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BNY Mellon Report Explores Big Data in Finance, in the 21st Century

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A report recently published by BNY Mellon – it’s available to anyone who request it from them – takes a look at big data, its definition and its potential, and “sketches its transformational influence on the 21st century global financial system, mainly from an asset management perspective.”

The report was written by Jack Malvey, chief global markets strategist for BNY Mellon Investment Management and director of the BNY Mellon’s Center for Global Investment & Market Intelligence (CGIMI); Ashish Shrowty, managing director, BNY Mellon corporate technology; and Lale Akoner, investment analyst, CGIMI.

Acknowledging that “the swift amplification of the big data din may foster doubts by some seasoned capital market veterans,” the report suggests that “over the long run, big data may come to be viewed as the successor to the internet in terms of revolutionary impact.”

Among other assertions, the report connects financial transactions with information: “Asset management and the entire financial services industry are extensions of the information and knowledge businesses, operating under a continuous state of uncertainty.”

Among the report’s predictions:

– Economic releases such as GDP, inflation, and industrial production may become more accurate (subject to less revision) and less surprising thanks to advance signals propagated via big data methods.

– Next-generation analytics (especially more rigorous time series, correlation, graphical, topological, and scenario analyses) will emerge.

– Intelligence garnered from big data techniques will have a profound influence on public and private sector users of capital markets as well as on consumers.

– Through visualisation-aided smart syntheses often in the burgeoning era of “dashboards,” big data will expand the “assimilation range” of even existing information by capital market professionals.

But along with these positive suggestions, comes a warning that: “As with any innovation, there will be constraints in the form of real-time data quality, privacy/confidentiality, transmission speed, overwhelming volumes, data scientist shortage, and security.”

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