Below are examples of work where Mass Street Analytics played either a lead role or significantly contributed to the work.
System Conversion For Health Insurer
Federal regulations mandate that health insurers modernize their data systems. A major health insurer had been running their systems on seventies-era mainframes. They needed their entire data infrastructure upgraded before the federally mandated deadline in two years. We provided the manpower and expertise to design and implement a modernized enterprise-grade data infrastructure.
New Data Warehouse For International Biopharma Firm
A biopharma firm was experiencing eroding profit margins but did not have the analytic capability to understand what was contributing to the problem. We built a data warehouse that not only helped to identify and eliminate the inefficiencies but also replaced the old monthly financial reporting system. We reduced monthly financial report preparation time from 3 weeks to 20 minutes.
Data Hub Deployment For A Large Telecom Provider
A major telecom was struggling to gain deeper customer insight due to data silos. Information was spread across many different large databases, and it was impossible to get a full picture of customer activity. We worked with the firm’s resources and 3rd parties to integrate data from various databases into one database. By creating a data hub and introduced the organization to data science analysis techniques, we were able to provide that true 360-degree view of a customer.
Providing Strategic Expertise For Agriculture Startup
An agriculture startup was just starting their analytics journey, and they had no senior data professional to guide them along the way. In addition to technical capability, we provided guidance on infrastructure and data architecture with an eye towards growth and the future. We build robust, highly available systems that are still in use today. Additionally, we gave them a data framework, so no matter who works on their data, there will be a consistent and unified approach.
New Business Intelligence Platform For Financial Services Provider
A provider of financial services to major corporations had data that was siloed across the organization. They needed to centralize information across many different geographically separate business units. We built an end to end business intelligence platform that imported information from several systems that included 3rd-party inventory systems, banking systems, and their CRM. The platform enabled executives to access data anytime and anywhere without the support of IT.
Customer Segmentation For Major Telecom
A major telecom had terabytes of data that needed to be analyzed for the purposes of customer segmentation analysis. Instead of using sampling techniques that are prone to error, we analyzed the entire dataset for the customer using common data science algorithms. After the analysis, we delivered a presentation that showed how exactly their millions of customers were using the interactive voice response system. This information was used to make recommendations for changes to the system.
Advanced Data Systems Engineering
Data Warehouse Acceleration For A National Marketing Firm
A national marketing firm was experiencing significant issues with its data warehouse load. It ran on batch processes that worked overnight and intraday. The process was prone to frequent failures that resulted in a delay of the delivery of data products that sometimes amounted to days. We re-engineered their data pipeline into a real-time paradigm. This resulted in a significant increase in uptime and a concurrent reduction in maintenance cost in addition to consumers getting their data instantly instead of having to wait for an overnight process.
Market Intelligence Platform For Investment Advisor
An investment advisor wanted to analyze years of historical data quickly to develop investment recommendations for his clients. We developed a data pipeline that imports data from various public and private sources. We then developed a batch process that analyzed the data using various data science algorithms to develop predictions about market conditions. We then developed a web interface that allowed a nontechnical user to view the information. The final product was a value-added service that the advisor could sell to new clients.