Guillaume Rondy,

Head of Data and Communications Offer Line at SimCorp

Investment managers use more resources than ever on data, but Data Management issues often prevent them from fulfilling their growth ambitions. In this article, we give some concrete market input and ideas on how to challenge data operating models and make the most of the exciting space that Data Management has evolved into. 

In contrast to a decade ago, investment managers now purchase datasets from financial data providers, ESG (Environment, Social, and Governance) data providers, and alternative data providers.

The idea is to uncover new investment opportunities by combining these various datasets. This trend will only accelerate as data volumes, complexity, and demand keep increasing.

However, having access to more datasets is a double-edged sword for buy-side firms. While it offers the potential for a competitive edge, it can also be a costly distraction if not executed well. Data lakes can quickly turn into data swamps that do not generate value.

For active asset managers, the ability to quickly determine the value of a dataset is an important differentiating factor, and an active manager might have all the best Alpha-generating tools but faces one critical issue: Accessing the data from all business systems is often a manual process.

As data becomes more scattered between different systems, it requires expensive manual streamlining, reducing agility and hampering the ability to generate insights and make faster decisions across the value chain.

In the worst cases, the investment manager cannot derive insight from its data despite spending more resources than ever on purchasing and handling the data. It is natural that firms want to share data across the organization as it is more cost-effective, but not all datasets need to be treated equally.

Whilst end-of-day security and price data for back-office functions or portfolio valuation purposes needs to follow strict data quality checks, investment simulations require real-time access to pricing information with little importance placed on data quality.

Any operating model geared towards agility and scale relies on strong Data Management practices to truly become a competitive edge. 

Many investment firms now recognize that their existing Data Management setups cannot keep up with this explosion in data demand and that their data operating model is preventing them from realizing their growth potential.

Guillaume Rondy
Head of Data and Communications Offer Line, SimCorp

Investment managers should ask themselves two key questions

The first is how to provide data to business functions more quickly and cost-effectively, which can provide a true competitive advantage.

The second is how to reduce inefficiencies. Data Management is not a one-size-fits-all kind of process, and firms need to review what is the core sets of data to be used per business function and optimize the respective underlying Data Management setup to reduce inefficiencies and eventually increase Straight-Through Processing (STP).

Increasing STP is undoubtedly on top of mind for many industry professionals.

In the webinar Taking a holistic approach to buy-side data management, which Data Management Insight hosted earlier this year, participants ranked too much manual intervention/high cost of human resources (71%) as the biggest challenge of legacy data management systems within their organizations.

A similar conclusion was reached in the 2023 Global InvestOps Report. In the report, 200 buy-side operation leaders globally cited inefficient workflows (56%) and data management and reconciliation issues (44%) as the primary reasons preventing them from reaching their growth ambitions.

Dynamic market 

The Data Management space has evolved significantly over the past three years because of several market drivers. I will focus on the two main ones here:

The first is the explosion in data demand as most investment managers have expanded into private assets, quantitative-driven strategies, and ESG investment strategies.

The second trend is increasing client demand for transparency, precipitated by the global pandemic.

During the pandemic, buy-side firms' end clients started taking a more active role in understanding their investments’ financial and environmental impact, forcing firms to improve digital capabilities for their clients to track and understand how their portfolios behave in real-time.

By using Data Management solutions and applying efficient data governance, firms can reduce their product time to market, improve the ability to derive insights, and empower their users to track their investments.

Coupled with an increase in asset mix complexity, this places great stress on many firms' aging Data Management setups.

One single data system

Many investment firms now recognize that their existing Data Management setups cannot keep up with this explosion in data demand and that their data operating model is preventing them from realizing their growth potential.

Some market participants have identified this issue and invested heavily in solutions. However, these solutions did not bring the promised outcome, and the fear of failing again is larger than the promise of a potential new Alpha generation tool.

Distributing the load to manage data across systems has often been seen as the best way to empower business users, but this approach creates silos that quickly become uncontrollable, preventing scalability.

Firms often risk ending up allocating substantial resources to streamline the data manually or patching systems locally rather than taking a holistic approach to Data Management.

A change in the target operating model is needed to avoid trying to patch the problem.

Modern approaches to data architecture challenge the traditional way of either centralizing all teams and knowledge of data or creating unreconcilable silos. Having one single source for data provides an organization with a core pillar of what it takes to have both agility and scale.

When done well, Data Management can be a powerful enabler for business teams and a true differentiator to drive competitiveness and client satisfaction.

The explosion in the demand for data analysts and data scientists and the fast and exponential rise of new Data Management technology providers such as Snowflake or Databricks are perfect examples of this.

The exciting conclusion for me is that Data Management has morphed from something that firms must do and keep to a minimum, to a winning differentiator!