In the present era, data has become one of the most important resources available for business organizations and Governments around the world to make many important decisions. Data is more important for financial institutions as most of their businesses run on decisions arrived at through analysis of data. Financial institutions have been doing this for ages but since the arrival of big data and the availability of some really handy data analytics tools, businesses are shifting their focus to take advantage of these tools to dig out valuable information from the vast data resources to arrive at meaningful conclusions for important decision making. This makes the need for data analytics for finance professionals literally indispensable. All professionals in the field of finance must learn these tools and techniques if they want to flourish and grow in this field. This article in a structured manner throws light on all the important aspects of data analytics for finance professionals and its importance for them to gain better work opportunities and move ahead in their careers.
Understanding Data Analytics
In simple terms, data analytics refers to the process of analyzing and converting raw data into an organized form to derive meaningful conclusions from the same. The raw data is transformed using certain tools, techniques, and methodologies into valuable information, which helps organizations predict future market trends, get valuable insights about consumer behavior, solve problems, and improve decision-making in various fields.
It is an extensive field involving several types of diverse data being subjected to various data analytics techniques to arrive at actionable findings. In a real-world context, the scope and importance of data analytics become evident through instances such as the ad promotion of a mobile phone that you searched on Google appearing on various other random apps on your mobile.
This happens because Google tracks your online and offline movements through search data, location data, voice data, and smart device activities to suggest Ads through the use of data analytical tools. As data analytics holds the key to generating better returns for every organization by improving overall productivity and efficiency, it has become a top priority for virtually every business establishment.
In today’s world, one of the most valuable commodities is data, which is also very abundantly available. We often come across an analogy where data is compared to oil, on the same lines it needs to be understood that raw data is like crude oil, which needs to be refined before it can become useful in different ways, hence, raw data needs to be refined through analytics before it becomes useful.
Thus, analytics can be considered as a refinery for the raw data. That is why the role of a data analyst is becoming increasingly essential in business organizations.
Data Analytics in the Field of Finance
Data analytics has become an essential component of the finance field. Given the enormously available and ever-increasing flow of data and the nature of financial transactions becoming increasingly complex, financial organizations that are looking to make well-informed decisions now see data analytics as a vital tool.
Financial institutions, such as banks, investment firms, NBFCs (Non-Banking Financial Companies), insurance companies, and brokerage firms make use of data analytics to gain a competitive edge in their operations by improving their decision-making processes. Finance professionals analyze financial data through the use of statistical and computational techniques, and identify patterns, trends, and inconsistencies to extract meaningful insights from data.
Roles and Responsibilities of Finance Professionals in Data Analytics
Before moving towards the importance of data analytics for finance professionals, let us first take a look at the specific roles and responsibilities outlined in data analytics for finance professionals in an actual financial institution to present a fair idea about their work profile. To begin with, here are the four important roles they typically engage in a systematic order:
- Data Collection: Finance professionals start by collecting financial data from various sources such as market data, transaction history, accounting records, etc., and convert all this data into a common format. They have to ensure that the data is accurate and not fudged, also that it is complete without any holes. This part usually takes some time.
- Data Cleaning and Organizing: They then clean the collected data to make it more meaningful, addressing issues relating to inconsistencies in data and missing values. The collected data is stored in a database and organized in a suitable format for analysis. SQL is a favored tool used for this step.
- Data Analysis: The cleaned data is then analyzed using analytical and statistical tools like Python or specialized software like R to inspect the data. Typically, risk assessment, financial modeling, and trend analysis techniques are applied to draw meaningful conclusions from the data to make well-informed financial decisions.
- Data Presentation and Decision-Making: After analyzing the cleaned data, the results need to be shared in an easy-to-understand manner with the people in the company who make decisions and at times also for other stakeholders. This is done through financial reports, dashboards, and visualizations generally by using Power BI and Tableau software. The results presented help in making important investment decisions, risk management, financial operations, and strategic planning for overall profitable achievements.
The above steps are fundamental in data analytics for finance professionals, ensuring they capitalize on data effectively for the optimum financial performance of an organization.
Next is a list of primary responsibilities that financial professionals are expected to effectively fulfill through the application of data analytics.
Financial Forecasting: Data analytics in finance is used to create precise financial models that help in forecasting future financial performance. This helps finance professionals identify future risks and opportunities to formulate strategies accordingly. Stock brokerage firms make use of such financial models to decide on their investments based on the generated forecast.
