Over the pasdecade t and a half, data management trends in the Investment Management industry have been rather straight forward. First everyone was chasing trends in integration, messaging, and service oriented architectures, followed by a period of focus on enterprise data warehousing and reporting, resulting in an unrealized vision of the be all repository of quality data for everyone. At the time, the visionary objectives of these trends spurred massive projects aimed at reengineering the way applications and services exchanged data and the way consumers accessed information.
Looking back, these trends pale in complexity relative to today's data management opportunity and solution sets. While many organizations are still rightfully focused on integration, warehousing, and reporting initiatives, many new trends have challenged firms to shift their IT and data architecture principles beyond control and structure to openness and innovation.
Some of the most common questions we get from clients, and the industry, illustrate the unknown challenges and benefits of today's trends for Investment Managers:
What can big data do for me, and what is it?
How to I deliver self-serv technologies to business users, and what is it?
Can I solve all my data access problems with Business Intelligence?
Do I need a Chief Data Officer and Data Scientists?
The ability to execute on these questions is evolutionary to organizational culture in many respects, and cultural shift is the most challenging transition of all. First and foremost, for most large and mid-tier investment managers, the idea of implementing solutions that do not operate in highly controlled technical data architectures that can be predicted, reconciled, and quality assured is entirely contradictory to IT's core fabric. Additionally, providing pseudo technical tools to users such as Business Intelligence, or open source development platforms such as R and MATLAB for complex modeling and analytics, sparks the fear of shadow IT and in some cases a misguided sense that IT's value to the organization is not being fulfilled. The organizational and cultural transition required to fully leverage the tools of the future is the most significant and overlooked challenge of all.
What's also ironic about the enablement of the technologies that assemble information from multiple sources and provide a user the ability to interrogate and analyze it in a way not possible from a data warehouse or typical report, is that the age old phrase garbage in – garbage out rears its head. Source quality management of data upon acquisition or creation becomes of the utmost importance. This is where the value of Data Governance plays a role. Defining the sources and owners of data used throughout the enterprise is key to implementing the measures and quality controls necessary to enable these new tools. Whether there is a Chief Data Officer or Head of Data Management, it is the organizational commitment to process management and data quality that will produce the inputs required to successfully open an architecture and enable end users. The Data Management Organization's strategic objective is to facilitate and champion the organizational understanding of governance principles and how they apply to the enterprise.
With this said, there are visionary trends which are still relatively out of reach. However, these trends are influencing innovation and transformation just as the full vision of service oriented architecture and enterprise warehousing did. Big Data is predicated on the idea that unseen value within data is discovered through collection, correlation and analytics. In the grand scheme of things, the concepts of Big Data appear to be converging with self-serve technologies and Business Intelligence for the Investment Management industry. Firms continue to pay for research, pricing, reference data, benchmark information, etc., and the notion of mass structured and unstructured data collection and interrogation is a ways off for areas of core investment decision making outside of customer acquisition and product development. Similarly, the role of the Data Scientist is gaining prominence (some say Data Science dates back to the 1970's), and appears to be converging with highly technical Quants and Analysts in the Front Office or in R&D arms of the organization.
These trends and challenges make it one of the most exciting times to be in our industry, primarily due to the exploration and adoption of emerging innovations which is new for a conservative industry that historically choses to wait for technologies to mature.