Data Analytics is the process of analyzing raw data sets to get meaningful information from the data. Data Analytics is widely used in organizations with massive data sets with the help of tools and software. Data Analytics helps generate insights from the data and help the organization make data-driven decisions that can increase revenue, improve sales & marketing campaigns, customer service, and many more.
Data analytics can be segregated into Quantitative and Qualitative analysis. Quantitative analysis involves numerical data like orders and sales with quantified variables, whereas Qualitative analysis analyzes non-numeric data like text, audio, images, and many more.
This blog talks about the critical aspects of understanding the role of a Finance Data Analyst: importance, roles, responsibilities, skills, educational background, career opportunities, and tools. It starts with a brief introduction to Financial Data Analytics and the importance of Data Analytics in the field of Finance before diving into the nuances of a Finance Data Analyst.
What is Financial Data Analytics?
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Financial Data Analytics is a field that provides an in-depth analysis of a company’s financial data. It helps organizations to gain insights into their financial data and implement indicative action to improve performance.
Financial Analytics provides a new perspective on financial data. Data Analytics in the finance sector plays a crucial role to forecast the future, calculating profit and sales, developing methods to improve business, and many more.
A Financial Data Analyst is the person who is responsible for analyzing and presenting the financial data to the higher management with the help of dashboards and visualization, helping the organization to make informed decisions.
You might also love to read about Clinical Data Analyst.
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What is Data Analytics?
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The science of analyzing raw data to draw conclusions about it is known as Data Analytics. Many data analytics techniques and processes have been automated into mechanical processes and algorithms that operate over raw data for human consumption.
Data Analytics is a diverse term that covers a wide range of data analysis techniques. Data analytics techniques can be applied to any type of information to gain insight that can be leveraged to improve things. Data analytics techniques can uncover trends and metrics that would otherwise be lost in a sea of information. This data can then be used to optimize processes in order to increase a company’s or system’s overall efficiency.
For example, Manufacturing companies frequently record the runtime, downtime, and work queue for multiple machines and then analyze the data to better plan workloads so that the equipment operates at peak capacity.
What is the Importance of Data Analytics for Finance?
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Financial Data Analysis provides better visibility to the business. A Finance Data Analyst can help the industry by identifying the company’s financial position and suggesting any course correction measures. Below are the few critical factors that reflect the importance of Financial Data Analysis.
- Financial Data Analysis helps business people to take important decisions by providing information on time, thereby increasing the value of the business.
- Financial Data Analysis helps businesses do efficient financial planning by looking at the insights from the data and forecasting the models to leverage the business.
- Financial Analytics help the business manage the organization’s assets, such as machines, inventory, cash, etc.
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Financial Analyst vs. Data Analyst: Which one to choose?
Consider a career as a Financial Analyst or Data Analyst if you are a student or young professional who is proficient with numbers, analytical skills, and a problem solver. Financial Analysts use financial data to identify trends and forecast the future, assisting their employers and clients in making the best investment decisions.
Data analysts play a similar role, with the significant difference being that these professionals analyze the data that may or may not be relevant to investment decisions. A data analyst, for example, might research sales figures, advertising effectiveness, transportation costs, or wages versus productivity.
Both Data Analyst and Financial Analyst are excellent careers: the income potential is high, the work hours are manageable (40 to 45 hours per week on average), and the job market is expanding. The distinctions between the two jobs are mostly hazy, but the most significant difference is that a financial analyst’s typical tasks are much more involved with the investment markets.
Financial Analysis is a career to consider if you are interested in investing and keeping up with Wall Street but want to avoid the volatile environment of investment banking and trading. If, on the other hand, you enjoy working with numbers while also being interested in computers and technology, you are likely to have the skillset and interests required to become a good Data Analyst.
What are the Roles and Responsibilities of a Finance Data Analyst?
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The Roles and responsibilities of a Finance Data Analyst vary from job to job. However, here are the few typical roles & responsibilities that an employer seeks from an Analyst:
- Create and maintain spreadsheets and dashboards to generate insights from the data and aid in their financial models.
- Create and develop financial models to support forecast, planning, and predicting the financial outcomes of the business.
- Create a suggestive approach to improve the performance by analyzing financial results, trends, forecasts, etc.
- Help the company do budgeting and expense planning and recommend ways to improve to higher management.
- Research the live market to perform comparison analysis among competitors to support internal financial analysis
- Adequate technical knowledge to quickly adapt to the market change.
Understanding the Requirements for a Finance Data Analyst
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To pursue a career as a Finance Data Analyst you need to check off a few requirements, like essential skills, programming knowledge, knowledge of tools, educational background, and more as talked about in the next few sections.
Finance Data Analyst Requirements: Skills
For a Finance Data Analyst, Analytics skills are most important. However, there are specific skills that employers look for in a Finance Data Analyst. Here are some of the essential skills:
- Research Skills: Researching is the key for a Finance Data Analyst, and it is an integral part of their day’s job. Research is needed to validate the data because there is no scope for the analysis without the proper, correct data. Hence, this skill requires an understanding of the problem and finding out the answers.
- Analytical Skills: This is the most prominent skill an employer looks for in a Financial Data Analyst. Analytical skill means being able to demonstrate logical thinking in black and white. Analytical skill develops over time while working and it requires good database and Excel knowledge.
