Shoreline’s North American team hosted a private industry roundtable on January 12, 2022 with peers from asset owners and managers to discuss common opportunities, challenges and trends in investment data & analytics. It was an active discussion with highlights of the primary themes summarized below.
Distinction between investment operational data and new data/analytics
- The categorization of data (e.g., operational vs research) is not as critical as defining the level of trust required to support different use cases. Categorization is useful for understanding the data context and is one metadata attribute that informs how and when the data should be used.
- Metadata is important for understanding data context and informing how it should be used. Other metadata attributes include information like timing, data quality, source (IBOR vs ABOR), etc.
- Data that is used for decision-making, regulatory, management reporting or public releases needs to be trusted (i.e., high data quality).
- Decentralized analytic capabilities with a centralized data repository is the preferred operating model for roundtable members, where centralized repositories allow contributions from decentralized teams for derived assets.
- Mastering all datasets is not required. Roundtable participants assess and define (typically based on business need and associated cost) what data needs to be mastered and what data does not need to be mastered.
- Emerging technologies are being applied to “traditional” data sets, but may not be an appropriate fit, and may not produce the desired results.
Investment use cases driving demand for new data and analytics
- Alpha-generation is the front-office driver for data as portfolio managers operate in a competitive environment, where data is among the most common ways to gain an investment edge. Portfolio managers are continuously looking for new / alternative types of data to help drive this alpha/investment edge.
- Data is ubiquitous and readily available. The main challenge is mapping data to public and private entities. The difficulty in mapping alternative datasets to public and private entities is that there are typically no explicit linkages and these relationships must be inferred.
- Increasing the utility of private data for private investment research is a priority, given private assets data is generally less mature and less available than public markets data.
- There are some commonalities between the business drivers for private and public assets new datasets (e.g., credit card, ESG data).
- ESG investing has become an important business driver for new data and analytics for many firms.
Eight individuals participated in the roundtable discussion. The panel included representatives from two global investment consulting firms, and four North American asset management firms with AUM between $25B and $1.5T.