In recent years, with the vigorous development of big data industry, enterprises and governments have re-recognized the value of their data assets. But unfortunately, the data itself cannot directly generate value. When we want to use data to generate value, many problems will be exposed, such as the lack of data standards, unclear data sources, and lack of supervision over data quality. This requires us to have a unified data standard and good data quality, in order to form the basis for realizing data value. And data governance is precisely to ensure the existence of this foundation.
The International Data Management Association (DAMA) defines data governance as a collection of activities that exercise power and control over data asset management. It is a management system, including organizations, systems, processes and tools.
In the practical application of domestic enterprises, data governance and data management are generally considered comprehensively, and it is considered that data governance is a series of collectivization work that takes data as organizational assets, including the process of comprehensively combing, constructing and continuously improving the organization's data model, data architecture, data quality, data security and data life cycle from multiple dimensions such as organizational structure, management system, operation specification, information technology application and performance evaluation support.
When it comes to data governance, no industry can rely on it more than the financial industry, and almost all links are closely related to data. Bank informatization has developed for more than 30 years, and the early data are basically by-products of transactions and are rarely used. In recent years, commercial banks have gradually begun to use data for more accurate customer marketing, risk management and operational optimization. However, this process is not smooth sailing, and many problems such as imperfect data management system, incomplete statistical data and scattered data distribution are obstacles to the further digital transformation of the banking industry. It is imperative for the banking industry to strengthen data governance. Only by doing a good job in data governance can we sublimate from data to value and truly improve the management level and market competitiveness of banks.
Data governance is the need of bank operation security.
Data is already one of the important assets of banks, and banks need to keep their own and customers' information safely. All kinds of information involving trade secrets and sensitive data are at risk of being violated, illegally used or information leaked in the process of processing and using, which will bring immeasurable losses to banks. In a good data governance environment, the management and use of data can be standardized to better adapt to the uncertain factors in business processes.
Data governance is the need of bank risk management and control
With the development and application of Fintech, commercial banks use technologies such as big data, data mining, machine learning, anti-fraud and blockchain to comprehensively assess risks. But all these depend on the data in the data model being well used. The consistency and integrity of data can ensure the good operation of bank risk management and control, and effectively manage and reduce risks.
Data governance is the need of bank business innovation,
Banks have always been labeled as "traditional". With the intensification of market competition, they are facing great challenges in terms of customers, products, channels and marketing. In the big data environment, banks need to mine and analyze historical and existing business data, and launch various innovative businesses on the basis of traditional business operations to improve customer experience and enhance the competitiveness of banks.
Data governance is a policy and regulatory requirement.
2065438+On May 2, 20081,China Banking Regulatory Commission issued the Guidelines on Data Governance of Banking Financial Institutions, which standardized the data management activities of banking financial institutions from the aspects of data governance structure, data management, data quality control, data value realization and supervision and management. This also indicates that banks have fully entered the era of data governance. However, at the end of 2065438+2009, Anhui Fengyang Rural Commercial Bank was punished by the China Banking Regulatory Commission for "failing to effectively carry out data governance as required, with serious defects in data governance and serious violation of prudent business rules". It also reflects the problem that the bank data governance system needs to be improved urgently.
In recent years, banks have also promoted data governance to the strategic level of the whole bank and carried out a series of work.
In 20 14, China Construction Bank renamed the information center as the data management department, as the first-level management department of the Head Office, which took the lead in promoting the data management and application capacity building of the whole bank, and was responsible for formulating enterprise-level data specifications, coordinating the management of internal and external data resources, and realizing information sharing; Manage the data requirements of the Group as a whole, provide data services for institutions within the Group, and promote the application of big data in the whole bank.
2065438+In March 2008, Nanjing Bank formally established the Digital Banking Management Department to take the lead in data management and promote the digital transformation of the whole bank.
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However, according to the Research Report on Financial Technology Development of Small and Medium-sized Banks (20 19), 9 1% of small and medium-sized banks have not yet established a sound and effective data governance system, which urgently requires banks to conduct comprehensive data governance. However, at present, the data governance of China's banking financial industry is still in the development stage, and it faces great problems in terms of system, data, technology and talents. Especially in terms of talents, there is a lack of professional and systematic data management and data analysis talents.
After five years of research and development and three years of training practice, CDA data analysts have launched the "Training Camp for Financial Digital Transformation Talents". On the basis of the original CDA certification system, they highlight the characteristics of data application in the financial industry, and at the same time integrate into internationally renowned enterprise architecture Togaf, data management and governance system DMBOK and IT governance COBIT certification system to cultivate students' theoretical framework and practical ability of financial data application, provide personal digital transformation solutions for financial practitioners, and transform them into digital empowerment within the organization.
