Liu Xianrong: Current situation, practice and expectation of data asset management in CCB

Source: Internet
Author: User
Keywords Cloud storage private cloud Intel cloud applications cloud storage cloud applications
Tags analysis application application innovation applications asset asset management banking basic

2014 Zhongguancun Large Data day on December 11, 2014 in Zhongguancun, the General Assembly to "aggregate data assets, promote industrial innovation" as the theme, to explore data asset management and transformation, large data depth technology and industry data application innovation and ecological system construction and so on key issues. The Conference also carries on the question of the demand and practice of the departments in charge of the government, the finance, the operators and so on to realize the path of transformation and industry innovation through the management and operation of data assets.

In the afternoon financial @big Data Forum, China Construction Bank CIO Liu Xianrong brought the "CCB Data asset Management status, practice and expectations" keynote speech. Its content has three parts, the first one is how the people who play the data in the bank view the big data this matter. The second is now that the bank is a strategic asset, and what has been done to manage the data assets of CCB. The third bank has any expectation of big data.

The following is the full text of his speech:

Liu Xianrong: To introduce to you today, I actually should be data management practitioners, because in many cases, director Wang also mentioned that the bank's data feel that the bank itself is a conservative industry, the number of banks are said to be in accordance with the secret State, the bank data is absolutely the most secretive range. Many people in the industry who play with the data may feel that the bank's data may have a lot of high value density data, can not be shared. I would like to spend the afternoon with you to share our practices and thoughts and our expectations.

Banks in recent three years, in fact, in the application of data should be said to have a very large ideological or cognitive views. The content of the afternoon exchange is three parts, the first one is how we regard the big data as the people who play the data in the bank. The second is to give you a simple look, now say that the bank is a strategic asset, data assets we do in the management of what work. The third one we can look at what we expect from the big data, and that's the direction we're going to work on.

In fact, the construction Bank we hope to use a few years to have a large data target, this goal is my own initiative to put forward the background, in many ways this is also our response to the challenge. The two years of internet finance are not comparable to banks in volume, but many have hit the banks ' penetration business. I often say an example, it is like the era of the Great Revolution. The Great Revolution era, now a lot of internet companies with advanced means, we are holding a broadsword, called a turtle or whatever, then how do we take the initiative to meet the challenge.

We can see that we recognize the traditional sense that we think we are a reporting agency, this is the bank data, many years ago, when I went to CCB we call Statistics Department, Statistical Department Dry report. What is the stage now? Now in the analytical phase, in fact we are in many areas, we have a lot of day-to-day banking business, at the back end there are many analysis model support. Like your credit rating, a lot of your products. Later we may be to the information-oriented institutions, that is, data-driven, which is our goal, the goal of a direction. Our perception of data assets, and how we view data as a problem. As you can see in the middle of a bank's data assets, we are a part of the middle, a panoramic data map, or a data break. The customer data, product data, transaction data, employees, institutions and channel data, to integrate them perfectly together, to form a unified customer, or to say a data map. Our goal has three directions of value, the first is the decision-making force, the second is insight, the third is the supply capacity. In fact, if we go deeper into the category, our big Data production target is the first we want to connect with our customers deeper and we want to stick together with our customers. Second, we really use data as a tool and a means, even as a technology to help us and customers to join together, this is our knowledge of the data. The left can be seen, actually this is what we are talking about a large number of concepts, the bank data volume is relatively large, originally we are report level, now we are quasi, time, reality, behind the diversity of data, this very good understanding.

The challenge of the future, we see this figure, the lower left corner of our bank is the current capacity of the section, structured static mass of data. But our challenge comes from the right, the dynamic mass of data that many internet finance companies use to attack with Dynamic data. We have a lot of data, we call banks, phone bank data, mobile phone and internet bank behavior data, and even more video data, will gradually into the bank's data management perspective. We often talk about the application of large data, we now speak of the data asset awareness is to hope it as a tool and means to help us better understand the customer, the most essential is to help us to provide services to customers. You can see that there are actually a few challenges.

