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Data exploration tool

Meeting data scientists’ need for speed

Giving data scientists, quants and developers free, easy and intuitive access to high-quality Refinitiv data sets and notebooks.

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Problem and opportunity

Data management is challenging - especially with different databases of records, varying degrees of data quality and multiple delivery mechanisms.  The task of aggregating, normalizing and correlating data is both time and resource intensive. 

The increasing volume and depth of data available to firms is matched by the growth in the number of data specialists hired to wrangle and analyze it.

Product screen shot of Data exploration tool featuring dataset categories and a search bar


Data experimentation with fewer hurdles

Refinitiv Labs set out to solve for this challenge by building a new data experience, so data scientists, developers, quants and fincoders could easily find, evaluate, access and use data. 

As part of the design thinking process, Refinitiv Labs listened to our customers’ needs to create a tailored experience for a data specialist’s workflow. 

The team interviewed over 30 data scientists in person through the development phase and gained additional insight from a Machine Learning survey of over 450 data scientists and financial sector professionals. 

The result is the free-to-use data exploration tool – a single interface that hosts a collection of Refinitiv datasets, services and documents, with pre-built Jupyter Python notebooks.

The rich catalogue of Refinitiv data in the data exploration tool

The rich catalogue of Refinitiv data in the data exploration tool

Data exploration tool in action

Data scientists, developers, quants and fincoders can quickly move through five steps.

1. Find

Find data sets, services and notebooks to help you get on with your work. Spend less time searching or dealing with permissions, and more time on building.

2. Access

Access a rich catalogue of sample data, including sentiment data, historical price data, machine-readable news and many other differentiated financial data.

3. Evaluate

Jupyter notebooks come with every data set, with examples and services. These can be previewed, opened and run in a hosted environment or downloaded to be used locally.

4. Use

Use ready-made and tested Jupyter (Python) notebooks with data structure & samples to build your model. Code can run on hosted AWS instances free of charge.

5. Build

Solve real-world financial data challenges with real world data. Examples include Stock sentiment analysis, FX rate forecasting and Stock price prediction.

A collaborative approach

Refinitiv Labs takes a collaborative, customer-focused approach to solving for some of the most challenging problems in financial markets today by combining customer feedback, extensive data capability and exceptional partner technologies.

Collaborating with our customers:

  • Working with our customers towards a shared goal of providing easily accessible datasets to the data science, developer and quant communities

  • In-person interviews with over 30 data scientists working for the world’s largest financial institutions

  • Survey of over 450 data scientists and financial sector professionals on their data strategies, challenges and ways of working

Refinitiv partner technologies:

  • Amazon Web Services (AWS)

  • Python 

  • Jupyter 

  • Google BigQuery

  • Elasticsearch

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