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Financial Analysts Should Embrace Big Data

Financial Analysts Should Embrace Big Data

Big Data, advanced analytics, data science etc. All areas that seem complex and unattainable as a career path for the average finance graduate/professional. Why is that you ask? Well, because it requires the ability to work with substantial amounts of unstructured data, to build complex mathematical models and to test and validate the models continuously using advanced statistical analysis. Sounds like something you need a specialist or even a scientist for, right? At the same time, simple analysis like variance and trend analysis can be automated in your BI tool to be shown automatically so the standard financial analyst is being squeezed from both sides. However, there’s no need for the financial analyst to be afraid of this squeeze mainly because the talent pool at data scientist is quite scarce hence companies need to address this by bringing the financial analyst in play to do more advanced analytics.

If you can’t build mathematical models or do statistical analysis how would this work?

To imagine how this could work think of the area of building a website. This entails coding, graphical design and content creation just to name a few of the primary areas. Let’s say you’re a writer and want a website for your blog. Most likely, you’re not capable of either coding or graphical design yet these skills are not needed either as there are several providers of ready-made websites where all you need is to choose between a few graphical layout options and press confirm. Then you’re ready to start publishing your content without having coded a single line or designed a single-color scheme. Transfer this idea to Big Data and advanced analytics and all you would need is for your data scientist to build several mathematical models that run in the background and a simple and easy-to-use front end on top where the financial analyst can load in all her data. Then as the models come up with answers the analyst will do what (s)he does best which is to performance manage which of the models that are best at predicting the actual outcomes. The predicted data compared with the actual data can again be loaded into the model where the data scientist has also built statistical models to run in the background to help choose the best models for predicting the actual outcome. This means that you only need to hire a few data scientists and designate your financial analysts to run all the needed simulations that will spawn a multitude of new insights you can use for better decision-making in the company.

What’s required from the financial analyst?

We have concluded that financial analysts with the right setup can do the work of what data scientists do today but still it does require a few things from the financial analyst to be successful.

  • First, (s)he needs to gain a basic understanding of Big Data and advanced analytics so (s)he’s able to communicate and to some extent speak the same language as the data scientist building the models.
  • Second, (s)he needs to be able to read and understand the results produced by the models and the high-level reasons for coming up with the specific prediction. Otherwise (s)he won’t be able to convey the results and insights to the business.
  • Finally, (s)he must understand the starting point and be able to distinguish between when Advanced Analytics or simple analysis is needed. With starting point is meant the business issues where the complexity might warrant in-depth advanced analytics as without this understanding (s)he won’t be able to interpret any results.

So, what do you think? Is it doable for financial analysts, finance business partners or even senior accountants to venture into Big Data and advanced analytics without a Ph.D. in same? If you yourself have had any exposure to this field I would love to hear your story and the business insights you’ve managed to generate?

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