In recent years, as an extension of Internet finance, financial technology has been fully applied in the fields of payment, credit, insurance, and asset management. This has undoubtedly accumulated a large number of data assets for major financial institutions. However, with the slowdown of China's economic growth and the gradual fading of Internet traffic dividends, the characteristics and needs of customer groups are also evolving. At the same time, the new round of financial opening policy has also prompted regulators to put forward stricter requirements for the entire industry, which has led to a general increase in the operating costs of the financial industry. Therefore, in the new round of industrial transformation and digital transformation, how to make full use of these silent data assets, tap business growth points and improve organizational operation efficiency has become the key factor to achieve ultimate victory.
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As a pioneer in the digital wave, the financial industry has always been at the forefront of the times. In terms of human, material and financial resources, the financial industry continues to make huge investments, which provides sufficient impetus for the rapid development of the industry. In the field of informatization, some leading large and medium-sized financial institutions have become the vane of the whole industry. However, due to the limitations of business attributes and the historical background of enterprise development, there are still many financial institutions with various problems:
In the current business environment,dataChimneyseffectIt becomes significant, led toBusiness systems are siloedphenomenon. Data silos and information barriers between different business systems make it difficult to flow and share data within the enterprise. This situation poses many challenges to enterprises, including the inability to fully understand and analyze the overall operation status of the enterprise, and the difficulty of achieving efficient business collaboration and decision support. At the same time, companies may be missing out on some important business opportunities and competitive advantages due to a lack of integration and unified data management.
The low degree of report automation leads to a large number of manual report jobs, YesIt takes a lot of time and human resources。This not only increases the workload of employees, but also leads to errors and inaccuracies in data processing. For enterprises, this inefficient reporting method may hinder management's comprehensive understanding of the business situation, affecting the accuracy and timeliness of decision-making. Therefore, improving the degree of report automation has become an urgent problem for many enterprises to solve in order to achieve more efficient and accurate report generation and analysis.
A system with trivial requirements, homogeneous reports, and bloated requirementsis a relatively common problem. Due to the diversity and change of business needs, enterprises need to generate a large number of reports to support management and decision-making, but these reports are often too trivial and repetitive, lacking real value. In addition, the internal systems of enterprises often become bloated due to complex functions and large amounts of data, which makes it difficult to achieve efficient management and decision-making. This situation not only wastes the company's time and resources, but may also affect the competitiveness and development potential of the enterprise.
1.Foundation building phase
During the foundational phase, organizations should aim to create a unified data portal to achieve the goals of enterprise-wide data governance and security applications. By establishing a complete basic report application system, enterprises can effectively integrate and manage various data resources within the enterprise, and provide accurate and timely data support. At the same time, enterprises should pay attention to data security to ensure that sensitive information is properly protected. This stage of work lays a solid foundation for the company's data management, and also provides reliable support for subsequent data analysis and decision-making.
2.Initial application stage
In the initial application stage, enterprises should focus on realizing the autonomous use of management information. By establishing flexible data drill-through and slicing capabilities, enterprises have the freedom to select, correlate, and ingest data as needed. Through data visualization technology, complex business data is presented in the form of charts and reports, so that business users can intuitively understand and analyze data. As a result, business users can be more autonomous, improving work efficiency and accuracy through data exploration and decision support. The goal of the initial application phase is to enable users at all levels within the enterprise to make full use of data resources and maximize the value of data-driven decision-making.
Comprehensive application analysis
In the comprehensive application analysis stage, enterprises can segment and parse data from multiple perspectives, and build corresponding analysis models. In this way, enterprises can extract valuable information from the huge amount of data to provide reference opinions for business users. Specifically, we can slice and dice the data from different perspectives, such as time, region, industry, etc., in order to better understand the trends and patterns behind the data. At the same time, machine learning and other technologies can also be used to mine and improve data in order to better grasp the future trend of change. Ultimately, the results of the analysis are presented to business users to help them make more informed decisions.
Integrated Applications**
In the comprehensive application stage, a model is built by comprehensively considering different businesses to identify future business priorities. Through the analysis and modeling of historical data, enterprises can improve future trends and changes, and put forward corresponding business recommendations and strategic solutions. At the same time, in order to increase the credibility of the results, companies need to supplement and refine the model with new data to better reflect the current reality and trends. Through continuous optimization and improvement of the model, enterprises can more accurately improve future market trends and changes, and provide scientific basis and guidance for the development of enterprises. The goal of comprehensive application is to help enterprises better understand market dynamics and trends, adjust business strategies in a timely manner, and enhance their competitiveness and performance.
