Data Management Meets Operational Challenges In The Alternative Asset Management Industry

By Mark Alayev

Featured in AlphaWeek

The Problem

Managers once sought far and wide for datasets they thought might give them an investment edge. However, with the rise of the alternative data industry, managers of all sizes and strategies now have seemingly democratized access to data. The issue at the forefront of forward thinking managers’ minds is not “how can I find more data?”, but how can they better ingest data into their models and systems, improve access to it across teams and better mine it for actionable insights.

Data management and strategy have risen to become critical business functions, not just for quantitative managers, but for traditional asset management firms, fundamental hedge funds and even private equity managers.

Whilst managers of all sizes are grappling with this problem, the issue is heightened for smaller managers that do not have a Chief Data Officer, or in many instances even a CTO. The task therefore is often left to the investment and operations team.

Managers are rushing to understand how they can manage their data more effectively so that their investment teams can focus on unearthing actionable insights from it.

The Complication

For hedge funds: A typical process for managing data might include scouring through various sources of information, from third party data sets from Bloomberg or Refinitiv to a multitude of internal Excel spreadsheets, to input data into models to analyze the value of securities to compare them with how the market is currently pricing them. This is a time intensive process that has to be actioned by analysts, who are also responsible for coming up with trade ideas.

For private equity firms: The economic consequences of the pandemic have meant PE firms are having to deal with changing valuations of their portfolio companies. For many, especially in vulnerable sectors, financial support from GPs won’t be enough, and as such, GPs are looking for ways to optimize data sharing across portfolio companies with a view to improving areas such as customer acquisition, to improve the value of said companies. Creating a proprietary data management solution from scratch to solve these challenges requires a specific skillset that is both hard-to-find and costly to acquire.

The Solution

A managed data services platform architects an efficient data flow that allows investors to better understand, access, and harness the power of their data through data warehousing and ingestion, preparing it for analysis. It provides a cost-efficient infrastructure powered by automation that removes the requirement for teams to spend hours on manual processes.

Virtual data warehouses separate the data processing and data storage layers, allowing managers to be public cloud-agnostic and scale compute while keeping a single source of truth.

A key benefit of data management platforms is their ability to provide insights that were previously not available, at speed.

While data ingestion can be automated, in many cases it takes for the form of adding current data to spreadsheets, which in the process of data so, removes historical data. Proper warehousing, supported by automation, gives managers the ability to track and store data over time so they can add historical datasets to their analysis.