Rosenblatt Securities: Using Tableau for Pre-Trade and Post-Trade Analysis
Customer Company Size
SME
Region
- America
Country
- United States
Product
- Tableau
Tech Stack
- SQL databases
- MySQL
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Revenue Growth
- Productivity Improvements
Technology Category
- Analytics & Modeling - Real Time Analytics
- Analytics & Modeling - Predictive Analytics
Applicable Industries
- Finance & Insurance
Applicable Functions
- Sales & Marketing
- Business Operation
Use Cases
- Predictive Maintenance
- Real-Time Location System (RTLS)
Services
- Data Science Services
- Cloud Planning, Design & Implementation Services
About The Customer
Rosenblatt Securities is a New York-based firm that has been operating for three decades. The firm's primary goal is to help institutional investors prosper by providing them with trusted, conflict-free advice and expert trade execution services. Rosenblatt frequently ranks as a top-20 broker by volume and consistently lands in the top tier of leading independent and client ratings of broker execution quality. The firm is run by a team of partners, including Scott Burrill, who also manages IT. Rosenblatt Securities deals with a lot of data, both structured and unstructured, and uses this data to provide value to its clients and its organization.
The Challenge
Rosenblatt Securities, a New York-based firm that provides institutional investors with advice and trade execution services, was looking for a way to improve its pre-trade and post-trade analysis. The firm wanted to be able to perform derived analytics on hundreds of different fields and visualize the data quickly and simply. They wanted to provide their traders and clients with insights on when to buy or sell a security. The firm was also looking for a tool that could handle large amounts of structured and unstructured data, including time series data.
The Solution
Rosenblatt Securities chose to use Tableau to analyze market data. The firm found that Tableau was able to handle large amounts of data and perform derived analytics on hundreds of different fields. The data could then be visualized quickly and simply, providing insights that could be acted upon. The firm uses Tableau to calculate predictive analytics on 800 securities to determine when to enter or exit positions. The firm also uses Tableau Server internally and externally with a few stakeholders. The implementation of Tableau started with a trial download on a computer and has since been deployed across the organization.
Operational Impact
Quantitative Benefit
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