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enterprise data management, reference data management,market data management

GoldenSource 101: Market Data Management

What is market data management?

Market Data Management is the process of ingesting, validating, enriching, and curating market data. This is different from market data administration, which involves overseeing the draw-downs and usage of market data to ensure they conform to the contractual restrictions of the data provider.

What are some types of market data?

Market data is primarily financial instrument pricing data, such as end-of-day pricing, intra-day pricing, ask/bid/mid/close pricing, curves and surfaces for derivatives, and time-series data, just to name a few.

What is market data for?

Market data allows financial professionals to see not only the price of a given instrument at a moment in time, but changes and trends over time as well, via time-series. This allows people to more effectively analyze the data point itself, and the points that interact with it, to make better investment decisions and comply with regulatory requirements.

What is a market data platform?

A market data platform is the system in which financial instrument pricing data is ingested, managed, stored, and interacted with via a user interface for analysis and investment decision making. Market data also needs to be related to instruments and issuers, which requires identifiers and hierarchies as part of the data management platform capability.

Why do I need it/why is it important?

Front office, risk and finance models and their associated calculations have traditionally been managed separately from the data that feeds them. This is changing, because preparing data for financial market models – validating, calibrating and transforming market data – requires an understanding of how that data interacts with the market models and calculations that run through them.

What should I look for in a good one?

What you’ll need is a market data management solution that provides you with robust coverage of market data types, attributes and definitions, which can be achieved with an industry standard data model. You’ll also need out of the box rules and workflows, as well as integration capabilities with down and upstream systems, both internal (such as a data warehouse) and external (such as data vendors). Finally, the solution will need to be accessible to non-IT personnel, with the ability to easily construct meaningful, actionable extracts of the data and UI workflow screens.

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