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.