What is Financial Data and How to Use in 2022?


Financial data is what investors use to analyze a company's economic and financial health. This is how they decide whether a stock is overvalued or undervalued. Such studies are often referred to as fundamental analysis.


Financial data comes from a company's income statement, balance sheet, and cash flow statement. Investors use this data to compare companies, create ratios (Price-earnings, Debt-equity, etc.), and make informed decisions about a particular investment.


The importance of financial data in making company decisions cannot be overstated. Financial data refers to crucial bits of information that help to paint a clear picture of an organization's or business's financial health. Such information can be collected from traditional sources including a firm's balance sheet, income statement, and cash flow statement, as well as alternative data sources from outside the company, sometimes known as "external data."


These data sets are analyzed by investors, managers, and regulators for a variety of reasons. Investors, for example, use financial data to determine whether or not to invest in a company and to watch its performance afterward. Internal management teams, on the other hand, examine financial data to assess business performance and assess the success of their objectives.


This article will go over the many forms of financial data, their applications, and the tools for managing and analyzing financial data.


Types of financial data and use cases

Traditional financial data and alternative financial data are the two primary categories of financial data.


Data from the past

Financial data from traditional sources, such as financial statements, press releases, and SEC filings, is referred to as traditional data. Assets, liabilities, equity, revenue, expenses, and cash flow are all examples of traditional financial data.


The assets of a firm include all of its assets, including real, personal, tangible, and intangible property. Liabilities, on the other hand, are a company's financial responsibilities, or what it owes to others.


Income, on the other hand, refers to the rise in wealth resulting from sales and investments. Expenses, on the other hand, refer to a company's operating costs.


Cash flow reveals where a company's income and expenses originate from, whereas equity refers to the amount of money left over after all of a company's assets are liquidated and all of its debts are paid off.


Data from other sources

Many investors and analysts are increasingly turning to alternate data sources to acquire a distinct picture of investment prospects in today's data-driven environment. According to Forrester's study, 56 percent of decision-makers said their companies are improving their ability to access external data.


According to Grand View Research, the alternative data industry was worth $1.72 billion in 2020, and it is predicted to increase at a CAGR of 58.5 percent from 2021 to 2028. These findings suggest that alternative data is gaining traction and becoming a significant factor in corporate choices.


External data from non-traditional sources, such as financial transactions, web data, and geolocation data, is referred to as alternative data. Alternative data frequently provides more granular insights into a company's performance and financial statistics. Alternative data was previously discussed in one of our prior posts.


Individuals, corporate processes, and sensors all generate alternative data. Web data like page views, click-through rates, bounce rates, and search data are examples of alternative data. Social media data, geolocation data, credit card transactions, email receipts, product reviews, and satellite images are all examples of alternative data.


Web scraping, raw data acquisition, and third-party licensing are the three main methods for obtaining alternative data.


Web scraping is the process of extracting meaningful unstructured data from web pages using software tools. Investors and analysts can then interpret the data by processing it into organized, readable formats like CSV and JSON.


Raw data can be received from a variety of sources, including sensors. Alternative data is collected as a raw data stream that must be cleaned and processed before being analyzed.


Alternative data can also be obtained from third-party licensees. These intermediaries get raw data from other organizations, such as credit card and POS transaction data. The collected data sets are subsequently processed into usable formats and sold to investors.


How to manage financial data

Financial data management is the process by which businesses use specialized technologies to measure and evaluate their financial data. Let's take a look at what financial analysis entails, as well as the many tools for collecting, analyzing, and managing financial data.


What is the definition of financial analysis?

Financial analysis is the process of assessing a company's performance by evaluating and analyzing its financial data. It entails a thorough examination of a company's financial accounts.


Internal financial data analysis can be done by a firm's management to review past trends and make future decisions based on the financial data of the organization. Corporate financial analysis is the term for this type of examination.


External investors, on the other hand, undertake investment financial analysis to see whether a business is worth investing in.


Fundamental analysis and technical analysis are the two main types of financial analysis. To estimate the worth of a company, fundamental analysis is used to calculate ratios from its financial data. Fundamental analysts research a wide range of aspects that can influence a company's worth.


Technical analysis is based on data trends gathered from a company's stock trading activity. As a result, technical analysis is concerned with examining price movements and identifying patterns and trends in order to forecast future activity.


Vertical and horizontal analysis, leverage ratios analysis, growth rate analysis, and profitability analysis are some of the other financial analysis approaches. Liquidity, efficiency ratios, and cash flow are also evaluated by financial experts.


Financial data management

Financial analysis is frequently carried out with the help of software such as Microsoft Excel or specialized financial data management software. When working with huge data sets, analysts must pay close attention to detail and develop reasonable assumptions based on the data.


