Accurate, Controlled Test Data for Credit Card Testing
公司规模
Large Corporate
地区
- America
国家
- United States
产品
- GenRocket Test Data Automation
技术栈
- ASCII
- EBCDIC
- Parquet
- JSON
- AVRO
实施规模
- Enterprise-wide Deployment
影响指标
- Cost Savings
- Customer Satisfaction
- Digital Expertise
- Productivity Improvements
技术
- 应用基础设施与中间件 - 数据交换与集成
- 应用基础设施与中间件 - 数据库管理和存储
- 平台即服务 (PaaS) - 数据管理平台
适用行业
- 金融与保险
适用功能
- 商业运营
- 质量保证
服务
- 软件设计与工程服务
- 系统集成
关于客户
A major financial services company, managing billions of dollars in assets, provides a variety of services including consumer and commercial banking, credit cards, auto loans and savings accounts. Their credit card business alone represents millions of cardholders transacting billions of dollars in annual purchase volume.\n\nThe credit card market has become the largest U.S. consumer lending market and continues to expand by every measure of growth. Credit card providers are under intense competitive pressure to deliver new and innovative products in the form of loyalty and reward programs, balance transfers, cash advances and other perks. This places a big demand on developers and testers to manage a continuous pipeline of software enhancements and revisions. These applications interface with multiple systems as they execute a massive volume of credit transactions and manage user accounts. As part of a regulated industry, QA organizations are keenly focused on the accuracy and security of their information systems. The financial services company profiled in this case study provides an example of the rigorous testing and data provisioning requirements that must be followed when testing financial application software.
挑战
QA leadership for this organization wanted to eliminate a potential breach of sensitive customer information, such as social security and credit card account numbers, during their testing process. For this reason, they decided to evaluate the use of synthetic test data. They needed test data in a variety of formats for testing multiple systems and databases, including ASCII, EBCDIC, Parquet, JSON, and AVRO. The database environments include DB2, Oracle, SQL Server, Postgres, MySQL and Snowflake.\n\nPreviously, the QA team pulled data from production and manually scrubbed it to remove sensitive information and to create appropriate data combinations for testing. Now their goal is to automate the process of provisioning secure, synthetic test data with full control over data volume and variation with an ability to output the data in the multiple file formats described above. The QA team was introduced to GenRocket by one of the company’s system integration partners and decided to evaluate GenRocket’s Test Data Automation capabilities as part of a formal Proof of Concept (POC).
解决方案
The GenRocket Test Data Automation platform is a perfect match for these requirements. Its component architecture allows testers to define data structures, select the required data generators for each field, and match them with data receivers for the desired output format.\n\nThe GenRocket platform allows testers to import any data model using a database schema or Data Definition Language (DDL) to automate the process of provisioning synthetic test data. This ensures test data is generated with the correct data structure and parent/child/sibling relationships between data tables.\n\nGenRocket has more than 250 data generators including 7 different data generators just for credit cards. Generators have parameters that can be configured to accurately control the data values that are generated. They are part of a Test Data Scenario that controls the quantity of test data that is to be generated. To support a wide variety of output formats, GenRocket has more than 50 data receivers including two new receivers created for this POC – IBM EBCDIC and Parquet file formats. Any receiver can be paired with any combination of generators to provide flexibility over the data values used for testing and consistency of data used across multiple environments. GenRocket worked with the QA team at this financial services company to automate the process of importing the data model, configuring the data generators, controlling the record counts, and generating test data on-demand and in the desired output format.
运营影响
Case Study missing?
Start adding your own!
Register with your work email and create a new case study profile for your business.
相关案例.
Case Study
Real-time In-vehicle Monitoring
The telematic solution provides this vital premium-adjusting information. The solution also helps detect and deter vehicle or trailer theft – as soon as a theft occurs, monitoring personnel can alert the appropriate authorities, providing an exact location.“With more and more insurance companies and major fleet operators interested in monitoring driver behaviour on the grounds of road safety, efficient logistics and costs, the market for this type of device and associated e-business services is growing rapidly within Italy and the rest of Europe,” says Franco.“The insurance companies are especially interested in the pay-per-use and pay-as-you-drive applications while other organisations employ the technology for road user charging.”“One million vehicles in Italy currently carry such devices and forecasts indicate that the European market will increase tenfold by 2014.However, for our technology to work effectively, we needed a highly reliable wireless data network to carry the information between the vehicles and monitoring stations.”
Case Study
Safety First with Folksam
The competitiveness of the car insurance market is driving UBI growth as a means for insurance companies to differentiate their customer propositions as well as improving operational efficiency. An insurance model - usage-based insurance ("UBI") - offers possibilities for insurers to do more efficient market segmentation and accurate risk assessment and pricing. Insurers require an IoT solution for the purpose of data collection and performance analysis
Case Study
Smooth Transition to Energy Savings
The building was equipped with four end-of-life Trane water cooled chillers, located in the basement. Johnson Controls installed four York water cooled centrifugal chillers with unit mounted variable speed drives and a total installed cooling capacity of 6,8 MW. Each chiller has a capacity of 1,6 MW (variable to 1.9MW depending upon condenser water temperatures). Johnson Controls needed to design the equipment in such way that it would fit the dimensional constraints of the existing plant area and plant access route but also the specific performance requirements of the client. Morgan Stanley required the chiller plant to match the building load profile, turn down to match the low load requirement when needed and provide an improvement in the Energy Efficiency Ratio across the entire operating range. Other requirements were a reduction in the chiller noise level to improve the working environment in the plant room and a wide operating envelope coupled with intelligent controls to allow possible variation in both flow rate and temperature. The latter was needed to leverage increased capacity from a reduced number of machines during the different installation phases and allow future enhancement to a variable primary flow system.
Case Study
Automated Pallet Labeling Solution for SPR Packaging
SPR Packaging, an American supplier of packaging solutions, was in search of an automated pallet labeling solution that could meet their immediate and future needs. They aimed to equip their lines with automatic printer applicators, but also required a solution that could interface with their accounting software. The challenge was to find a system that could read a 2D code on pallets at the stretch wrapper, track the pallet, and flag any pallets with unread barcodes for inspection. The pallets could be single or double stacked, and the system needed to be able to differentiate between the two. SPR Packaging sought a system integrator with extensive experience in advanced printing and tracking solutions to provide a complete traceability system.
Case Study
Transforming insurance pricing while improving driver safety
The Internet of Things (IoT) is revolutionizing the car insurance industry on a scale not seen since the introduction of the car itself. For decades, premiums have been calculated using proxy-based risk assessment models and historical data. Today, a growing number of innovative companies such as Quebec-based Industrielle Alliance are moving to usage-based insurance (UBI) models, driven by the advancement of telematics technologies and smart tracking devices.
Case Study
MasterCard Improves Customer Experience Through Self-Service Data Prep
Derek Madison, Leader of Business Financial Support at MasterCard, oversees the validation of transactions and cash between two systems, whether they’re MasterCard owned or not. He was charged with identifying new ways to increase efficiency and improve MasterCard processes. At the outset, the 13-person team had to manually reconcile system interfaces using reports that resided on the company’s mainframe. Their first order of business each day was to print 20-30 individual, multi-page reports. Using a ruler to keep their place within each report, they would then hand-key the relevant data, line by line, into Excel for validation. “We’re talking about a task that took 40-80 hours each week,” recalls Madison, “As a growing company with rapidly expanding product offerings, we had to find a better way to prepare this data for analysis.”