The first customer identity data, how to know your customer is my customer, when can provide services. The second you are in what position, in fact, director Wang also said, this data is very sensitive, many situations are the need for customers to know the right or even the customer's permission, large data times we most emphasis on the ownership of the data. The third is insight, the so-called insight is that we have to know in real time what customers need services. For example, a customer in the online bank recently often browse our wealth management products, after a day after the customer to the counter, how do we know that the customer to the counter is to buy money? The customer's own internal traditional data to merge it together, only to know when the customer calls, to the online banking, to the dot when exactly what to do, and then can make products and services in advance preparation. The last one is the first time, is now the internet age, and even the most concerned about the mobile internet era, is how the first time the corresponding customer demand, even in the customer demand he found before we find his needs.

This is our understanding of large data. The second part can be seen, now that we are engaged in data management, to make big data, banks in the field of data management, what do we do? Simple with you all over again, the first is the capacity of the data system, the so-called data capability system is the bank to establish what data management capabilities, can adapt to the requirements of large data times, to achieve large data industry goals. In fact, the work of three latitude, these three jobs we play the data know that each job is not good to do, are not in the imagination to hold the data can go to war. The real work is at the back end, how to integrate a large number of data together, the first is the most fine, is the large data era of self collection, this is the traditional sense of the large difference in data. Traditionally we have a minimalist principle, the original is not automated process, this time in order to ensure the best process experience, we have to collect information and input content less the better, to ensure the best experience. But now with the automation of the product channels, including new facilities and the like, we find a lot of data we need to do a lot of work. The second level is data management, and we in this room who are really doing the data know that this is the most painful phase. is how to put each path, each way, or even different channels to collect data, we put it together, the so-called integration is to generate a unified map, if the big data insights this is essential, a lot of work is to do data integration work. To the right is to reflect our value, I will have large data models, through large data insights, so that the value, so the last is the data application model, has the traditional meaning, now includes many new means. These three combined together are what we say we are in the data inside our true ability system.

There's a lot of work here. You see, this one is a bit complicated, which is control, collection, integration and application. We want to integrate them all together, what is the application of the integrated data in different banking fields? This is the gap between the people we do data management and the people who do business, what is this gap? For example, when the meeting will say you do data management, you tell me what data management can help me? Before we did data management, what was my first reflection? My first reflection is that I have a lot of data, you tell me what you want to do, I solve your problem through data, this is the traditional concept of playing data. But now we're talking big data, or in the field of data assets, we usually build a business framework model that will take a lot of business areas of the bank, such as customer insights, what to do, risk insights, what to do, what to do, what to do, and where to break down, it will form a very broad, Or a very complex business model. What does the data in this model do? This is doing data bank, data management, data usage really to think about. In the past is to provide you with the hosting and decision-making services, our data is really going to do is to help the business unit to really optimize his process, really find his customers, really help him how to provide better services to customers, this is called operational data, this is the next goal of data asset management.

Application System of data assets you see, in fact it needs a series of environment, especially for large banks, this is not a guerrilla, guerrillas often get what data to use. But as a system from the soft and hard environment, from the delivery of evaluation, demand and management, and so on, we have to establish a framework of the structure out.

We will find that in order to achieve this goal we have a lot of ability, the first is the data management capabilities, the past, banks are also very painful, many years ago, the CBRC data, People's bank data, including our internal management data, we are tired of coping. This is the scene that we used to face. You provide a data user does not believe that regulatory challenges you, your management also to challenge you, your data why this? The beginning is always unable to find the data, is to find a scene at all do not know where the data, and then everywhere to find, some are handmade, some are to do tests out, or simulated, often inconsistent. The real goal of data management, then, is to have a good ecosystem of data, an ecosystem that is within the unit and should be the case for a whole country. The morning of the exchange why the emphasis on the data ecological environment, in this environment everyone must be a contributor, everyone is an application. This environment found that for each enterprise, for the whole country we will form a relatively good ecological environment. This can share with you, in the data management in fact there are many institutions they have established a better framework model, the framework model is not good or bad, only whether the application. So in the enterprise can find a, this is our definition of the analysis model, we define the data asset management system. The system may not be the same for big banks or small banks, but for different data cultures. The back is the data mining ability, has the data, has the data conformity, really produces the value and the function. We want to find our real rules from massive numbers, which are called data mining, or large data applications, and many companies are doing it.

