About a-team Marketing Services
The knowledge platform for the financial technology industry
The knowledge platform for the financial technology industry

A-Team Insight Blogs

How to Combine Data Science and Analytics to Drive Business Value Out of Data

Subscribe to our newsletter

Data science and analytics are critical to driving value out of data, delivering business insight at speed, and gaining competitive advantage. With so much at stake, financial institutions need a strategy that puts data scientists in business units, identifies appropriate technologies and tools, and scales solutions beyond proofs of concept.

This week’s A-Team Group webinar on data science and analytics will cover the role of data scientists, steps you can take to develop analytics that provide business insight, how to overcome challenges along the way, how to win management buy-in and more. Discussing these issues will be Arijit Bhattacharya, executive director of analytics at UBS; Stef Nielen, director of strategic business development at Asset Control; and James Corcoran, chief technology officer, enterprise solutions at Kx Systems.

Previewing the webinar, Corcoran notes the multi-disciplinary nature of data scientists, who need to be experts in all aspects of data, including architecture, engineering, analytics, and machine learning. The best will also have vertical domain knowledge and all will need to converse with the business.

Corcoran notes data architecture as the starting point for analytics projects, saying: “Projects tend to fail if you think of them as point solutions. You need to prioritise data architecture and data across the business. If you do this, data scientists working with machine learning should be able to provide good quality, timely and ideally real-time insights into data.”

Nielen takes a slightly different approach, proposing an iterative process of gathering required data, and cleaning and consolidating the data to provide a central source. Open source technologies and tools, particularly AI applications based on R and Python, such as Tensorflow, can then be used to interrogate and analyse data on the central platform.

Nielen comments: “These languages have progressed rapidly in recent years. They are modern and easy to learn, allowing both quants and portfolio managers to use them. With a few lines of code you can do tremendous things.”

Subscribe to our newsletter

Related content

WEBINAR

Recorded Webinar: Unlocking Transparency in Private Markets: Data-Driven Strategies in Asset Management

As asset managers continue to increase their allocations in private assets, the demand for greater transparency, risk oversight, and operational efficiency is growing rapidly. Managing private markets data presents its own set of unique challenges due to a lack of transparency, disparate sources and lack of standardization. Without reliable access, your firm may face inefficiencies,...

BLOG

Theta Lake Touts First-of-its-Kind ISO Certification for AI Comms Data Trust

Data security specialist Theta Lake has been awarded trust certification for its artificial intelligence-powered compliance communications services. The designation was conferred as the company prepares to release a report that shows IT teams in financial services and other industries are facing challenges with their AI governance and security. Santa Barbara, California-based Theta Lake achieved ISO...

EVENT

AI in Data Management Summit New York City

Following the success of the 15th Data Management Summit NYC, A-Team Group are excited to announce our new event: AI in Data Management Summit NYC!

GUIDE

Regulatory Data Handbook 2025 – Thirteenth Edition

Welcome to the thirteenth edition of A-Team Group’s Regulatory Data Handbook, a unique and practical guide to capital markets regulation, regulatory change, and the data and data management requirements of compliance across Europe, the UK, US and Asia-Pacific. This year’s edition lands at a moment of accelerating regulatory divergence and intensifying data focused supervision. Inside,...