Finance Manager, Investment Banker, Financial Advisor, Economic Analyst, Financial Analyst, Quantitative Analyst, Portfolio Manager, or any other job profile, it does not matter what role you play as a Finance professional, Data Analytics for finance professionals is an indispensable tool.
And the intrinsic value of data is not confined to the financial space, because data is the most powerful asset to any company or organization if they know how to use it and mine the value out of it.
That’s precisely what Data analytics does. It helps you analyze data using maths, statistics, and other analytical tools along with Artificial Intelligence to extract patterns, trends. And these insights from the data help you answer questions more efficiently.
You Already Know the Basics!
As a Finance professional, you are already involved in the analysis of data on the fundamental level. Reading reports analyzing patterns, and helping the decision-makers and stakeholders make informed decisions.
But from the angle of Data Analytics, you have just scratched the surface in terms of extracting the value out of the data. The financial data is just a tiny part of the entire dataset. An organization produces a mixture of different types of data.
Just imagine the volume and variety of data that the company has access to thanks to the digital revolution and the rise of social media.
Do you realize how drastically the quality of your decisions will improve if you efficiently analyze the data and incorporate it into your decisions?
Not to mention the boost to your career and your paycheck!
Data analytics is one of the most in-demand skills in the industry. And the demand is on the rise so it might be the right time to upskill or upgrade your skillset to complement your goals.
So now that you have realized the value of Data Analytics let us explore Data analytics a little more.
What is Data Analytics?
Data Analytics is the process of analyzing data with the help of mathematical tools, statistics, and complex computational tools based on Artificial Intelligence to draw insights into the trends and patterns and use them to improve performance and decision making.
Although the definition provides the basic idea about the scope and utility of data analytics, data analytics has several different components. Learning these components is crucial to understand how Data analytics works.
Types of Data Analytics
Data analytics is of four types-
This is what we meant when we said “Finance professionals are fundamentally involved in Data Analytics”.
Looking at data and identifying trends and patterns to determine what the data suggests is called Descriptive analytics and is the first step in Data analytics.
We have so much more to gain from the data than just patterns and trends. Other forms of Data analytics help you extract the real value from the data and utilize it to make better decisions.
Diagnostic analytics is the next step after descriptive analysis. While Descriptive analysis recognizes patterns and certain trends from the data, Diagnostic analysis uses this data to determine the reason behind the particular pattern or trend.
Descriptive analysis answers the question “What happened?” and Diagnostic analysis answers “Why it happened?”.
For example, if a set of reports suggest that sales of wired earphones are on the decline in the market. Diagnostic analytics will analyze the data to determine why the sales of wired earphones declined.
Predictive analytics of the data predicts the future trends of the market based on Descriptive and Diagnostic analytics. So it is important to understand that Predictive Analytics cannot be done before Descriptive and Diagnostic analytics.
Because Predictive analytics uses the data from the Descriptive and Diagnostic analytics and correlates this data from other external datasets to determine the probable trends in the future.
For instance, after Diagnostic analytics reveals the reason behind the decline in sales of wired earphones. Predictive analytics will determine the future trends of wired earphone sales. And that will help the company make better decisions about its new products or strategies in the future.
Prescriptive analytics is the most advanced form of Data analytics. It uses Machine Learning and deep learning to analyze all the data available. And then suggest the right course of action or strategies to optimize performance.
This branch of data analytics requires high computational power, because to predict the right course of action, different complex mathematical models and calculations are required to extract the relevant information.
For instance, in the above example, Prescriptive analysis may suggest halting production of wired earphones in the next quarter and supply the inventory stock into the market for sales. It might also suggest the company to launch new wireless earphones in the next quarter.
The examples used to explain all the four types of Data analytics are trivial and are based on simple data. But the data that a Finance professional has access to, is much more complex than a simple sales report suggesting a decline in sales of wired earphones.
And greater is the complexity of the data, the greater is the amount of useful information embedded into it. Therefore, potential value that can be extracted from the data using Data analytics is even higher when data is complex.
Datasets, in general, will increase in complexity over time as the companies get bigger. The consumer base grows, and the companies start diversifying their product portfolios.
So in essence Data analytics for finance professionals is a blessing in disguise. Because this skill will grant you the ability to perform even better as the complexity of your work increases.
How it helps?
Normally, the efforts of a Finance professional as the company grows increases exponentially. And their productivity and the quality of their work decline under the pressure of the additional responsibilities.
But with access to Data Analytics and Data science, finance professionals can increase the quality of their decisions. And improve the operations of the company even further as they are introduced to more complex data.
It’s like being a computer that can decrease the amount of time it takes to solve problems when you increase the complexity. For example, this computer can solve 12 X 67 very fast but it can solve 45611 X 768856 even faster.
It’s almost unbelievable how much value Data Analytics can add to the skillset of a finance professional. And how much value finance professionals can create for their company if they have access to data analytics.
As a Finance professional you possess complex problem-solving skills, good accounting, and a fundamental understanding of the way businesses work. This makes you the perfect candidate for learning Data analytics.
And if you have technical skills like Programming in addition to your financial skills, take a look at our article on Python for Finance.
Learning Data analytics also offers you the option to explore new opportunities in the domain of data analysis and data science. There are a plethora of opportunities available for finance professionals as Data managers if they know data analytics.
Where to Start?
The first step to learning something new is to learn the basics. Basics provide the foundation, that you require to build a concrete understanding of the subject along with the practical application of the theory.
First off, try out basic courses on Data Analytics for Finance professionals specifically designed for people like you. It will help you build your concepts from the ground up and prepare you to tackle advanced data analytics.
And once you learn the basics involved, try working with data analytics tools like Google analytics. Start implementing what you have learned so far. Practice will help you polish your skills up to industry standards.
The world around us produces an incredible amount of data every second. And it’s more than just data, it’s the most powerful resource to anyone who can extract the value from this pile.
Data analytics is one of the most important skills to have in this digital era where customer behavior and buying habits are invaluable resources to companies.
As a Finance professional it is the perfect time to add Data analytics to your skills and be a part of the data revolution.
We hope this article on Data Analytics for Finance Professionals helped you understand the value of Data Analytics.
What are your views on Data analytics and its prospects as a finance professional?
We would love to know your views in the comments below!