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The future of data intelligence is the era of ubiquitous big data without mentioning big data

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Looking back on the past decade , We have witnessed the birth of big data from concept to industry landing , Gradually developed to the era of data intelligence based on platform driven decision making in data . As a product of the Post Internet era , Data intelligence will be the core of a long development stage in the future .

Daily interaction ( A push ) As one of the representative enterprises of data intelligence industry , This year ushered in the first decade . During this decade , A tweet starts with a message push , With the help of technology, we can broaden the business scope with data intelligence as the core , Has grown into a family in A Data intelligence enterprises listed on the stock market .

but “ Data intelligence ” The word , Still relatively unfamiliar to the public . How do senior practitioners in the data industry define “ Data intelligence ”? In the past ten years , What kind of development has getui gone through ? forthcoming “ Data intelligence ” The new decade , What kind of role will a tweet play ?

On the occasion of the 10th Anniversary , A push CTO Ye Xinjiang in response to the above questions , And SegmentFault Think or not to start a dialogue .
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01

What is meant by “ Data intelligence ”?

With the coming of mobile Internet Era , Great changes have taken place in the dissemination and presentation of Internet content , It also changes the magnitude and shape of the data , Data services have also changed . The traditional data service is only for the data itself , And data intelligence as a kind of data from 、 Driving force and algorithm , It has the ability of enabling enterprises to manage and make decisions .

Q1、 Data intelligence is a hot topic this year , Can you tell me something about 「 Data intelligence 」 and 「 Data center 」 The definition and understanding of ?

At present, data economy is a very important development direction at the national level , But at the same time, it also extends a problem to be solved —— How to take the data of various industries as production factors , Play a further role through data governance .

For invisible data , There needs to be something that can be managed sensibly , Data platform is a product or platform for data governance , Data intelligence belongs to a broader category , It uses data as the means of production , By combining large-scale data processing 、 data mining 、 machine learning 、 human-computer interaction 、 visualization 、 Cloud computing and other technologies , Extract from a lot of data 、 Explore 、 Acquire knowledge , Provide effective support for people in making decisions , Solutions to reduce or eliminate uncertainty .

These two words look very similar , But it's different dimensions . In terms of our company , The data center is more of a product , It reflects our company's data capabilities ; And data intelligence involves more technology , Integrating more industry knowledge , Middle ground is one of the most important tools . A figurative metaphor , Data platform or platform is the operating system of a computer , And data intelligence is a combination of business , Using this computer , Using a variety of techniques APP Development , And continue to optimize the process .

Q2、 What do you think of in the data 「 Industry barriers 」 and 「 technology barrier 」 What are the differences ? As a successful listed professional data intelligence service company , What's your biggest advantage in this field ?

At present, there are many Taiwan products in the market , According to our observation , Most of the focus is still on data governance capabilities , But it may just be data centric “ First step ”.

For the data center products , Technical barriers may not be very high . The technology that will be applied by Zhongtai , Although some functions can be realized by directly using open source products , But there are also many standards or requirements , Like security 、 The real time 、 Visualization, etc , Need professional technology to customize the implementation .

Of course, at the data level , The technical architecture adopted is different 、 Different ideas of product design can be different , For example, whether it is easy to use 、 Easy to understand 、 Whether the demand for resources is economic or not .

If you want to solve business problems through the data platform , Achieve cost reduction and efficiency improvement or discover new business models , This is not a problem that data governance can solve , Deep understanding of the industry is needed , It's the so-called industry barrier .

The difference between getui and other big data companies , It is reflected in how to mine the value of data .

The first product of a push is a message push service , This is a business closely related to data . To support this business , Realize the fast response of message push in the mass data volume , Getui has been trying to create a business for the enterprise + Collaborative closed loop of data platform . This also laid a good foundation for the company to launch data service products .

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At the data service level , Individual push is both a service provider and a consumer , This is our biggest advantage in this field .

Q3、 Whether there is a set of standardized judgment system in the field of data service ? How to evaluate the service ability and technical level ?

In the field of data services, there is no set of industry standards proposed by international or national authorities . The reason is , It's because data services are very different in different industries , Compared with the general technical system , With stronger service attributes .

For customers , There are several dimensions to consider when selecting data services :

●  Whether the platform can be quickly deployed ;

●  Is the deployment reasonable ;

●  Whether the business can be improved quickly with the help of data services ; For example, whether we can quickly develop new business requirements , Whether the coordination between the internal roles can be well completed .

These three criteria will be the basis for customers to choose services . But after the selection , How should the service provider complete 、 How to realize the customer's demand , Because there is no clear way to define and judge , Customers need to make comprehensive consideration in the selection of models , And feel the service ability of the platform in the subsequent actual business scenarios .

