Blueprint for Success: Enabling Data-Driven Insights Across the Asset Management Value Chain

Natalie McIntyre, Sam Gevirtz, Kerri Heidemann, Shreya Moola

Data science and analytics is becoming critical to differentiation and success in the asset management industry. Throughout the 2010s, outsized market returns allowed many asset managers to rely on market performance for organic AUM growth and hid systemic headwinds such as fee compression, product commoditization, and higher investor service expectations. With low growth and subdued market returns expected to continue going forward, differentiation will be more difficult and efficiency more important. We believe that harnessing insights from data, which is more accessible than ever across the asset management industry, is the key to winning in this environment and into the future.

In this whitepaper, we discuss why we believe the time to invest in data science and analytics is now. Many firms we work with have already begun their journey, but there are challenges to getting meaningful, actionable insights from data at scale. Our paper explores four important angles:

  • What are the main components to a successful data science and analytics platform? We identify three critical components managers must consider; (1) the front-end user experience, (2) the Data Science & Analytics ‘Engine’ where insights are generated and extracted, and (3) the underlying data foundations which are needed for quality and consistency. In our experience, neglecting these components results in poor adoption, limited insights, or an inability to scale, respectively.
  • What does the operating model of the Data Science & Analytics ‘Engine’ need to consider? We zoom in on the heart of a firm’s data science capabilities – the Data Science & Analytics ‘Engine’ – and highlight the need for human capital, culture, thoughtful use cases, and well-designed processes alongside the technology and types of insights at a firm’s disposal.
  • What does this look like in practice? We are frequently asked by our clients to help bridge the gap between data science and analytics as concepts and as real business outcomes. Organizations are applying these capabilities across the value chain. Historically, the Front Office has been far ahead in applying data science and analytics across core activities, such as research and portfolio construction. However, we are now increasingly seeing compelling applications and outcomes across the value chain such as in Marketing, Sales, and Operations.
  • How do you get started, practically? We have deep experience working on data science and analytics projects with our clients, so we offer up some tips and tricks for starting off and for continued success. We outline our ‘crawl, walk, run’ philosophy, the importance of well-defined questions and use cases, and the value of business champions and leadership.

Contact Us

Alpha FMC has extensive experience working with asset managers to explore where and how data science and analytics can empower their business. We’d be delighted to discuss this topic as well as our analytics technology solutions and consulting frameworks which can support accelerated delivery. For more information on how Alpha FMC can help your organization, please contact us here.

About the Authors

Natalie McIntyre

Natalie is a Director, Head of Technology Services and a member of the Management Committee in North America. She has led large scale transformation programs and advised clients on business strategy, distribution management, investment operations and M&A due diligence for both retail and institutional asset managers. Most recently, Natalie has been on the forefront of leveraging Data Science to drive digital sales and generate investment alpha. Prior to joining Alpha, Natalie managed distribution operations and business intelligence at Bridgewater Associates.

Sam Gevirtz

Sam is a Manager based in Alpha’s New York office with over 8 years of experience in the Asset & Wealth Management industry. He has managed and delivered on a wide range of engagements spanning across data & analytics strategy, target operating model design, technology implementation, and growth strategy.

Kerri Heidemann

Kerri has over 7 years of consulting experience working with Asset Managers, Asset Owners and Wealth Managers to deliver technology-led transformations across distribution, investments and operations with Canadian and US clients. Her more recent experience covers data science and analytics strategy, distribution strategy, target operating model design, implementation and change management.

Shreya Moola

Shreya is a Consultant in Alpha's New York office with experience supporting Asset and Wealth Managers with distribution technology transformations and M&A integrations. Most recently, she led efforts at a Wealth Manager to enhance the quality and accuracy of their CRM data to increase adoption of their existing data analytics platforms and drive future decision-making using data science and analytics.