Risk Management: By analyzing historical data, finance professionals can identify potential risks and develop strategies to manage and reduce foreseeable risks. Like, the banking sector uses data analytics to assess credit risk using CIBIL scores which include borrowers’ credit history, income levels, and other important financial indicators to determine the likelihood of default. They carry out a risk prediction analysis to determine probable defaults by customers, this helps banks reduce their non-performing assets (NPAs).
Interestingly, with changing data dynamics, Kreditch, a German fintech company has started making use of big data such as online buying habits, location data, social media activities, etc. to profile people and decide on lending by determining which individual is more likely to default on payments.
Fraud Detection: Data analytics for finance professionals can be helpful in identifying unusual patterns or inconsistencies in financial transactions that help in fraud detection. Through analysis of financial data financial institutions can identify suspicious activities to take preventive measures. In India, The Securities and Exchange Board of India (SEBI) by monitoring trading patterns and analyzing market data, can detect suspicious and fraudulent activities like stock manipulation and insider trading to take corrective action.
Cost Optimization: With the help of data analytics, finance professionals can identify areas where resources can be utilized more judiciously to reduce overall costs. This helps an organization stabilize finances and increase its profits. For example, various audit firms use data analytics to optimize their overall financial operations. They reduce their operational expenses and improve profits by analyzing data on project costs, resource allocation, and overall productivity of the firm.
Portfolio Management: Finance professionals use data analytics to identify growth and diversification opportunities to create investment portfolios and for data-oriented decision-making to maximize their profits. They analyze a number of investment options, past performance, and other risk elements before making important investment decisions.
Mutual fund companies are an ideal example, they use various data analytics techniques to optimize returns on their investment portfolios. They analyze past stock data, the financial performance of the company, and macroeconomic indicators to make investment decisions that generate optimum returns with low-risk probability.
Customer Segmentation: Finance professionals use data analytics to categorize customers into various groups based on their demographic data, behavioral aspects, etc. This helps financial institutions in creating products and services as per the needs and preferences of their customers. For instance, credit card companies, use this technique to provide custom-made products and services for their customers.
It also helps them decide on marketing strategies to differentiate between customer groups based on factors such as their spending habits, income levels, residential location, and usage patterns. Based on analytics they create marketing campaigns to target specific customer groups, design card products like a credit affiliated with an E-commerce firm, offer variable interest rates based on CIBIL, and decide credit limits for various groups. This helps to cater to their various customer groups efficiently.
Decision-making: Finance professionals nowadays heavily rely on data analytics for decision-making based on a plethora of data. This helps them make informed decisions. A basic example can be how a bank decides to advance a loan to a customer based on his income, employment, credit history, and so on. Banks use data analytics to find out the creditworthiness of an applicant. It helps them make an informed decision which is beneficial to the banks as it reduces the risk of default and to the customers of the bank as well, as the bank will make steady profits.
Performance analysis: This is another area where data analytics for finance professionals comes in very handy. Data analytics helps institutions regularly analyze the performance of their asset portfolio and important financial investments. This helps them to decide on their future initiatives, which are based on the performance of these existing investments. Investment banks usually evaluate the performance of their portfolio of stocks, bonds, and other investments using data analytics. This helps them make appropriate decisions to optimize returns for their clients.
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Importance of Data Analytics for Finance Professionals
All finance professionals be it CA, ACCAs, MBA Finance, CPAs, CFAs, CMAs, etc. need to learn data analytics to remain relevant in their fields. Highest paymasters like management consultancies, investment banks, and Big 4 Audit companies demand these skills. They expect you to know these skills. So finance professionals must learn these skills or they will see others move ahead in competition.
According to research conducted by management consulting firm McKinsey and Company on the use of data analytics by companies. It was learned that companies using data analytics acquired customers 23 times more than the companies that didn’t, they also retained their customers 9 times more than the ones that didn’t. Understandably, this skill is in high demand, and accordingly, data analytics for finance professionals have some important professional and individual benefits which are listed below:
Progress in Career: For finance professionals aspiring to excel in their field, data analytical skills serve as a stepping stone to move forward in their careers. Every employer nowadays actively seeks candidates with strong skills and aptitude for data analytics, making such skills a valuable asset that will help distinguish you from your peers.
Improved Job Performance: Even in job roles where data analytics is not needed directly, upgrading to these skills offers substantial benefits. A better understanding of data sets allows finance professionals to effectively utilize all the available resources, thus boosting job performance and adding more value to the organization.
Intellectually Stimulating: Data analytics for finance professionals will provide them with an opportunity to learn and grow not just professionally but also personally, as data analytics is like a workout of your mind that can be really stimulating and satisfying due to the challenges it throws at you. The application of data analytics will test and refine one’s intellectual abilities.
