Financial Data Analytics: Benefits, Methods, and Use Cases

In the world of investing, it can be tough to steer a private equity firm, hedge fund, or investment bank in the right direction all the time. There are a wealth of choices and opportunities to pick from – but in an ever-changing business landscape, not everything pans out as it’s expected to. That’s why most modern companies involved in trading securities will have one or more financial analysts on their team: to process data regarding assets and make recommendations about what to take action on, and when.

This article will give you an introduction to what financial analysts do, how they do it, and why they do it in the following sections:

  • What is financial data analytics?
  • Benefits of using financial data analysis
  • Common financial data analytics tools
  • Types of financial data analysis: top use cases

We’ll start by defining what data analysis in finance means, including how it’s a bit different from data analysis in other fields of business.

What is financial data analytics?

Financial data analytics refers to the interpretation and use of financial data for making investment decisions. It is the use of market trends, company financial forecasts, and other economic indicators by financial analysts to advise the firms they work for on setting investment strategies.

People sometimes make comparisons between financial analysts – the people who perform financial data analytics – and data analysts. They’re not only termed similarly, but also are similar in terms of qualifications, job competition, pay, work-life balance, and function. So we’ll explain some of the subtle differences below.

Financial analyst vs. data analyst: the main differences

The main areas of contrast between financial analysts and data analysts are in what types of information each role focuses on, and what purposes each role applies them to. Financial analysts rely, unsurprisingly, on finance-related information such as economic data, market trends, and financial forecasts. Their focus, therefore, is solely on investment markets: what trading options are available, what investment objectives are, and when the best time to make a move is.

Data analysts, meanwhile, collect and look at any kind of “big data” they feel might hold insights into how to improve a company’s operations. That includes decisions on things like product or service lineups, target audiences, marketing tactics, and hiring strategies. It can also include decisions on business investment and expansion, like the decisions financial analysts advise on, but not always.

People with statistics backgrounds usually fare decently in either role. However, those with finance or economics backgrounds tend to lean toward becoming financial analysts. In contrast, those with backgrounds in information technology and computer science are often find themselves in the data analyst role.

For the purposes of this article, whenever we refer to a financial analyst, we are talking about someone who performs financial data analytics/analysis. Conversely, whenever we mention financial data analytics/analysis, we are referring to a function specifically performed by a financial analyst.

Benefits of using financial data analysis

Financial analysts are expected to be in demand for years to come, as markets get more complicated and regulations on them get tighter. So hedge fund managers, private equity firms, and investment banks alike need people on their teams to help them navigate these complexities. Here are a few reasons why financial data analysis is becoming common in the investing world:

  • Clearer direction: Financial analysts can help the investment firms they work for fine-tune their strategies by using data to lay out what options are available, as well as their potential pros and cons.
  • Timely decisions: Companies often have a lot of money riding on their assets, but things can change quickly in the world of finance. Having a financial data analyst on board to make sense of what’s happening can help hedge fund managers and private equity firms take corrective action before they take too big of a loss on slumping assets.
  • Better forecasting: On the other hand, vigilant and accurate financial data analysis may be able to predict market trends before they happen. This could allow a company to sell off assets before they become liabilities, or to purchase assets at lower prices just before they are predicted to take off in value.
  • Awareness of relationships: Businesses don’t run in a vacuum. They often rely on partnerships and cross-promotion with other businesses – or even entire industries – to be successful. Hiring a financial analyst who understands these relationships can allow investment firms to make more informed decisions than if merely examining an asset by itself.
  • Comparison-based modeling: Another thing financial analysts can do is model the financial performance of an asset based on the performance of similar assets in comparable geographic locations and time periods. This can be useful if, say, a private equity firm is thinking about expanding into a new market but doesn’t have a lot of financial data on it readily available.

Common financial data analytics tools

The mass quantities of information that financial analysts have to deal with today take more than a calculator, or mental math done with paper and pen, to handle. Here are some of the tools that modern financial analysts are working with.

Microsoft Excel

Microsoft’s spreadsheet program is a common platform used in many workplaces as part of the Microsoft Office suite. But it’s extremely powerful in the finance world specifically. Part of the reason it is so popular in finance is it has built-in math and analysis functions that allow for performing calculations and gathering insights quickly.

It also has features that help when working with complex datasets. One example is the “VLOOKUP” function that allows you to search for or check information across multiple datasets. Another is PivotTables, which allows for visually comparing multiple datasets at once. You can even get add-ons for Excel that will expand its capabilities even further.

