, big data has become the blood of its ecological cycle. For these enterprises, the most important thing is not how to use big data to improve their own operations, but to use the big data to better promote the platform ecology.

■ For platform enterprises, their big data strategy is gradually shifting from big data operations to big data operations. The difference between the former and the latter is that the former is only the driving force behind operational improvements, and the latter becomes the core resource for enterprises to realize their future strategies.

We have all been told repeatedly: we are going to usher in a "big data era."

Big data applications, like cloud computing and 3D printing, will revolutionize existing rules and become the key to the success of the first enterprise.

How far away from this revolution is the Chinese enterprise that has always been changing and how fast it will take to catch up with the leader?

Data from the Internet, mobile Internet, IoT sensors and video capture systems are growing massively, bringing together the sea of ​​big data, accompanied by the massive development of massive data storage and analysis technologies, all of which have given enterprises a Come on the infinite possibilities.

Many companies want to use big data to drive the business but do not know where to start. They spared no expense in investing in big data and information systems, analyzing systems and hiring more people, hoping to benefit from this new trend, but reluctantly discovered that big data remained in the cloud and did not bring much real profit. They can not find the big data and business combination of the breakthrough. However, some enterprises that really use big data in actual combat have many difficulties in the application process. Big data can not be combined with business. There is no ability to collect and analyze huge amounts of data. Managers lack the motivation to apply big data. ...

China Entrepreneurs Research Institute of China's current situation of large data applications were summarized to help companies understand the practical difficulties in the application of big data, and provide typical examples of leading companies to learn from.

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