This situation is also related to market demand . At present, the demands put forward by domestic enterprises are relatively low “ Customized ”, Plus the differences between different industries , As a result, most of the domestic data service companies provide vertical data service capabilities in combination with the industry .

02

Every step of development , It's all about the needs of the industry

China's digital economy has just begun , The capabilities of data intelligence services are also iterating 、 Development . Artificial intelligence 、 Blockchain 、 Knowledge map 、 Visualization and other emerging technologies , Can be used as the underlying technical support to enable data intelligent services , So that our data services have more powerful capabilities , Generate more value .

Q1、 The development of getui has gone through many stages , Technology is also iterating and evolving . Can you share the business logic focus and technology R & D focus at different stages ?

A tweet is our message push product , It was done earlier and more mature . But actually our company has been developing for ten years , The business area has long been more than just message pushing .

The first stage of the company's development is 2010 Year to 2013 year . At that time, our core team was mainly polishing the ability to push messages , It's a large-scale communication system + Internet search system , This is a relatively vertical but highly technical field .

2014 - 2018 year , Companies enter the data value of “ Nuggets ” And “ practice ” Stage . While constantly improving push capabilities , We found that the system produced a lot of data , How to make these data play value has become a problem we need to think about and solve . It's also from this stage , Getui is committed to the research and development of big data system .

Push products generate dozens of messages a day T The data of , How to store 、 cleaning 、 modeling , How to generate commercial value in business scenarios , This is our research and development focus at this stage . After several years of business training and scene practice , We will be able to precipitate in the field of data services and know how( knowledge 、 Experience 、 technological process ) , Become a product that can serve more industries , It can be said that the company has officially entered the stage of data intelligence .

2019 year 3 month , The success of daily interaction lies in A Stock gem listing , It also caused a new trend in the field of data intelligence .

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In the course of this year's epidemic , The ability of individual push data intelligence has been verified to a certain extent . During the epidemic , We got in touch with Academician Li Lanjuan , Cooperate with Academician Li Lanjuan to form a joint team . Through big data technology , The joint team has studied and judged the development trend of the epidemic situation , Help the epidemic prevention and control department to find the key areas of work 、 Key people and key scenes . When production is gradually and orderly resumed throughout the country , Individual tweets also give full play to the ability of big data and participate in it , For the joint prevention and control of epidemic situation in many provinces and the guarantee of people's livelihood 、 The orderly planning of resumption of work and production provides a reference for decision making .

Of course , The data intelligence service of individual push is still in continuous iterative development . With artificial intelligence 、 Blockchain 、 Knowledge map 、 And so on , This data based service will also generate more value .

Q2、 Getui is committed to creating “ Daily governance platform ”, What's the target direction ? What's the difference between them and the general products in the middle of Taiwan ?

「 Daily governance platform 」 In fact, it is also the concept of data center , But the data services we provide are mainly applied in the field of data governance , So it's more accurate to name it the governance platform .

This platform also has a refined name —— DMP.D It represents data (DATA),M On behalf of machine (MACHINE), and P It represents people (PEOPLE) And specialty (PRO). These keywords also represent the positioning of our platform , That is, data as the underlying information , Fast and professional service in specific business .

The development of digital economy in China has just begun , according to the understanding of , Many enterprises don't know how to digitize 、 Intelligent transformation , There is also a lack of professional data professionals in the industry .

therefore , Our goal is different from the traditional data center . We want to really push know how It's the ability to settle down , Form services or platform products that customers can reuse . The reason why we want to build such a platform , It is also based on the actual needs of users .

Q3、 Which company is the first customer of the first push ?

The first customer of a push message push service is Sina Weibo . Weibo was very popular at that time , It has hundreds of millions of users , Message push is an indispensable function for them .

2011 year , We took the initiative to contact the person in charge of microblog related business , It can provide professional message push service , Better results at lower costs , That's why we can move them .

The reason why the first customer dares to challenge “ Top of the line ” The difficulty of , On the one hand, the technical team has a solid technical foundation and industry foundation , On the other hand, it's just mentioned , The data and technical capabilities that we precipitate in the process of supporting our own business .

There is such a big customer with clear requirements , So that our technical service capabilities can be iterated more efficiently , Greatly accelerated the speed of technological progress .

Take the challenge and do it “ The acme ”, It's our company's engineer culture , We feel very proud of it .

Q4、 What are the current customer groups , What areas are involved ?

As data capabilities grow , And a deeper understanding of the industry , Our current customer service has expanded to four areas —— Internet 、 Public service 、 Risk control services and brand marketing services .

Take the financial sector for example , Many financial institutions have their own internal App, It's about pushing 、 Statistics 、 Analysis and other very complex data and information processing scenarios . We helped them build an integrated message platform center , Solve the problem of decentralized message processing , Improved workflow . After deep communication , We found that data intelligence service is a very serious business scenario in the financial industry , To this end, we are also considering the development of a governance platform specifically for the financial industry .

