下载PDF
Rosenblatt Securities Uses Alteryx and Tableau to Solidify Its Position as the Buy-Side Firm of Choice
技术
- 分析与建模 - 数据即服务
适用行业
- 金融与保险
适用功能
- 商业运营
用例
- 实时定位系统 (RTLS)
服务
- 数据科学服务
挑战
Rosenblatt Securities 是一家领先的独立机构经纪和投资银行精品公司,在为客户执行复杂的数据分析时面临挑战。该公司正在处理数百万行执行记录和市场报价数据,通常需要几天甚至几周的时间才能处理完。该公司自 2006 年以来一直在使用 Tableau 可视化软件,但许多流程包括混合不同的异构数据集和运行多个分析模型,这可能需要几天甚至几周的预处理时间,并且仍然需要手动进行错误抽样。该公司面临着一个决定:是花费数千美元购买专门的 ETL 工具来与其结构化和非结构化数据源进行交互,还是在内部构建这样的工具。
关于客户
Rosenblatt Securities 是首屈一指的独立机构经纪和投资银行精品公司之一。这家只接受代理机构服务的公司被认为是将个性化服务和诚信与业内对市场结构和交易动态最深入的理解相结合的领导者。该公司代表股票和上市衍生品市场的客户。该公司自 2006 年以来一直在使用 Tableau 可视化软件,并且经常将 Tableau 推向超出其预期用途的境地。许多流程包括混合不同的异构数据集和运行多个分析模型,这可能需要几天甚至几周的预处理时间,并且仍然需要手动进行错误抽样。
解决方案
Rosenblatt Securities 决定部署 Alteryx Analytics,它与公司现有的 Tableau Software 投资无缝集成,并无缝连接到其内部实时和历史数据基础设施。Rosenblatt Securities 使用 Alteryx 进行数据混合和高级分析,并使用 Tableau 可视化数据集,从而能够非常快速地发现趋势和主题。该公司用简单的拖放式分析程序和可视化工作流程取代了复杂的脚本,从而快速获得结果,使他们能够轻松地将分析追溯到数据源,并将某些流程从几周或几天缩短到几小时甚至几分钟。仅几个月后,Rosenblatt Securities 就已将 Alteryx 嵌入其许多流程中,包括其对每天推动投资格局的主题的日常分析。
运营影响
数量效益
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.”