Investment management relies heavily on data analysis to inform decisions. Effective data models are critical to enable advanced portfolio analytics. Key requirements for investment data models include handling time series data, linking multiple data types, and flexibility to incorporate new data sources. Achieving high quality data with integrity and governance is essential but challenging.
Time series data critical for investment analysis
The ability to analyze trends over time is fundamental for investment management. Time series data, with accurate historical records, is vital for techniques like performance attribution and risk modelling. However, effectively managing temporal data at scale poses engineering challenges.
Relational data links investments to attributes
Understanding the linkages between investments, issuers, benchmarks, and related metadata enables deeper portfolio analysis. Relational data models allow connecting disparate datasets to uncover insights.
Investment management leverages data science techniques which rely on flexible, high quality data models. Prioritizing governance, integrity and extensibility of investment data platforms provides a foundation for advanced analytics.