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Data-first thinking

How trusted data will power digital transformation in your trading business

Download the latest Refinitiv white paper to explore the strategic importance of trusted data in managing digital transformation in your trading business.

Why should financial services firms change their relationship with data?

This white paper explores the strategic role that connected data now has across the front, middle and back office. Today, new ways of engaging with historical and reference data are transforming the way trading strategies are built and back-tested. High quality market data is fundamental to successful trade execution and analysis. More and more regulatory compliance relies on high-quality reference data and compliance analytics. And, of course, connected data is essential for most of the innovation happening within trading teams.

Access the full report to find out:

  • How trusted data can support better trading strategies, more efficient executions and analysis
  • New methods of managing data volume and complexity
  • Ways to transform regulatory reporting into business intelligence
  • 6 key data trends within trading innovation
  • $8
    cost of processing, storing, and transforming data for every $1 spent on the data
  • 73%
    of traders who believe that execution management system technology will have an impact on the markets over the next 3-5 years.
  • 53%
    of firms say they are using market data in pricing in the cloud already
  • 98%
    firms who are using market data in machine learning

Read on - Complete this form to open the full report

Firms are using a wide variety of data sets, and they are bringing together data sets for analytics and for AI and ML. They want to work with data sets that are compatible, that can be normalized quickly and easily, that can be safely used together and won’t create errors. Success means a powerful combination of data and workflow that can deliver enhanced efficiency, agility and alpha.

Key content


of firms say identification of incomplete or corrupt records in new data is a challenge


of firms agree that cleaning and normalizing data is a challenge


of data scientists say machine learning is being used at their firms to generate trading investment ideas