Analysts can track a wide range of variables with some financial analytics systems, including income, expenses, and profits. Furthermore, these solutions make the data collection and preparation procedure easier. Some solutions can also assist firms to generate insights that can help them enhance their operations.


Extracting relevant insights from alternate data can be a difficult procedure. Alternative data sets are frequently huge and complex, and processing them necessitates the use of powerful technologies and competent data scientists. Nonetheless, with the correct tools, you can succeed.


Using Data From Financial Statements

Financial statements are documents that detail a company's financial performance over time. Accountants, businesses, and government organizations frequently audit these statements. Financial analysts and investors use the information in these statements to assess the financial health of companies and make decisions based on that information.


Financial statements are similar to a company's scorecard. They're utilized to keep track of important financial information about various areas of an organization's operations. As a result, they can be evaluated based on the company's present, previous, or anticipated performance.


The balance sheet, cash flow statement, and income statement are all examples of financial statements. Figures on a balance sheet. A balance sheet is a snapshot of a company's assets, liabilities, and equity at a certain point in time. Investors can swiftly assess the success of possible investments using such vital financial data.


Profit and loss statement. Sales, costs, and total net income for a certain time period are summarized in income statements. The profit and loss statement is another name for it. By subtracting the company's expenses from the generated revenue, the net income value is calculated.


Statement of cash flow. The cash flow statement calculates how successfully a company can generate cash to pay its bills and meet its liabilities. This statement completes the previous two financial statements by providing additional information.


Businesses keep financial statements on a daily basis, and internal management teams utilize them to make decisions. When analyzing data from a company's financial accounts, financial statement analysts frequently employ three techniques: Analyze from the side. To find growth trends, this technique compares historical financial data over two or more years.


Analysis from the top down. This method examines how financial statement components compare to one another.


Analyze ratios. It aids in the comparison of data from financial accounts in order to assess how the company is functioning in various areas. Liquidity ratios, solvency ratios, profitability ratios, efficiency ratios, and coverage ratios are examples of financial ratios.


While financial statements contain a wealth of information about a company's finances, they are subject to individual investor interpretation. As a result, investors may come to diverse conclusions about a company's financial health.


Which is better: Financial Analyst or Data Analyst?

If you are a student or young professional who is good with statistics, analytical skills, and problem-solving, consider a career as a Financial Analyst or Data Analyst. Financial analysts analyze financial data to spot patterns and estimate the future, helping their employers and clients make the best investment decisions possible.


Data analysts perform a similar function, with the important distinction that they examine data that may or may not be relevant to investment decisions. For example, a data analyst might look into sales figures, ad effectiveness, transportation expenses, or wages vs productivity.


Both Data Analysts and Financial Analysts are fantastic jobs with high earning potential, acceptable work hours (40 to 45 hours per week on average), and a growing job market. The differences between the two positions are generally unclear, but the most notable distinction is that a financial analyst's typical activities are significantly more connected to investment markets.


If you want to invest and stay up with Wall Street but don't want to deal with the risk of investment banking and trading, Financial Analysis is a good option. If, on the other hand, you enjoy dealing with numbers and are interested in computers and technology, you are likely to possess the necessary abilities and interests to become a successful accountant.


Finance Requirements for Data Analysts: Tools

There are a number of tools available on the market to assist Data Analysts in their data exploration. Here are a few of the most widely used tools:



BI Tools

R and Python


Excel is a financial aficionado's dream come true. Excel features such as Pivots, V-Lookup, Mathematical, and Analytical functions give insights in a matter of seconds. Excel is a component of Microsoft Office and is widely used in every sector and organization. A lot of its features that can examine complex data sources are frequently used by finance professionals. Excel is similar to a database in that it has rows and columns as well as a large number of mathematical and analytical capabilities.


Excel is one of the most versatile programs on the market, with a number of plugins that can improve its productivity.


BI Tools 


Another wonderful collection of Financial Data Analysts tools is BI tools, which help finance professionals to extract relevant data from large data sets. Data cleansing and data modeling capabilities are available in popular BI tools like as Microsoft Power BI, MicroStrategy, Tableau, and Qlik Sense, which may be used to detect financial trends, sales forecasting, and areas for improvement, and more.


R and Python are two programming languages.


R and Python are popular computer languages with powerful data modeling and extraction capabilities. These programming languages can be used by professionals to create out-of-the-box capabilities for doing customized and complicated statistical analyses.


Users can create algorithms that do regression analysis and classify data using computer languages.


We hope that this blog post will be beneficial for you. We will continue to create useful works in order to get inspired by everyone. We are sure that we will achieve splendid things altogether. Keep on following Finage for the best and more.  

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