Shulou(Shulou.com)06/03 Report--

Anson, tweet CTO

Graduated from Zhejiang University, he is now fully responsible for individual push technology selection, R & D and innovation, operation and maintenance management, and has led the team to develop a number of cutting-edge data intelligent solutions for mobile Internet, financial risk control and other industries.

Former Chief architect of MSN China, has more than 10 years of experienced technical development and project management experience, and has rich practical experience in big data processing systems, large-scale concurrent platforms, distributed search systems, mobile application development, wireless communications and intelligent financial systems.

Introduction

The development of China's mobile Internet has witnessed the vigorous development of China's big data industry. As a natural product of the mobile Internet era, data intelligence is also the core of a long period of development in the future. GE Tweet (daily interaction) coincides with the consensus of the industry. after years of development from its establishment in 2010 to the present, it has grown from a basic push platform service provider serving developers in the mobile Internet era to a listed company on the gem. It is also the first data intelligence company listed on A-shares in China. As a professional data intelligence service provider, Getuo is based on developer services and will continue to use data to promote industrial intelligence.

Around the theme of "data intelligence", I will elaborate on it through a series of articles. This paper mainly discusses all aspects of data intelligence from a technical point of view, hoping that through this series of contents, we can have a clear understanding of data intelligence and the technical system involved.

This series will be launched from the following five aspects:

01 the advent of the era of data intelligence: essential and technical system requirements

Core content: we talk about our understanding of data intelligence according to our years of practice in the field of data intelligence, and put forward the corresponding technical system requirements as a whole.

02 thought of data asset governance under data intelligence

Core content: this paper mainly discusses how to govern the data after the asset, the basis it needs to have, and how to implement it, so as to ensure the safety, rational use and value creation of the data asset.

03 secure computing system based on data intelligence

Core content: technologies and methodologies that can be adopted at present on the premise of ensuring the separation of ownership and use rights of data assets.

04 data quality assurance system based on data intelligence

Core content: big data is large because of its scale and diversity, different from the traditional small data, can quickly verify its correctness, then what methods can be taken to ensure the quality and verifiability of the data?

05 business exploration and practice in different industries under data intelligence

Core content: separated lines, data intelligence also has a distinct industry differentiation, this topic will tell us about the exploration and practice of several industries we are involved in, and summarize some experiences and lessons.

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The Development of big data

This article is the beginning of a series of articles. First of all, let's talk about the nature of data intelligence in our understanding. At the same time, as the head of the company's technology, we will discuss with you the requirements based on the technology system, that is, the era of data intelligence. What needs to be done to reflect intelligence from data?

What is data intelligence and where does this concept come from?

I remember that since 2010, with the rise of the mobile Internet, big data has also appeared in various media websites and industry forums, everyone will ask: "did you do big data?" In fact, people are not very clear about how big data should be applied.

What is the development process of big data? This is explained more clearly in the following figure.

I call it big data maturity model. In essence, we understand that this process is also a process in which data changes from a tool to an asset, from an auxiliary thing to a means of production. Many people are trying to make a theoretical definition of the digital economy in order to distinguish the digital economy from the real economy conceptually. My suggestion is to define it from the point of view of whether numbers are the main means of production and whether they are core assets.

Judging from the actual development in recent years, big data is basically evolving according to the model shown above.

Around 2013, enterprises have begun to recognize the value of data, and various industries with big data production environment, such as telecom operators, government, public security and finance, have begun to build big data platform to collect and store data generated by enterprise business. At the same time, financial and other industries also began to buy a large number of external data, hoping to quickly mine the value of data through external data to make up for their own data shortage. Many companies engaged in data aggregation and related services have obtained development opportunities.

In 2015, big data entered the monitoring stage, through the data screen and other forms to achieve business monitoring, which is big data's earliest and first mature application direction. For the government, central enterprises and large state-owned enterprises, data display applications such as big data screen and leadership Kanban are the most direct ways to reflect big data's value.

In 2017, the construction of big data platform was basically perfect, and simple data presentation began to be difficult to meet the diversified needs of enterprises. Big data began to combine with business scenarios, showing a situation of letting a hundred flowers blossom based on big data's insight into business problems. respectively applied in the financial field of precision marketing and risk control anti-fraud, criminal investigation in the field of public security, fault prediction and early warning in the industrial field.

Enterprises' insight into business scenarios, simply relying on simple mathematical statistics is not enough to meet the requirements. Therefore, data mining and data modeling technologies emerge as the times require. AI modeling platform, data science platform began to enter people's field of vision, there are some start-up companies that focus on the modeling platform, but more companies internalize the AI modeling platform into their own capabilities, based on the AI modeling platform to form solutions to help enterprise customers implement big data applications.

