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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.
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.
Solution
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
Data exploration tool in action
Data scientists, developers, quants and fincoders can quickly move through five steps.
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.
Access a rich catalogue of sample data, including sentiment data, historical price data, machine-readable news and many other differentiated financial data.
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.
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.
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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