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论文编号：	14845
作者编号：	2120223754
上传时间：	2024/12/4 14:42:03
中文题目：	A银行集团商业智能业务流程优化策略研究
英文题目：	Research on Business Intelligence Process Optimization of A Banking Group
指导老师：	李凯
中文关键字：	商业智能；业务流程再造；商业银行集团
英文关键字：	Business Intelligence; Business Process Reengineering; Commercial Banking Group
中文摘要：	伴随着各行各业中海量业务数据的不断产生，越来越多的企业由此便进入到了数字化时代。在数字化时代，这些积累的历史数据是企业完成业务决策的重要战略资源，如果企业能够有效地将积累的业务数据进行采集、处理、建模和分析，那么企业将会从业务数据中获取商业洞察力，而这最终将会为企业的业务规划和决策提供支持，实现数字化管理。在这种背景下，商业智能技术应运而生。企业可以通过商业智能解决方案从海量的历史数据中获取有价值的信息，并以此指导现在和预测未来。如今数字化转型的浪潮席卷各行各业，越来越多的企业都纷纷选择建设商业智能系统来获取一个完整的数据分析与决策支持服务以保证自身的竞争优势来应对日益恶化的市场环境。然而，有调查数据显示有87%的组织的商业智能成熟度低。这个结果对于企业的投资的热情而言无疑形成了巨大的反差。 A银行集团是一家世界500强企业，其主要为澳大利亚和新西兰等29个国家和地区约950万商业银行客户与零售银行客户提供金融产品及服务。由于历史原因，A银行集团的核心业务系统都互相独立，这使得各部分业务数据无法在各业务系统之中流通和汇集，这给业务分析工作带来了极大的不便，而A银行集团通过自行组建商业智能团队完成商业智能系统的设计及开发解决了这一问题。在2020年的组织架构调整中，A银行集团新成立了数字化转型业务线并将商业智能团队并入其中，以实现将所有的业务数据进行整合并将其转化成有价值的业务信息，以此来定位和解决业务问题，提升管理效率，最终实现数据驱动决策的战略目标。然而，在日常工作当中，商业智能团队及其用户对商业智能业务的运行效率并不十分满意。 基于以上背景，本研究首先对数字化转型相关概念、商业智能相关概念以及业务流程再造相关理论进行了回顾，再通过对A银行集团商业智能业务的利益相关方进行访谈调查来描绘出现状，然后识别出存在的问题并分析其原因，最后根据这些问题提出具体的针对性的解决方案。
英文摘要：	With massive business data generated continuously across various industries, the digital time is coming to an increasing amount of enterprises. In the digital time, the accumulated historical data is an important strategic resource to support business making decisions. If enterprises can collect, process, model, and analyze their historical business data effectively, they will gain business insights from the data, which will support business planning and decision-making, achieving digital management ultimately. Under this circumstance, business intelligence technology emerged. Business intelligence solutions can provide enterprises an integrate and reliable decision system, which makes enterprises get valuable information from massive historical data to both plot the course and predict the development trends of business. And the decision-making process will be more scientific and reasonable, enterprise will lower down operation costs and risks as well as enhance operational efficiency. Nowadays, the digital transformation floods across all industries, and more and more enterprises are building business intelligence systems to acquire an integrate data analysis and decision-making support service to secure their competitive advantages in a deteriorating market environment. However, a survey data shows that 87% of organizations are classified as low business intelligence maturity, which is a big contrast to the investment enthusiasm of enterprises. A Banking Group is a Fortune 500 company that mainly provides banking and financial products and services to approximately 9.5 million business banking and retail banking customers across 29 markets such as Australia, New Zealand and so on. Due to historical issues, the core business systems of A banking group are all independent with each other, which hinders the flow and integration of business data among different systems, causing significant inconvenience to managers in conducting business analysis, and A banking group addressed this issue by building its own business intelligence team to design and develop business intelligence systems. A banking group restructured its organization in 2020 with establishing digital transformation business line, and business intelligence department was consolidated into the new business line, aiming to integrate all business data and transform it into valuable business information to identify and solve business problems, enhance management efficiency, and achieve the strategic goal of data-driven business. But, in daily operation, both business intelligence team and users are not satisfied with the business operational efficiency of the business intelligence department. Based on information above, the research begins with a review of relevant theories and concepts of digitization, business intelligence, and business process reengineering, then performs interviews with stakeholders of business intelligence team to elaborate the status quo, and identifies current issues and analyzes the causes respectively, then ends up with providing a specific and particular process reengineering solution.
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