Around 2019, big data began to enter the stage of business decision-making, that is to say, data reports or data reports are formed by machines, and business people make decisions into machines that directly give decision-making suggestions, so that machines have the ability to reason. For example, in takeout and travel scenes, Meituan and Didi's system directly forms the best scheduling mode, and the system automatically completes the decision-making process and assigns tasks to riders and drivers. This kind of consumer Internet relatively common scene, will gradually appear in the industrial Internet, enterprise business scene. In other words, big data began to move forward from the business digitization stage to the data intelligence stage.

Characteristics and Definitions of data Intelligence

From the development of big data in the previous section, we can see that data intelligence now corresponds to the stages of decision-making, optimization and business reshaping, which means that machines have reasoning capabilities; and these capabilities mean the gradual maturity of cognitive technologies such as natural language processing (NLP) and knowledge graph (Knowledge Graph), which is why NLP and knowledge graph have become hot spots in the market in 2018. Therefore, the new demand of data-driven decision-making and data-driven business development will inevitably lead to the rise of a number of data intelligence companies.

In the future, as the technology becomes more mature, big data will move from decision-making to the last link, that is, business reshaping. Many execution links can be realized by machines, but there are still many links that people need to participate in. Therefore, human-computer cooperation will usher in the rapid development, from artificial intelligence AI (Artificial Intelligence) to human intelligence enhanced IA (Intelligence Augmented).

So far, we try to make a definition of data intelligence: data intelligence takes data as the means of production, through the combination of large-scale data processing, data mining, machine learning, human-computer interaction, visualization and other technologies, extract, explore and acquire knowledge from a large amount of data to provide effective data intelligence support for people in decision-making to reduce or eliminate uncertainty.

The Development of big data

First of all, data intelligence needs to be provided by data, and data plays the role of core assets and means of production, so data governance is particularly important. What is data governance (Data Governance)? We often hear the word corporate governance, which mainly solves several problems in economics:

How do ownership and management rights be separated?

How do company owners authorize and supervise professional managers scientifically?

Accordingly, data governance has to solve several similar problems:

What are the data (assets)?

How to separate the ownership of data from the right of use?

How do data asset owners scientifically authorize and supervise data users?

All the means of data intelligence are actually solving the above problems. I will describe data governance in detail in the second part of this series.

At the same time, we know that the difference between the rich and the poor lies in their attitude towards wealth. The rich are more likely to treat wealth from the perspective of asset appreciation, thinking about how to create more assets and let them continue to grow in value; the poor tend to look at wealth from the perspective of consumption, and most of the money earned is spent on consumption. So in the era of data intelligence, if we want to be a "rich man", we need to consider how to make the data more valuable and how to find other partners to jointly create value, but the data is different from other assets. it has the nature of replicability and uncertainty, which requires us to solve the problem of data security, that is, the secure computing technology that is more concerned in the industry at present. I will elaborate on this in the third part of this series.

Another point we need to pay attention to is that because of its 4V characteristics, especially its large quantity and variety, big data sometimes makes us doubt its aggregation or results, although some can be judged by common sense or intuition. But there's always something unspeakable. This requires a quality assurance system to allow us to have a complete inspection process for each link of the data from generation to final. the fourth part of this series will describe the quality assurance system in detail.

To sum up here, the technical system of data intelligence needs to include at least three aspects:

Data governance system

Data quality assurance system

Data security computing system

Conclusion

As an important and exciting stage in big data era, data intelligence has both opportunities and challenges. As the beginning of this series, this article gives an overall overview of the topic, and the specific content will be carried out step by step. I hope it will be helpful to all of you.

Outside the country

The article was conceived on July 24, 2019, and suddenly found that this figure was very appropriate. 724 is the attitude and commitment of service in many industries, indicating that services are provided 24 hours a day a week. In the era of data intelligence, a push's products and services must be online all day, seven days a week!

We have been deeply engaged in the field of developer services, and based on message push, we have developed a series of products for APP development and operation, such as "user profile", "application statistics" and "one-button authentication", to build a new developer ecology. At the same time, Getui continues to broaden the service boundaries with data intelligence as the core, and uses innovative technology to provide customized big data solutions for various vertical areas such as mobile Internet, brand marketing, financial risk control, smart city and public service. In the future, a push hopes to use the power of data and technology to build a win-win ecology of data intelligence with more industries!

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