I give a few examples, such as profitability analysis. The whole point of view of data mining is not quite the same in many places, we also do a lot of work inside. If it is summed up the idea is this, do not expect data mining first to do a very large system, data mining is the specific scene, first find very specific small applications. For example, if you know that big data is used in banks, you can come up with a huge system, the first to communicate with the Bank of America they told us one example, the U.S. bank's data application to make people work first, or let you know the data useful, you know what is the case? Very simple case of transporting money by looking at a lot of American ATM machines, day of the increase in the amount of money, the amount of money in the design, such as in New York every day of the banknote printing machine should be what path, or even fine to the banknote can only turn right, at the traffic lights this car must turn right, and finally through the data found that this is the best path, Found that a lot of data mining is the case, this is the framework of our design, this framework is an (English) framework, the real time is to find the details, find the real environment.

And finally, we say that the data is really worth it, in fact, we do data people like me think I am a technical system, do their own data collection and integration, mining, but we have to remember that the data really play a role to use, we must put the data, basic data, data mining model, or To be embedded together is useful. For example, I can have a process that can help to find the user, the most important how to embed data into the process, data application capability is the core of data value for the bank. Data products, basic data can be a little less, but if you want to build a system, first of all to the data and business real integration. including how to do data integration and so on, you see actually really do data management we first talk about large data has a lot of very perfect scenario, but from the whole data asset management, data asset practice, most of the work is in two aspects. The first is basic data management, and the second is how to apply the basic data to the process, two things. Every bank has a different approach to business.

The third is to look to the future, we put forward a goal called the establishment of large data lines, that big data line what do things? Because we have a lot of internal and external challenges, more is that we have a lot of opportunities. Because we suddenly find that the external environment we are facing has changed, and the internal management level has changed. First we positioned a large data application direction, and we define internally what is the future bank? We hope to be a smart bank, in different areas have their own different levels of intelligence, such as European customer intelligence, risk, operational intelligence, each domain has a different understanding. For example in customer intelligence we want to achieve customer precision marketing, what do you mean? It was more than 10 years ago, in order to find a book, to go to a bookstore when you bought a book. Maybe from the first floor to the four floor, the new book may not look at all, but people now Amazon, when, and so on, I recently bought a book is very simple, I buy a few years in a row, I want the book they can provide relatively accurate to me. In a few days I saw the basic of the field of the new book almost, the bank is the same. There are also cross marketing, customer experience, customer service and so on. We know that the recent experience of a very hot is called the Wisdom Bank, many banks are doing this thing, we CCB in Shenzhen also engaged in a smart bank, interested can have time to Shenzhen to experience it.

This is an overview of the value and application of large data, and this is something that we can simply look at, which is the directionality of our entire large data. For example how to define the data strategy, in fact, we will find very important points. Data classification, this is very core. Data collection principles, data security and quality management, etc. Two of the most important characteristics of the data, the first is the quality of data, the second is the activity of data, which is related to data acquisition. Large data methods, as well as application architecture, including different capabilities, large data management, analysis of objects, including large data technology and some analysis of mining models and so on. In short, according to the needs of different users we provide different data applications and service capabilities, large data really want to see a few cases, customer map, real-time customer delivery platform, the bank's ecological environment. What do banks want most? In recent years we have summed up our bank's data, found the bank's data we say that in fact our value rate is very high, this is standing in the traditional perspective, but now we find that we understand the customer, the bank's data has a flaw, often to a link on the order truncated, the customer exactly bought what we do not know. Again for example, the data to the Alipay, support to the customer to transfer money to Alipay, the customer to pay treasure in the end what we do not know, we found that this is a natural flaw in the bank data. The future hope that through with the industry to establish a better ecological environment, first of all, to ensure the security of data, the second guarantee the privacy of data, these two conditions, the bank data will never break through these two points, this is now to make big data when the two bottom line, these two bottom line no matter how many good data collection principles, The number of good data acquisition direction, but the security of information privacy to ensure. This is based on the possibility of establishing a better data ecosystem, through which the banks and customers together, this tool is the data.

In fact, there are a few more important ideas, in the past, the data business, now how to use the data better to do business. For example, through data technology, which is similar to technology, we know which processes can be optimized through data, the traditional banking process is more complex, the data is rich to a certain degree, the data is applied to a certain degree, some processes can be optimized. For example, the big data really need to function, in fact, the three unification of banks is inevitable, unified data, platforms and services, the bank must be the internal and external data into an environment, in this environment to integrate, only together we can achieve the smart goal we put forward, which I share with you. We hope to have the opportunity to explore in different areas or different environments, how to make banks better, or to help us achieve our strategic objectives, to create more value for customers, this is our big data to play all the goals, thank you.

(Responsible editor: Mengyishan)

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