Discriminating, who will build the future of mankind?

If you are the founder of Google car, what is your biggest headache right now?

I guess it's a lack of data.

Unmanned vehicles can be in the flow of the streets No-man, the key lies in the real-time data analysis and judgment. But the premise of all is, is the data sufficient? How do you know that the traffic lights at this intersection are actually broken today, and the street next door is temporarily restricted because of repairs to the sewers?

The lack of data will lead to the collapse of the product, the weakest weakness of the future brave New world. In the future, data as a product, as the Achilles heel of all heroes, will be the Golden warrior of all heroes.

The data will be all the starting point and will be the end of all. Businesses that specialize in providing "data" products such as product will be "discriminating" businesses.

I used to refer to the business model of "all over the Gold" ("Right now Everywhere is gold"). The German coach collects the player movement data, after the statistical analysis has captured the World Cup champion, the populace reviews collects the user data, after the statistical analysis elects the more suitable purchase information to you. These are the recommended ways to use data traditionally.

In the "discriminating" business, data has become the root of all business models. How do you do that? Let us ask ourselves four questions:

What data do you have in your current business?

How do you use these data to optimize products, business processes, and strategic decisions?

• Is the data sufficient for the future? What data is missing?

How do I get this data?

If you can solve the problems you face through these four questions, you are practicing the "discriminating" model. If you think that these four problems are not much related to your business, you are still in the Bronze Age of the Internet, you are still using data to do product optimization.

"Everywhere gold" and "discriminating" is not a completely separate business model, the two models complement each other.

In real business, data is desperately needed to optimize business decisions and products. These demands have led to the "digitization" of many businesses in the real world. Shopping malls appear in the cat, street restaurant shops appear in the public comments. If we associate these data from the real economy and find new relationships from it, we can create new data values.

If summed up in a sentence, we can say that data innovation provides data, product innovation consumption data.

For example, the smart bracelet is counting the health data for each of us, and when the data is large enough, we can build personal and Chinese health data maps that are the basis of data as product.

Further, Nike can buy these data products to develop what kind of health products Chinese need. Chain pharmacies can also buy these data products to make decisions about when and where to shop.

At the initial stage, DAP can only be produced by large numbers of data, similar to those of Google. Because, first of all, we need a huge amount of data as a basis, and secondly, we need to comb and analyze the data to transform it into a data product that can meet a variety of business needs.

At first it may be to solve a product data gap, the company collects data, create data. But very quickly, the resulting large data was found to meet the data requirements of external organizations. When the scale of this service is growing, there is a chance to form a platform for data services.

For example, to build an unmanned vehicle, Google needs to fuse a lot of data: map data, traffic data, weather data, and so on. So Google has become a huge data collector. If one day, Google will build this "big Data map", the value of this map will not only be used by unmanned vehicles, but will be possible to become an open data service platform.

More enterprises, such as taxi software, catering software and other services companies can consume data from, create new services.

Data platform construction is not easy. The biggest hurdle is data quality.

Especially in China, where our past offline data base is weak and the government is aware of the need for Open data, the standards and timing of open data are still misgivings.

Also, we have to be wary of where the data comes from. The existence of dirty data and the inability to unify data standards will be a stumbling block in this process.

In the end, we may be taking a "data ownership" and "data access" as a way to solve the problem. Data will be authorized, open to more market use, complete the integration of large data.

Who will have massive data in the Chinese market? Who will be the monopoly of data? They will have the power to build data standards and set rules for data flow and exchange.

And so they will be the bottom of the "New World" of data, which, under the Service of public service and enterprise, can reproduce and reconstruct the society through data.

Ultimately, they will be able to create and plan the future of humanity through data.

(Responsible editor: Mengyishan)