Future Ready: Data analytics for finance professionals serves as a crucial tool to prepare for the challenges and opportunities that lie ahead in the future given its ever-evolving landscape. By refining intellectual skills through data analytics one acquires the adaptability required to thrive in the future, it will empower you to stay ahead of the curve and remain future-ready.
Better Lifestyle: Also as these skills will provide impetus to your job growth, it will lead to a higher pay package. With an improved income, you gain the financial means to move to a better lifestyle. This can include access to better medical facilities, education, travel, and the ability to invest in your personal and professional development. In essence, data analytics can pave the way for a more prosperous and fulfilling life.
Essential Financial Data Analytics Tools and Programming Languages to Acquire
There are some important data analytical tools and programming language skills that financial professionals must acquire to do well in this field. These tools and programming languages can be divided into three stages as per the work classification of a data analyst, which are briefly explained below.
In the first stage as data is collected, taking out sensible information from the same is essential. This requires the knowledge of programming languages like Python, R, and SQL which are commonly used in the industry. These are principal programming languages that have exceptional features for data modeling and extraction.
Python: Python quickly creates and manages large data volumes, making it easy to analyze and manipulate intricate data sets. Python also has an extensive set of libraries and tools that help in processing data quickly and effectively. Python is easy to learn and understand with simple syntax features.
R: R is another highly-ranked programming language used for statistics analysis and data mining. It is usually used in data analytics to identify trends, and patterns and create practical models.
SQL: SQL (Structured Query Language) is a must-have skill for every data analyst. SQL helps in retrieving precise pieces of data from a database, allows existing data to be updated and deleted, and adds new data to the existing database. There are also inbuilt powerful tools for performing complex calculations, sorting and filtering of data, and aggregation of large data sets.
In the next stage, data is analyzed to arrive at meaningful conclusions. For this knowledge of Excel and Advanced Excel is essential. Excel is invaluable in the field of financial analysis.
Excel: For data analysts, Microsoft Excel is one of the principal tools and the built-in feature of Pivot Table is certainly one of the best analytical tools available out there. It has many useful capabilities like plugins, macros, V-lookup, mathematical functions etc. that can be used to generate valuable insights in minutes. Advanced skills in Excel are a must for finance professionals aspiring to venture into data analytics.
In the final stage, the entire analyzed data is to be presented visually on dashboards. For this, it is important to acquire expertise in tools like Power BI and Tableau
Power BI: Power BI is essentially a cloud-based analysis service that transforms complex data into understandable insights and dashboards. It is an ideal choice for financial modeling and forecasting as it has a wide variety of in-built financial templates and visualizations. Power BI plays a significant role in data analytics for financial professionals and is a must-learn for all analysts.
Tableau: Tableau is famous for its data exploration and visualization abilities. Tableau is essentially an end-to-end data analytics platform that allows analysis, collaboration, and sharing of big data insights. It helps data analysts to be more productive by saving time and effort as it makes it easier to explore and visualize data.
The above compilation is limited and there are many other useful tools and software out there especially for data analytics for finance professionals, they can also be explored as you advance in this field but this list is sufficient and comprises of most important ones. Here, it is also necessary to bear in mind that a finance professional working in data analytics does not need in-depth knowledge of programming languages, you just require reasonable literacy in the languages mentioned above.
Check Out: Learn these important tools through the Master Data Analytics Course by IIM SKILLS
Job Demand and Salary
As per the U.S. Bureau of Labor Statistics, demand for financial analysts is projected to grow at 8% YOY from 2022 to 2032, it is faster than the average percentage increase for all occupations. In actual figures, about 27,400 job vacancies for financial analysts are projected for each year, on average, over the coming decade. The demand outlook for financial analysts in India is no different.
A simple search on Google reveals that at this moment there are about 10,000 plus vacancies for financial analysts on various job portals in India. With data analytics getting a lot of attention in the financial industry, this demand for finance professionals with these skills is only expected to soar in the coming future.
It is also important to keep in mind that this field is highly competitive, with mid-level or even senior most of finance professionals also pursuing these skills. As for the salary estimates the U.S. Bureau of Labor Statistics report suggests that the median annual wage for a financial analyst was $95,080 in May 2022 and the median annual wage for a financial risk specialist was $102,120 in May 2022.
Similarly, as per Payscale, the average salary of a financial expert with data analytics skills ranges from $54,000 to $84,000 per annum. The Indian salaries for beginners in this field as per the assessment of Payscale is an average of INR 4 lakh per annum, which can go up to INR 9.93 lakh per annum depending upon a range of factors, such as your specialization, geographical location, and company.