- Mathematical Skills: Financial Analysis requires a lot of mathematical operations on the data. Hence, a Financial Data Analyst is expected to have strong mathematical skills and is preferred to have a mathematical background.
- Technical Skills: Financial Data Analysis requires a lot of calculation, and hence a person having good technical skills can solve the problem quicker than manually. There are lots of programming languages and tools available in the market that aid in Financial Data Analysis.
- Focused Mind: Financial Data Analysis requires utmost attention as a slight mistake can heavily cost the organization. A wrongly typed amount or inaccurate mathematical function can lead to a balance sheet mismatch, and a slight change in the value can lead to a significant difference in conclusion.
There are several tools in the market that help Data Analysts to explore the data. Here are some of the popular tools:
Excel
Excel is heaven for the Finance world. Excel has excellent capabilities like Pivots, V-Lookup, and Mathematical and analytical functions that generate insights in a minute. Excel is an integral part of Microsoft Office and is commonly available in every industry/organization. Finance professionals heavily rely on a number of its features which can analyze complex data sources. Excel is nearly a database with rows and columns and a vast list of mathematical and analytics functions.
Excel is one of the versatile tools available in the market and has several plugins that can enhance its efficiency.
BI tools are another great set of Financial Data Analysts tools that allow finance professionals to extract meaningful data from the raw data sets. Popular BI tools like Microsoft Power BI, MicroStrategy, Tableau, and Qlik Sense features data cleaning and data modeling capabilities that identify Financial trends, Sales forecasting, area of improvement, and many more.
R and Python
R and Python are leading programming languages with exceptional features for data modeling and extraction. Professional users can use these programming languages to build out-of-the-box capabilities to perform customized and complex statistical analyses.
With programming language, users can build algorithms that perform regression analysis, classify data clusters, and more.
Finance Data Analyst Requirements: Programming Languages
Several programming languages can help Financial Data Analysts perform analyses on the data, and we have listed a few popular languages that are widely used in the industry.
- SQL: SQL stands for Structured Query Language, and it is not a programming language; instead, it is a language that works with databases and tables. It is widely used by Financial Data Analysts as it has a vast library of financial functions that can be used to aggregate financial metrics, and it works well with structured data.
- Python: Python is the most popular language used in the Finance world. It has made revolutions in the Finance world with its exceptional library of statistical and mathematical functions.
- Java: Java is a multi-purpose object-oriented programming language and is mainly used to create Desktop applications. Java is considered one of the most secure applications and is extensively used in the Financial and banking sectors.
- R: R is ranked as the top programming language for statistics and data manipulation. R helps discover and maintain the relationship between nodes, mainly used for predictions and forecasting.
- Matlab: Matlab is the acronym for Matrix Laboratory and is widely used in the Finance world. Matlab has extensive support for financial algorithms, data manipulation, data functions, and plotting and is very well suited for cross-platform integration.
Finance Data Analyst Requirements: Educational Background
The Education background needed to become a Financial Analyst includes one or more from below:
- A Master’s degree in Finance, Accounting, Economics, or a related field.
- A Bachelor’s degree in computers or related fields.
- Five years of prior experience in Data Analysis, Financial Planning.
- High proficiency in Financial Modelling techniques.
- Related certifications will be an added advantage.
- Financial Analyst requires strong quantitative skills, expert problem-solving abilities, adeptness in logic, and above-average communication skills.
Finance Data Analyst Requirements: Salary and Job Openings
The Job openings and Salary for a Finance Data Analyst vary over time. However, there are certain averages that you can consider when choosing the Finance Data Analyst as your career.
As per Glassdoor, the average salary for a Finance Data Analyst in India is 7395 USD per year.
The average salary for a data analyst with financial analysis skills is around $75,000, according to Payscale.com, with the low end of the salary spectrum at $54,000 and the high end of the salary spectrum at $84,000.
Understanding the Career Roadmap of a Finance Data Analyst
Whether it is a small or large industry, it requires a financial Analyst to maintain its finances. In the next section, you will get to understand the career map for a Finance Data Analyst according to the different subject areas:
Domains
- Banks: Banks include Commercial Banking, Investment Banking, Trading, etc.
- Public Accounting: Tax and Advisory, Due Diligence, Treasury, etc
- Institutions: Portfolio Management, Research, etc.
- Corporates: Treasury, Corporate Development, Financial accounting, etc.
Career Path
A Financial Analyst may have the following career paths:
- Data Analyst
- Business Analyst
- Corporate Strategy Analyst
- Credit Analytics
- Social Media Analytics
- Machine Learning Analytics and many more.
Conclusion
In this blog post, you have learned about Financial Data Analysis in detail and got an idea about what a Financial Analyst can do to improve the business.
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Vishal Agarwal is a Data Engineer with 10+ years of experience in the data field. He has designed scalable and efficient data solutions, and his expertise lies in AWS, Azure, Spark, GCP, SQL, Python, and other related technologies. By combining his passion for writing and the knowledge he has acquired over the years, he wishes to help data practitioners solve the day-to-day challenges they face in data engineering. In his article, Vishal applies his analytical thinking and problem-solving approaches to untangle the intricacies of data integration and analysis.
