A framework for effective data management outsourcing in investment management

Abstract

Investment firms have historically engaged in outsourcing a range of back, middle and front office functions to specialist providers. Anyway, operational data management, until recently, remained within the purview of the in-house data team and managed on-premises. This state of affairs is changing. Over the past decade, firms have attempted to centralise data governance — here defined as the process of managing the availability, usage and integrity of data through internal standards, policies and processes — in order to meet a range of data challenges including poor data visibility, rising data costs and regulatory requirements. While many of these attempts have resulted in expensive and inflexible enterprise data management systems, they have helped make the case for outsourcing data management. Firms are now in a position to draft appropriate service-level agreements (SLAs) for outsourced data management services, and many see the value that comes with outsourcers’ consumption-based models and agile approaches to data management. In most investment firms today, there is a recognition that there are various different types of data and various data processes, some of which are core and some of which are non-core. Firms are coming to realise that the foundational, but not differentiating, activities of operational data management is expensive and time-consuming and that by outsourcing it they can free up time and resources to focus on what is core and proprietary — data analysis, insight and using that data and insight as a factor in business decision-making. As firms set about migrating to managed data services, they need to consider the core boundaries of their business, the commercial models of available services and how they can best ensure oversight of their outsource partners. Those that get it right will form strategic partnerships that can unlock value from data.