In this course, you can get:
I. Planning and management of data assets
The digital transformation of enterprises is to dig deep into the value of data as a means to assist business process reengineering and improve the ability of enterprises to cope with change. It is necessary for enterprises to formulate a clear digital strategy and continuously improve their data asset management capabilities. Data products are divided into five categories: data model, data quality, data tools, data applications and data algorithms. Among them, data application products are the products of complex labor used for business process optimization, and their labor objects are the original data accumulated with business operation and relatively primary data products obtained from the outside. From the operational level, the enterprise digital strategy is equivalent to the data product portfolio strategy, and it is necessary to formulate the data application plan according to the business strategic objectives of the enterprise, and then determine the data product portfolio; The purpose of data asset management is to transform data into data application products in the most economical way; The data center is the processing factory of data application products, which interacts with the AI center and provides input for the business; Data governance is the quality assurance system of data application products, which ultimately serves business index analysis and data mining model application.
Second, intelligent customer base operation.
McKinsey, a world-renowned management consulting firm, reported that in 2020, China is expected to become the second largest retail banking market in the world after the United States, and retailers will dominate the world under the new situation. With the maturity and in-depth application of mobile internet technology, big data technology, artificial intelligence technology and blockchain technology, the future banks will present "five changes": entrance scene, digital operation, intelligent risk control, cross-border talents and universal services.
Therefore, the goal of this course is to realize the operation and management of smart customers, from how to find problems to how to solve them.
The content of this course is mainly divided into three levels: Tao, technique and qi. Theory, implementation and tools.
1, theoretical chapter, which mainly introduces the evolution from the old 4P theory to the new 4P theory, as well as the theories and concepts of digital operation and digital marketing and their practice in banking;
2. In the chapter of implementation, three strategies are mainly introduced: one is the operation monitoring of customer base based on NES; Second, digital marketing system (model, label and CRM system, etc.). ); Third, digital closed-loop marketing.
3. Tools, focusing on the application of specific algorithms in digital operation through cases.
Firstly, the clustering algorithm and its application in customer segmentation are introduced.
The second is to introduce collaborative filtering algorithm and its application in product recommendation;
The third is to introduce the application of community discovery and its trading circle in bank marketing.
Third, intelligent credit risk control.
This course takes the emerging consumer finance and internet finance in China as the main scenes, introduces the data application of consumer finance in credit risk management before, during and after lending, and tries to provide students with comprehensive data-driven risk management knowledge on the basis of in-depth actual scenes. The course focuses on three credit scenarios: pre-lending, mid-lending and post-lending, and introduces the application of data analysis and data mining in the form of explanations and cases by introducing the relevant business background and combining the actual risk control needs.
The first part focuses on the common consumer loan product elements, risk points, the basic framework of intelligent automatic approval, data-driven loan access, the formulation of rules, the construction of credit scorecard for application and credit pricing based on risk differentiation. The second part introduces the management of performance customers, including the construction of behavior scoring model and the formulation of corresponding quota strategy. The third part introduces the establishment of the collection scorecard and the formulation of the collection strategy in the collection process.
Fourth, intelligent operation risk control.
In recent years, with the frequent occurrence of financial risk control cases and the continuous tightening of regulatory policies, it has become a top priority for many banks and other financial institutions to improve their risk control capabilities to reduce internal and external risks. As one of the three major risks of the New Basel Accord, operational risks include common anti-fraud, anti-money laundering and anti-fraud scenarios. In view of this "three evils" scenario, it has become an important starting point for many financial institutions to comprehensively use various means to manage and prevent risks.
In the two-day course, the concept of operational risk and common sub-scenarios will be analyzed first, so that students can have a clear and complete understanding of operational risk. Then it introduces what kind of prevention and control system should be established to deal with operational risks, and analyzes it from the perspectives of system, talents, data and technology. Especially in the technical means, it will focus on several major problems and solutions faced by machine learning modeling of operational risk. In the one-and-a-half-day practical case session, three typical Python modeling cases of anti-credit card fraud, anti-money laundering and anti-marketing bonus hunter were arranged, aiming at strengthening the general process of risk control modeling, and effectively improving the practical ability of students in risk control modeling by covering the technical difficulties of modeling with cases.
Verb (abbreviation for verb) data and artificial intelligence intermediate station
As the financial industry is entering the fourth major development stage-the digital age, it brings development opportunities to financial institutions, but it is also accompanied by severe challenges. How to solve the problem of data islands and the difficulty of combining new applications with old systems? Is the existing IT capability insufficient to support the rapid change of business? The data call methods are diverse and the standards are not uniform, and the quality is poor? The problem that the digitization ability of data resources has not been released is a common problem faced by enterprises. Data integration and data asset management are one of the effective ways to solve these problems.
This course will start from four aspects: how to effectively integrate data, the introduction of various data platforms, how to effectively manage data, the management of data assets and the construction of data centers. Help enterprises to quickly establish data integration systems between systems in the process of digitalization, and support the rapid realization of user data integration applications; Provide a perfect data management system and an effective data integration scheme to support the mining, analysis and application of upper data; Provide effective data support for enterprise's development strategy and business innovation, gain insight into enterprise's operating conditions and market trends, improve the flexibility of new business, and create an agile data application environment.