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In financial services, data science may be a promising investment

In the financial services arena, data scientists are taking a seat next to traditional business analysts, tasked with finding value in an ever-expanding quantity of data. By delivering smarter insights using analytics and cognitive technologies, data scientists help address problems in investing and trading strategies, portfolio management, regulatory reporting, client service, and more.

May 12, 2017

A blog post by John Houston, Global Workforce Analytics leader.

The employment site, Glassdoor, reports that data scientists once again top the list for top jobs in 2017, with financial services among the top three sectors for demand.1 A relatively new field, data scientists are making their mark by bringing together a wide range of big data and cognitive computing tools to answer questions and aid in decision making. While the description of a data scientist varies greatly, most bring three key distinct skill sets to the table: A statistics or mathematical background, computer programming skills, and the business acumen to turn data into practical insights.2 By bringing those critical skills together, data scientists are able to solve problems that couldn’t be solved before.

It’s true, some of the financial modeling that investment professionals have done over the past 50 years form the basis of the predictive models that data scientists are using today. The difference is the growth of data, computing power, and storage power. Simple spreadsheet calculations have given way to complex algorithms that simulate human thinking through cognitive computing and machine learning.

The financial services industry runs on data from investments, transactions, balances, rates, pricing, and more. Now, it is adapting to new sources of data from internal and external sources, both structured and unstructured. Data scientists should instinctively know which data sets have the most value and how to use the right cognitive tools to deliver the greatest insights.

For example, machine learning can be used to track data from seemingly unrelated events, recognize unexpected correlations, and advise trading decisions. To better observe compliance, analytics can observe millions of transactions that take place each day, pinpoint the instances that require closer attention, and help direct limited resources where they are most needed.

The quantity and quality of data that is available today—plus advances in computing and storage power—offer an unprecedented opportunity for financial services organizations to realize a competitive advantage. As financial services companies adapt and evolve, data science is leading the charge to uncover insights that can lead to better investments and help manage customer relationships more effectively.

How is your financial services organization using analytics and cognitive technologies to uncover value? I’d like to hear from you.


1 George Leopold, “Demand, Salaries, Grow for Data Scientists,”, January 24, 2017.
2 Julia Schieffer, “Data Scientist in Financial Services—A Look at this Evolving Role: Podcast with John Houston,”, March 8, 2017.

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