However, experienced and highly qualified finance professionals can expect about 15 to 20 Lakhs per annum as a starting salary.
FAQs – Frequently Asked Questions
Q 1. What is the eligibility required to do a course on data analytics for finance professionals?
To pursue data analytics courses for finance professionals, candidates will certainly require some specific educational qualifications and/or experience in the fields of finance, accounting, statistics, or related disciplines, as these are advanced courses and are not recommended for rank beginners. Moreover, candidates with prior experience in basic data analysis and financial planning will have an edge while doing these courses. Additionally, strong quantitative abilities, an aptitude for logical thinking, and above-average communication skills will help students to excel in these programs.
Q 2. What is the cost and time required to do an online data analytics course?
The cost and time required to do a data analytics course online can vary widely. It will depend on certain factors such as the length of the course, whether it is beginner or advanced and the institute offering it. A comprehensive certification program online covering all important modules, live training, internship, and placement support will however take at least six to eight months to complete and it will cost about INR 50,000 plus taxes. Check Data Analytics Master Course, India’s top data analytics online course offered by IIM SKILLS.
Q 3. What is the difference between data science, data analytics, and data analysis?
Data science, data analytics, and data analysis all look much the same due to their overlapping nature of work, but these interconnected concepts are fairly distinct from each other. Data science is an extensive field that includes both data analytics and data analysis as essential components, it uses algorithms, scientific methods, processes, and systems to extract valuable insights and knowledge from structured and unstructured data.
Data analytics, on the other hand, is a broad field that includes a range of activities like data collection, data cleansing, preparing visual dashboards, and application of statistical and financial modeling techniques to gain valuable insights from the available data for effective business decisions. In comparison, data analysis is a smaller field that only focuses on specific actions within data analytics. Data analysis is a subfield of data analytics which is again a subfield of data science, while these three terms are related, they are not the same.
Q 4. Can I do a data analytics course along with my work or studies?
Yes, It is possible to do data analytics online certificate courses without having to leave your present job or regular studies, you can learn these skills side by side pursuing your present commitments and adding valuable skills to move ahead in your career. In fact, it is recommended to undertake these certification courses on the side to upgrade your skills and boost your CV as these skills will act as a springboard to add additional support to your present qualifications and catapult your career.
Q 5. Which is the best data analytics course online for finance professionals?
There are several data analytics courses online offered by various institutes that are suitable for finance professionals. All the institutes have their own merits and shortcomings. You need to choose the course which aligns with your current needs as per your profile. Personally, I will recommend the “Data Analytics Master Course” offered by IIM SKILLS Online. It is ranked as one of the top data analytics courses online and offers one of the most comprehensive programs including almost all the essential aspects of data analytics particularly suitable for individuals with a finance background.
The mode of study is a live interactive classroom format. It is a 6-month course including live training and a 2-month non-paid internship. Further, it consists of 160 hours of lectures, 100+ hours of practical assignments, and hands-on tool-based learning. That is not all, the course additionally provides opportunities to work on 7 plus live projects and 10 plus case studies. This course is highly recommended as it helps master the use of all essential tools like Excel, SQL, Python, R, Power BI, Tableau, and more.
Remember these were highlighted in this article as the most fundamental tools necessary to be learned by finance professionals. The institute also guarantees a 100% assured internship. They offer all this at a very reasonable fee of INR 49990 + GST. Students can also pay in 7 interest-free easy EMIs at INR 8411 per month. Upon completion, students receive a Data Analytics Master Certification from IIM SKILLS, recognized by the Government of India.
Conclusion
A staggering volume of estimated 328.77 million terabytes of data is generated every day. It is estimated that about 120 zettabytes (1 zettabyte = 1 billion terabytes) of data is likely to be generated in the year 2023. And to top that, just imagine that 90% of the world’s entire data was generated in the last two years. This shows the abnormal rate at which data is being generated all around the world currently. This is commonly referred to as big data. All big companies are looking to lay their hands on this data. There is no point in having access to such humongous amounts of data if the same cannot be converted into valuable information that can be used to benefit organizations financially.
This is where the role of data analytics becomes a fundamental component for the growth of every organization. Financial institutions by nature, work with large amounts of data which they convert into meaningful concise reports to make well-informed decisions. Thus capability in data analytics for finance professionals becomes pretty much indispensable. So not acquiring data analytics can put you behind your competition. This article has shed some light on this important factor by providing all the necessary information required for aspirants who are still undecided on their future course of action and acts as a guiding factor to reinforce the importance of data analytics for financial professionals by providing some meaningful insights in this field.
