International Credit Union Leverages Mainframe Data with Connect to Build Market-Leading Solutions
公司规模
Large Corporate
地区
- America
国家
- United States
产品
- Precisely Connect
- Microsoft Azure Databricks
技术栈
- Change Data Capture (CDC)
- Batch ETL
- Data Lake
实施规模
- Enterprise-wide Deployment
影响指标
- Productivity Improvements
- Digital Expertise
- Innovation Output
技术
- 分析与建模 - 大数据分析
- 平台即服务 (PaaS) - 数据管理平台
- 应用基础设施与中间件 - 数据交换与集成
适用行业
- 金融与保险
适用功能
- 商业运营
用例
- 远程资产管理
服务
- 系统集成
- 数据科学服务
关于客户
The client is a full-service credit union with over 10 million members and assets exceeding $130 billion USD. It employs more than 22,000 people across over 300 locations worldwide. Unlike traditional banks, credit unions serve specific groups of people, such as employees of a particular company or members of a not-for-profit social group. This credit union is committed to providing personalized service to its members, who expect financial strength, safety, and better interest rates compared to larger banks. The credit union serves millions of Americans, including those living and working abroad, offering them a sense of financial security and a connection to home. To remain competitive, the credit union must be more efficient, innovative, and technologically advanced than its competitors.
挑战
Serving nearly 10 million members across the U.S. and in dozens of countries, with assets and transactions handled in just as many different currencies, requires a truly global presence and technological capabilities. This credit union has 22,000 employees working at its three operations centers and over 4,000 branch locations globally. And its core IT is likewise expansive and complex. So, like thousands of other global financial firms, it relies heavily upon IBM mainframe systems to run its business. While mainframe systems are unsurpassed in their ability to process millions of banking transactions at millisecond speeds, their complex and unique architecture and data formats make it extremely difficult to access and use the data they generate for business analytics, ITOM or to manage enterprise IT security in an integrated way. So too, building and running applications on mainframe to provide the kind of mobile and online services that people expect is more than challenging. The specialized skills and extremely long development and testing cycles required are just impractical and costly. So, when the credit union’s Data Governance and Management team embarked on a three-year effort to modernize its systems and services, one of their first and most urgent issues was getting past the restrictions imposed by mainframe systems in order to build truly data-driven, AI powered and cloud native operations. The higher-level objective driving that architectural transformation was to be able to run advanced analytics on huge volumes of customer data, to derive meaningful insights that could in turn guide and accelerate the creation and deployment of innovative new services.
解决方案
The team understood from the start that not only was it critical to have access to all their mainframe-generated customer data, it was just as important to be able to deliver it to their other core systems, developers and data scientists as quickly and continuously as possible. To achieve this, the team built a Microsoft Azure Databricks environment which it dubbed the 'Data Ingestion Factory.' The objective was to feed all their customer data streams into the Databricks data lake as soon as it was created, cleansing, curating and making it accessible to developers and data scientists 'ahead of demand,' that is, avoiding reactive, ad hoc data collection efforts each time a new application or service was being developed. To make all this possible, they implemented Precisely Connect as their mainframe data collection and forwarding solution. Connect uses efficient and flexible change data capture (CDC) and batch ETL capabilities to deliver mainframe data, including Db2/z, IMS, and VSAM files, to cloud data lakes and analytics platforms. Connect’s graphical interface makes it fast and easy to create flexible and re-useable data transformation and delivery models, with no need for specialized skills, coding or tuning, even when working with complex mainframe data.
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