A better way to manage your investment data. For everyone from start-up fund managers to global investment institutions.
Take control, make more informed decisions and get ahead. With all of your investment data hosted in one place, LUSID removes the complexity, reduces your operating costs and gives you better access to your data.
Clients have always been trapped in a battle with vendors over ownership of their data. Where once they may have allowed others to control their data, expectations are evolving. Data owners are now demanding greater flexibility in how they view and access their own investments. This typically manifests as requests for an explanation of results, or for data in a given format or view. Consumers of this data can be both internal and external. Examples could include a segregated mandate looking for better access (custom feeds) to their data, or a fund manager looking to use BI tools or to experiment with data science techniques.
LUSID is your own platform that allows multi-view representations of your data, with API access to that data and a set of Software Development Kits (SDKs). Programmatic access to data is simple to set up. LUSID has Excel and Python as first class clients in addition to a web based client which means your most frequent queries can be answered without the need for a custom feed. LUSID also allows for segregated data enclaves and entitled access.
LUSID can be accessed through SDKs and APIs so that investors can integrate their own systems and explore the data using API calls, Web components or Excel.
LUSID’s ‘derived portfolios’ allow clients to clone data for what-if portfolio testing.
LUSID ensures that entities can be entitled within a ‘scope’, enabling a copy (can be a linked copy using derived portfolios) of the data to be made available to only the investor.
Entitlements ensure the investor can only see the cut permissioned to them (e.g. signed off month end results). This provides investors with safe and convenient access to explore their own data without having to resort to error prone data extracts.