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Financial Analysts vs Data Scientists - Darwinism In Action?

 

"Do you believe that data scientists will slowly supplant traditional finance roles (e.g. Financial Analyst) in most organizations?"

This question was asked at a recent webinar, now available on-demand:

"How ‘Modern Modelers’ Will Take Over the World of Financial Analysis"

Please add your thoughts about it below. Thanks!

Answers

Topic Expert
Alan Hart
Title: Consultant
Company: Pacific Shine Group
(Consultant, Pacific Shine Group) |

I have written about this topic in my professional practice and the following text is a summary of my opinion:

There is a legitimate concern that with the increased complexity and advancement in data mining and analysis tools, data scientists are going to replace traditional financial analysts and eventually both financial analysts and data scientists are going to be completely replaced by automated processes.

My observations and experience with large finance organizations shows that data scientists and financial analysts are still going to be in demand, especially in large organizations where data analysis needs are complex and where the majority of data queries and reports are custom built for specific users or functions. The tools and expertise required to extract and present the data far exceed the knowledge and experience of the average employee or manager and the conclusion of course is that these positions are here to stay and perhaps even expand.

This observation changes in small and medium sized businesses. As it turns out, these are companies that never had a great number of financial and business analysts on staff and many still have few or even none. With the availability of analytics software tools, some of which can interface with the company’s accounting or ERP software, many employees and managers in such companies can gain access to specific data and in a format meaningful to them.

A good example is the business intelligence gained from analysis of actual financial performance as evidenced by accounting data extracted from the accounting or ERP software for recently closed accounting periods or a series of historical periods (months, quarters, years). This data is compared in the analytics software with similar data extracted from the planning and budgeting software, representing future accounting periods.

The result is a clear picture of how the company performed against its approved budget. The analytics software can be set up to give its users key data in pre-defined formats, either graphical, tabular or both. Users can slice and dice the data in any imaginable way in order to extract views of specific reporting entities, sales territories, product lines, customer classes, etc., for any desired time frame.

The information gained from this analysis, which can be performed as soon as an actual accounting period is closed, is intended to convey key data to management, who are tasked with making business decisions based on this data. Since the analysis should never be delayed in a well-executed process, management can continually steer the organization in the right direction, while minimizing business errors and wrong decisions.

Since this process and its underlying software tools are now available to small and medium sized companies, the software and its users jointly become the “data scientists”. As for large organizations and their much more complex needs, it looks like data scientists, business analysts and financial analysis will continue to enjoy job security.

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