The financial industry is experiencing technological disruption on a scale never seen before. Access to data is underpinning this rapid transformation.
copyright by www.enterpriseinnovation.net
The financial industry is experiencing technological disruption on a scale never seen before. Access to data is underpinning this rapid transformation. The volume, complexity. and diversity of data available to decision-makers have grown exponentially in recent years – but it has also become more disjointed. According to IBM, 90% of the data in the world today has been created in the last two years alone.
Three factors – data (and alternative data), machine learning and natural language processing – are converging to fundamentally change the way investors across global capital markets derive, consume and analyze the information available to them. In turn, this is reshaping investment firms’ trading strategies and their approach to differentiating business intelligence.
The data challenge – more diverse but more disjointed
The pool of data for financial institutions is vast and fragmented. It includes not only the classic fundamental data most are familiar with (financial results, securities prices) but data generated by business processes (such as commercial transactions), machine-generated data (like satellite information) and data from less traditional sources such as social media.
Investors are also turning to the latter alternative data sets with new techniques focused on finding new, relevant investment signals to capture alpha. Today’s investor may need to make sense not only of security pricing and financial performance but satellite images, supply chain information, ESG factors, and even Tweets.
While alternative data can add depth to an investment decision, history and common identifiers are essential context for translating the data into relevant information for trading books and portfolios.
The finance industry generates vast amounts of data. Bloomberg receives 100 billion market data messages a day and ingests two million new stories a day from 125,000 news sources. Predictive analytical tools powered by machine learning algorithms and natural language processing sift through all this data in order to find and deliver the most critical information to investors.
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The greatest challenge for market participants and financial institutions today is to identify what data sets to use; how to ensure the data sets are high quality, consistent, linked and ready to use; and how to quickly make sense of that data to inform critical decisions.[…]
read more – copyright by www.enterpriseinnovation.net
The financial industry is experiencing technological disruption on a scale never seen before. Access to data is underpinning this rapid transformation.
copyright by www.enterpriseinnovation.net
Three factors – data (and alternative data), machine learning and natural language processing – are converging to fundamentally change the way investors across global capital markets derive, consume and analyze the information available to them. In turn, this is reshaping investment firms’ trading strategies and their approach to differentiating business intelligence.
The data challenge – more diverse but more disjointed
The pool of data for financial institutions is vast and fragmented. It includes not only the classic fundamental data most are familiar with (financial results, securities prices) but data generated by business processes (such as commercial transactions), machine-generated data (like satellite information) and data from less traditional sources such as social media.
Investors are also turning to the latter alternative data sets with new techniques focused on finding new, relevant investment signals to capture alpha. Today’s investor may need to make sense not only of security pricing and financial performance but satellite images, supply chain information, ESG factors, and even Tweets.
While alternative data can add depth to an investment decision, history and common identifiers are essential context for translating the data into relevant information for trading books and portfolios.
The finance industry generates vast amounts of data. Bloomberg receives 100 billion market data messages a day and ingests two million new stories a day from 125,000 news sources. Predictive analytical tools powered by machine learning algorithms and natural language processing sift through all this data in order to find and deliver the most critical information to investors.
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
The greatest challenge for market participants and financial institutions today is to identify what data sets to use; how to ensure the data sets are high quality, consistent, linked and ready to use; and how to quickly make sense of that data to inform critical decisions.[…]
read more – copyright by www.enterpriseinnovation.net
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