A Large Investment Management Firm Increase Efficiency Through Outsourced Data Management Operations

THE CLIENT: A large North American investment management firm

USERS: Research and investment operations 

In recent years, private equity and private fixed income investing have experienced remarkable growth, fueled by the pursuit of higher returns and diversification in response to volatile public markets. As of 2023, the global private equity market has reached an estimated $4.5 trillion in assets under management, reflecting a compound annual growth rate (CAGR) of around 12% over the past decade. The private debt market has similarly expanded, with assets exceeding $1 trillion, as institutional investors increasingly turn to non-traditional fixed income strategies. However, investing in private markets presents distinct challenges compared to public markets, primarily due to the lack of consistent data and common security identifiers. Investment managers strive to treat private securities with the same level of uniformity and transparency as public securities, but the absence of standardized identifiers complicates this objective. Additionally, the difficulty in obtaining reliable performance metrics, valuations, and credit assessments for private investments hampers thorough due diligence. 

Pain Points

For this large and one of the oldest investment management firm in North America, a significant challenge has been the integration of over 50 disparate data sources as well as the need to manage and link unstructured data for research and insights, all while developing standard operating procedures and automated workflows in an increasingly cumbersome and error-prone process. They needed to identify a well-recognized third party that could provide:

• Outsourced data management for linking, matching, and enriching entity data of multiple sources with Bloomberg and S&P company foundation data
• Operations staff to augment a new team developing operating procedures and workflows
• Data Automation for data acquisition and curation service to ingest structured and unstructured data to support their research teams
• Enterprise Data Management (EDM) for entity master, acting as a centralized repository providing a unified view of entities across various data sources and distribution to Snowflake
• AI-based Entity Matching to improve match rates and confidence in the data

The front office has reached out to the S&P Global’s EDM and Data Management-as-a-Service (DMaaS) team to tackle these challenges. EDM is the one of the most trusted Enterprise Data Management solution, boasting the largest client base across North America, EMEA, and APAC. Together with DMaaS, it offers clients access to the world’s largest entity dataset powered by AI. Financial institutions and investment managers can rely on S&P Global’s deep domain expertise and best practices for a reliable, effective solution with rapid deployment.

The Solutions:

The client collaborated with S&P Global resources to identify key pain points and establish project goals. The S&P Global team developed a target operating model along with a comprehensive project plan that effectively addressed this large institution's needs, ensuring timely delivery and a smooth go-live process. Our integrated solution encompasses the extraction of alternative data sources, matching, cleansing, and consolidation of both internal and external data. The enriched data is then distributed to downstream systems via Snowflake, allowing over 400 research and front office users to access the cleansed data seamlessly within the company’s investment platform.

Step 1: Acquire the data

S&P Global employs a range of sophisticated data adapters that are tailored to connect with different types of data sources, including databases, APIs, flat files, and third-party data. Once the data is acquired, the adapters help standardize and transform the data into a consistent format. This process is crucial for ensuring that the disparate data can be effectively matched and linked. By applying predefined rules and data transformation techniques, the system ensures that variations in data formats, naming conventions, and structures are reconciled.

Step 2: Entity Matching powered by AI

After standardization, the Entity Linking service utilizes advanced algorithms and machine learning techniques to match and link entities across the integrated datasets. This involves identifying duplicates, resolving discrepancies, and establishing relationships between entities, which is critical for creating a unified view of the data.

Step 3: Enrichment, Mastering, & Distribution

The integrated data is then enriched by incorporating additional context and information from S&P Global and other third-party providers. This includes financial metrics, corporate hierarchies, and market data, which enhance the overall context and usability of the entity profiles. Data enrichment is a crucial step for improved downstream analytics, reporting, and decision-making. The enriched and linked entities are then compiled into a centralized master repository, EDM. EDM serves as a single source of truth for all entity-related data, ensuring consistency and accuracy for downstream applications. Once the master entities are created, a data pipeline is established to distribute the cleansed and enriched data to Snowflake.