Practice: Data Science

A key theme running through this five-part series is that Enterprise Architecture (EA) has never been more essential to organizations facing rapid technological change and growing business complexity. As enterprises strive to align strategic objectives with operational execution, EA provides the structural backbone that connects vision to delivery. This installment outlines some of the foundational elements required to build an effective, scalable EA practice that integrates business priorities with data, application, and technology capabilities to drive clarity, agility, and long term value across the organization.

Our latest 2026 Asset & Wealth Management Outlook is out, and the message is clear: the industry is shifting from innovation to execution. After years of investment in AI, data platforms, and private markets, firms are now under pressure to deliver measurable results. Despite market resilience, growth is increasingly driven by operational discipline, strong data foundations, and strategic clarity. As fee pressure, regulatory scrutiny, and evolving client expectations intensify, firms that can translate investment into performance will lead the way.

Is the investment in an Enterprise Architecture (EA) practice and the value it can deliver understood and fully leveraged in organizations? In a five part series, Mike Quinlan DBA, MSSM, TOGAF L2, CSSLP, Enterprise Architect at Alpha FMC, discusses the foundations of an effective EA practice and how it can benefit an organization. The second installment addresses some of the organizational factors that need to be considered when establishing and maintaining an effective Enterprise Architecture practice.

We set out to cut through the noise of AI use within Investment Research and decided to conduct a pulse survey to quantify some of the discussions we were having with clients. Our pulse survey spanned asset managers (65%), wealth managers (15%) and asset servicers (10%). The findings are informed not only by survey data, but by the experience of our firm’s AI Working Group—professionals working hands‑on with asset and wealth managers as they deploy GenAI solutions.

In a five part series, Alpha FMC discusses the foundations of and effective EA practice and how it can benefit an organization. In the first installment, the origins and evolution of EA are addressed. Each installment will discuss a different facet of the EA practice to include:

What EA is and what it should avoid becoming
Organizational factors that shape the EA practice
Foundational elements of the EA practice
Governance
Measuring Value

In the fast-paced world of asset and wealth management (AWM), fund services providers grapple with an explosion of financial data—from market feeds and portfolio transactions to regulatory filings and client profiles. Yet, much of this data remains siloed and underutilized without inherent context and structure. The semantic layer acts as a vital bridge, infusing raw data with meaning, relationships, and interpretability to drive informed investment decisions and operational efficiency.

Data and AI are reshaping how investment managers operate – and the ecosystem around them is evolving faster than anyone can track.

Alpha FMC are collaborating with Snowflake on the release of their latest product offering, Openflow, to support clients with compliance with the Digital Operational Resilience Act (DORA), which came into force in January 2025. In-scope firms have, to date, leveraged manual processes to comply with these standards. Alpha’s accelerator will allow for the full end-to-end automation of the process to meet the regulatory requirements.

Are your teams turning today’s data deluge into AI-powered insight, or still drowning in information?
We’ve seen that, with the right approach, firms can spot untapped mandates before competitors, free Sales, Marketing, Client Service, and Product teams for high-value conversations, and predict redemptions early enough to protect revenue. Read our insights on the enablers and critical success factors here.

Artificial intelligence has seen explosive growth across industries, and financial services have been no exception. As AI capabilities evolve, a new paradigm is emerging: agentic frameworks, which can revolutionize financial workflows by utilizing multiple autonomous agents to manage complex, dynamic environments like trading, risk, and compliance.