Q5、 For data companies , Data security is the red line . How to guarantee data security ?

Since the establishment of a push team , It pays great attention to the protection of data security . As the question says , Data security is a red line for data companies , Getui always believes that protecting user data privacy is an industry standard that enterprises must adhere to and defend 、 Moral bottom line , And always be in awe of data security .

While strictly implementing compliance and regulatory requirements , Advocate industry self-discipline , Actively combine the power of industry partners , Jointly promote the healthy development of the industry . This year, 8 month , It was drafted by a Tui 《 Information security technology, mobile Internet applications (App)SDK Safety guidelines 》 National standard project , It has been officially approved by the National Information Security Standardization Technical Committee , This will help SDK In the development industry 、 operating 、 Information processing 、 Safety management and other links are moving towards a more standardized direction .

In this year's network attack and defense drill in Zhejiang Province , A push in 395 There are no external security companies among the candidates , Only by the company's own security capabilities , Then we got the first 14 Name of grades , Still proud .

03

The future of big data , It's an era when big data is not mentioned, but data is everywhere ,“ The biggest thing I don't realize is that I don't exist ”

As the size of the data industry grows larger , The ecological division of labor will be more and more detailed in the future , Data service will be more and more detailed . And with AI 、 Cloud computing 、 The development of Internet of things and other technologies , Will big data be the same as the Internet , It's no longer a concept that people talk about alone , It's an infrastructure like hydropower , Serving all walks of life ?

Q1、 With the development of society , In the future, data will coexist in many forms . What changes do you think will happen in the field of big data in the next few years ? What factors will drive these changes ? What's your ecological layout like ?

With 5G Maturity , The development of the data field will usher in a qualitative change from the quantitative change of the foundation , Become a social infrastructure like hydropower .

In recent years, people seldom discuss the concept of the Internet alone , One of the factors , I think the Internet is no longer new , At this stage, the development of technology is based on but higher than the Internet , The development after big data will certainly be the same , The future of data intelligence , Although based on big data , But we won't discuss the concept of big data alone .

From a technical point of view , The development of big data industry, the storage of massive data 、 Handle 、 The demand for mining is bound to be higher and higher , In the future, there may be several giants , The industry will be more clearly subdivided . This is a trend that we have been able to observe .

As the size of the data industry grows larger , Ecological division of labor will be more and more clear in the future , Data services will become more and more refined . We build a platform to push daily statistics , I hope to participate in the ecological construction , Even create a new ecology around the platform .

Q2、 There is an opinion in the industry recently :“ Cloud native architecture is the data management methodology in the era of cloud services ”, From a technical point of view , How do you judge the relationship between cloud and data intelligence ?

Different people will certainly have different starting points when they evaluate the same thing , It seems that this idea should be put forward by cloud service providers ( laugh ). Very clever , I was talking to Geely about this a while ago , They just moved out of a cloud vendor recently .

For businesses , Can't get to the cloud 、 Public cloud or private cloud 、 There are several dimensions to consider when choosing a cloud vendor : The cost is too much 、 Whether it is autonomous and controllable 、 Whether it is convenient to switch when the business requirements or service requirements are not met .

Take a tweet, for example , We're not going to put data on a third-party cloud right now , But from the scheduling of resources 、 Cost control and other aspects , The cloud system is already a recognized standard answer , So we will also use cloud computing related technologies , Like container clouds 、 Virtualization, etc .

Q3、 The Internet of things 、AI、 The emergence of new technologies such as machine learning , Is it an opportunity or a challenge for the industry ? Is there any forward-looking exploration on the development of individual push response technology ?

The emergence of new technology , It's both an opportunity and a challenge for the industry . Every time new technology comes along , All of these provide the possibility for human to liberate or improve productivity .

But some technologies are too “ new ”, We haven't found the scene of exerting our ability yet , There is no way to give play to the actual social value , Let the enterprise's input and output is not proportional .

Getui also established the data Intelligence Research Institute , The current research direction is to explore how to better serve the specific business . For example, our exploration of the direction of Internet of things Technology , I want to extend my push technology to TV sets 、 Cars and other scenes ; The exploration of machine learning and blockchain is for the research of some business models .

Technology is for the future , But the needs of users are now . For us , At present, the focus is still on how to use technology to quickly iterate service capabilities , Empowering users and industries .


Review the development timeline of big data industry and getui , It's not hard to find that in the past ten years since the establishment of a push , It happens to be a decade when big data has changed from an auxiliary tool to a core engine leading the development direction .

With the development of big data industry , The country is vigorously developing the digital economy , Enterprises are ushering in a wave of digitalization with the help of data intelligence 、 The new wave of intelligent transformation . In this wave , Start “ Governing number ” What kind of role will a tweet play ? We'll see .

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