Intelligent application stage
The intelligent application phase is based on the collection of various internal and external data, and by performing data mining, enterprises can find potential patterns from seemingly unrelated data. These patterns can help companies gain insight into market trends, consumer behavior, and business operations, and provide targeted decision support and business insights. Driven by real-time data, businesses are able to quickly capture business opportunities, uncover new market needs and opportunities, and identify potential risks and challenges. Through real-time analysis and monitoring of data, enterprises can adjust business strategies in a timely manner and improve business flexibility and adaptability. The goal of the Intelligent Application phase is to leverage a data-driven approach to help companies better grasp business opportunities,** mitigate risks, and achieve continuous innovation and growth.
1.Integrate business data and manage data in a unified manner
Traditional informatization is mainly centered on processes and information records, with the purpose of serving the current business process. However, with the passage of time and the complexity of the business, many institutions were formed"One type of business, one IT system, one database"'s closed IT architecture. This has led to the emergence of data silos, that is, the data language in different IT systems is not uniform, the data cannot be connected, the same data needs to be entered repeatedly in different systems, and even the same data in different systems may be inconsistent.
In order to fundamentally solve this problem, we need to digitize the existing business objects, business processes and business rules, open up scattered data centers and various business systems across the country, and establish a unified data warehouse and data lake. Through front-end filling and entry and setting input data verification rules, the automation and standardization of data management are realized to ensure the integrity of data assets.
The benefits of this are manifold. First of all, by breaking through data silos, data between different systems can be exchanged and shared with each other, improving the value and utilization efficiency of data. Second, establish a unified data warehouse and data lake, which can centrally manage and store data and improve data security and reliability. In addition, automating and standardizing data management processes can reduce manual errors and increase productivity.
2.User-based data application system
The C-side emphasizes the focus on user experience, while the B-side also needs to pay attention to the user experience, although their essence is different, one is to serve users, and the other is to serve the enterprise. However, the user experience is consistent throughout the implementation. So how to ensure that the data application system meets the needs of users and provides a good user experience? First of all, we need to understand a basic concept, that is, what is a business? At the same time, from the perspective of data people, how can we build a data application system based on business needs?
thinkThe core component of business is the flow of people and information, which is the so-called information chain and management chain. There are many key nodes in this chain. Therefore, when we do data analysis, the key is to analyze the interrelationships between the various key nodes and key people in the business process. These interrelationships may be abstract, such as information flows, capital flows, etc., or they may be concrete, such as various physical objects. Then, we need to identify choke points along the chain to help companies optimize their processes and even improve their business performance. Therefore, when sorting out business requirements, we must clarify the corresponding business scenario choke points and data choke points, classify users and blocking levels, and then use visual language to convert data into charts.
3.Build a closed-loop data ecological chain
Digital transformation is a huge transformation for non-digitally native businesses. This kind of change not only involves the change of business model, but also includes a new change in the overall strategic vision, organizational structure, management culture, personnel skills, working habits and thinking methods of the enterprise. In order to build a complete closed loop of enterprise data ecology, we can divide it into three levels: technology ecology, management ecology and data ecology.
The technology ecosystem involves a series of technical means to ensure the cornerstone of data assets, such as data centers, data warehouses, big data platforms, data marts, etc.
The management ecology is the core of the entire system, and it is necessary to establish a series of top-level data governance systems and talent training systems to realize the whole-process management and control mechanism, so as to promote the development and application of the ecosystem.
Finally, the data ecology combines management methods and technical means, through point-to-point connection, to provide effective support for data service work, and give full play to the subjective initiative of business personnel to achieve maximum business results.
In the digital era, the digital transformation of the financial industry has become a necessary development trend. Only by establishing a complete digital ecosystem and comprehensively improving the digital capabilities of enterprises from the three aspects of technology, management and data can they better adapt to market changes, enhance their competitiveness, and achieve sustainable development. In this process, it is necessary to continuously promote organizational reforms, cultivate digital talent, and strengthen data governance to ensure that digital transformation goes smoothly. We believe that on the road of digital transformation, only by following up unremittingly can we realize the dream of the digital era!
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