BI tools

Business intelligence (BI) software is another common class of tools used for financial data analytics. These programs are designed to take raw datasets, clean them, and organize them into models that financial analysts (among others) can extract actionable insights from. These insights can be used for a number of business purposes, including finance-related ones such as product demand forecasts and profit/loss trend analysis.

Popular BI tools include Microsoft Power BI, Tableau, Domo, MicroStrategy, and Qlik Sense.

Programming languages

Of course, if you’d rather have the most fine-tuned control over your financial data analysis, you can write your own programs with coding languages. Here are a few of the most widely-used ones:

  • Python – Financial data analysis uses this programming language most often, as it has several built-in libraries of math and statistics functions.
  • R – The second most popular programming language in finance, largely because it’s excellent for manipulating statistical data to find and retain patterns. This makes it useful for modeling and prediction.
  • MATLAB – Short for MATrix LABoratory, MATLAB has support for tools used in financial data analysis such as functions, algorithms, and data manipulation & modeling. It also works fairly seamlessly with tools written in other programming languages.
  • Java – Java is considered a general-purpose programming language, though it’s sometimes used to create financial or banking applications because it’s very secure.
  • SQL – Though not a programming language in and of itself, Structured Query Language is often used in finance because it has a number of functions that simplify aggregating metrics from tables and databases.

Types of financial data analysis: top use cases

We’ve talked briefly about the benefits of analyzing financial data when making investments. In this section, we’ll expand on those by taking a deeper look at the specific functions that financial analysts fulfill in the investment process.

1. Investment research

Obviously, businesses with a lot of money to invest don’t want to blindly put it on just any assets. They need to survey the financial market to find what opportunities are available, how lucrative they might be, and what risks they may represent. They also need to draw comparisons between options and look at investments from different angles for relationships that may affect performance.

This process can be broken down into a number of different processes, which we’ll discuss in the points below.

2. Economic forecasting

Part of weighing an investment’s opportunities and risks involves predicting how it will perform over a future period of time. That means not only analyzing how it has performed previously, but also looking at factors that point to how its situation will change from its current state. How are assets in similar industries performing in similar places and time periods? How high is demand for a product or service relative to supply? Are supply chains working well enough to meet the demand? These can all be clues as to what direction an asset’s financial status may be going.

3. Due diligence

Corporate-level investments often involve very large sums of money, so there is a lot at stake if something goes wrong. That’s why financial analysts need to look at potential opportunities from many different angles, in case there are pitfalls or anomalies hidden within the data that could spell trouble. Certain indicators could point to waning demand for a product or service, supply chain disruptions, or even social circumstances that could take their toll on particular companies. Finding these patterns may mean it’s a better idea to move on to invest in competitors, or even other industries.

4. Brand/industry relationships

Another thing financial analysts might want to look at is the data on relationships between certain industries or specific businesses. They may see that certain companies are doing well because they are effectively cross-promoting with other companies or industries that cater to similar lifestyles. Others may be struggling because competitors that are disrupting their market niche are emerging. Some may even show signs of heading for mergers or acquisitions. Financial analysts would do well to consider these relationships when advising investment decisions.

5. Competitive advantage

Some financial analysts are under pressure to provide information or make investment recommendations faster than other financial analysts at competing companies. So the faster, more efficient, and more accurate their financial data analysis is, the more likely they are to succeed at their firm – perhaps even gaining the firm new companies as clients. That makes their financial data analysis a balancing act between width and depth.

By going wide, a financial analyst can offer insights relevant to more businesses that invest with their firm. But by going deep, a financial analyst may find opportunities or risks that financial analysts who go wide might miss. The decision mainly hinges on whether a financial analyst works for a single client, like a hedge fund or private equity firm, or works for an investment bank that serves multiple clients at once.

6. Portfolio management

Even after the assets to invest in have been settled on, a financial analyst’s work isn’t done yet. They still have to monitor financial data to make sure that their analyses and predictions were correct. Keeping a close eye on metrics may allow them to spot signs that things aren’t working out, and to advise selling off certain assets before they become too big of liabilities.

This just scratches the surface of what financial data analytics can do. And it can be even more powerful if it’s fueled by alternative sources of data, like the kinds of geospatial data we have here at SafeGraph. See how our data is being used in the financial services industry and check out our site for data samples you can get started with today.


Head to our blog to learn more about using alternative data and where to find it.