Olmstead Blog

In continuation of the Digital & Data-Driven Asset Manager Insight Series and in response to inquiries from our our clients on “how do we become data-driven?” we address the critical success factors at both the executive and the execution level.

Asset managers see the potential for data science to generate alpha and increase sales but many struggle to achieve results. Investment models, alternative datasets, predictive lead generation analysis, marketing campaigns, client content, and more will continuously evolve as asset managers search for the winning or differentiated formula. 

To become a data-driven asset manager, firms must attract a new data savvy workforce. Our industry has always enjoyed the success of being able to recruit top talent from colleges. Recent graduates have gained experience creating data solutions using Python, big data, statistics, and the cloud and are perfectly prepared to help us achieve our ambitious data strategies. However, there is one big problem – our data platforms are not ready.      

How does the asset management industry adapt and thrive in the age of digital disruption? In an upcoming series of Olmstead Insights focusing on Digital & Data-Driven Asset Management, we will explore the critical role data plays at the heart of the digital transformation.

An investment manager is a complex web of data. In a data-intensive industry, few firms have mastered their reference data to create a single view of their “data truth” and even fewer firms have solved the single data platform to meet the needs of front, middle, and back office, distribution, and corporate functions. With data science being added to the mix, the data landscape has become increasingly more complex and also exciting, elevating data from operational efficiency to alpha generation. With data at the heart of all investment business functions, an under-performing data platform impacts business results.

Asset managers are tackling a bevy of new data-related requirements, driven by clients, boards, global regulators and the FinTech disruptors. Many buy-side firms have responded by embarking on operational and technological transformation projects. Some have focused on data access and delivery, upgrading legacy data architectures using hubs, virtualization, warehouses, or data-as-a-service (DaaS); others are tackling costs and efficiency by taking advantage of newer technologies such as machine learning, robotics, and various forms of artificial intelligence (AI). As they institute these initiatives, investment managers also discover new bedrock needs: enhanced data governance, integrity and controls.

There has never been any greater need for Investment Management firms to optimize the management, delivery and reporting of their enterprise data. Firms are being driven to reconsider their business models and adapt architectures and IT systems to accommodate several new factors affecting the industry.  All these factors, be it product innovation, the shift to passive, new regulations, raised client service expectations, or fee compression, converge on one thing - Data.