As data consumption continues to increase across the investment management industry, leaders need to understand how this data is consumed across their organization. Leaders can segment these consumers into distinct personas in order to define their role, which will help determine what data is needed along with how it is used. This requires a change in mind-set since the focus has largely been on the traditional personas of data operations, middle and back-office professionals. With the increasing sophistication of both investment management professionals and clients, leaders need to look beyond these traditional personas and start to get more familiar with several emerging investment management roles around the organization.
Focusing on these emerging personas can help firms make more informed decisions and keep up with the increasingly quantitative nature of the financial services industry. It also helps firms determine what additional data types and data capabilities are necessary to support the work of these professionals. These new personas, including data scientists and experts in machine learning and artificial intelligence, will not be the last of their kind, and leaders will have to ongoingly seek out new personas that will further change the industry.
How Investment Management Roles are Changing
Traditional roles with stakes in acquiring and handling data correctly include data operations, middle office and back-office professionals. Historically, front-office staff, including traders, have been hands-off when it comes data management, operations, and issues, leaving these tasks to these traditional roles. Traders for example, only needed to identify a fixed-income security that has incomplete data and send it to colleagues who can rectify the issue by manually filling in the missing fields. As firms become more data-driven and automated, and develop more quantitative investment strategies, the roles and responsibilities of these personas is changing.
Five years ago, a sell-side job posting for a trader responsible for interest rate products would require skills such as an understanding of statistical concepts and the ability to program using Python. Now buy-side firms are looking for quantitative experts with similar skillsets to help meet their investment outcomes, which is a big advance for traders’ staff personas. This is true for portfolio managers too, since their skillset has evolved to become more comfortable with data creation and consumption.
Of course, professionals such as portfolio managers, traders, risk, and research have historically been core to any investment management organization. We group these roles into the emerging investment management roles along with some others, such as Client Portfolio Managers (CPMs), because they are part of a profile that is increasingly more quantitative, using various data sets and ad hoc analysis to help derive insights that can help drive investment outcomes as well as meet the needs of data savvy clients.
CPMs embody another important emerging persona. The CPM is a hybrid role: they support investment teams by handling a combination of relationship management, provide investment and subject matter expertise, along with sales functions – but do not have the ultimate investment decision-making authority. Still, CPMs need sophisticated data sets to perform their job function effectively, such as quantitative analysis and performance attribution reporting.
Changes in Consumer Profiles
Even the typical consumer profile for investment firms is changing, which creates another kind of emerging persona. Asset management clients have changed from needing less information, and less sophisticated information, to more demanding professionals who expect more complex data points for analysis, for flexible and dynamic reporting and even to populate rich visualizations of that data.
What does the industry need to make decisions quicker and satisfy the demands of these emerging investment management roles? Systematic ways to sort data, including stock screens, quantitative screens and signal generation, as well as evaluations of company and industry reports whose appearance will differ depending on the sector of the company covered.
Firms should also recognize that all these emerging personas will demand more dynamic reporting of data to ease the difficulty of modeling asset allocation. Providers serving these personas face transparency challenges in responding to demands for position look-through to see disaggregated views across both internal and externally managed assets.
What all these personas have in common is high demand for larger and more complex data sets. That can even mean they are willing to just rapidly on-board data and put it into their quant models, or send to a data warehouse and derive high-level bullet points and dashboards using certain software or programming code, without turning to a fuller business intelligence visualization.
The data management community and investment firm managers are just starting to grasp the usefulness of personas for defining data needs at firms. Fuller development of these personas is needed to reach a “final frontier,” which should include data scientists who can run complex analytics producing investment and trading insights. These emerging investment management roles are nascent, but a group to watch.