Capability Enablement

Today, gradient-boosted models leveraging hundreds of variables are commonplace. Consumers demand services in real-time and will not wait for batch processes. Regulators require in-depth explanations of decisioning rationale. Legacy analytics, decisioning and monitoring infrastructure cannot keep up, but most Big Data projects are long and expensive, plus it is often difficult for leadership to evaluate the success of these projects. Full Spectrum takes a use-case driven approach to Big Data, working backwards from the problems that need to be solved to find the right solutions.

Scaled Data Science and Machine Learning

In Big Data initiatives, it is critical yet difficult to create Data Science environments that will nimbly handle the terabytes of Big Data and that will connect this data to legacy production systems and processes. Full Spectrum brings a wealth of experience in Data Science enablement at large financial institutions, and we can help you make Big Data real through targeted use cases spanning from model and insight development to production implementation.

SAS-to-Python Conversion

Moving from legacy SAS processes to a modern data science platform enables financial institutions to attract talent, reduce the time to implement business intent, and enable analysis that is impossible to do with SAS. However, taking full advantage of the conversion requires more than simply converting SAS to Python. Full Spectrum has helped large financial institutions convert critical business processes from SAS to Python while taking full advantage of the speed and scalability of a modern data science environment.

Data Lake Migration

As more and more data is generated by customer activities and interactions, leveraging Hadoop and Big Data management technologies is essential to own, maintain and generate value from disparate data sources. We bring a use-case backed approach to create data products and discovery systems on Hadoop. These products enable the data lake to be directly consumed both for business decision-making and for Data Science experimentation and visualization.