Business leaders strive to make decisions that improve the bottom line, allowing their companies to become more profitable and more viable over the long haul. Accountants and other finance professionals play a pivotal role in supporting these decisions, empowering business leaders to act from a position of knowledge and clarity.
Accounting professionals provide meaningful decision support through data analysis, among other ways. More than ever before, companies have access to large volumes of financial data. When properly analyzed, this data can point toward meaningful avenues for business expansion or operation.
As data becomes more central to corporate financial activity, accountants and other finance professionals are tasked with gathering, managing and assessing it. Success in finance hinges on expertise in data analysis, including familiarity with key data analysis tools.
This guide outlines a range of data analysis tools and resources that can be useful for finance students as well as for current finance professionals who wish to hone their skills.
Data Analysis Tools
Finance professionals leverage a number of tools to help them evaluate a company’s financial standing from various perspectives. These data analysis tools can serve multiple functions, including:
- Helping accountants evaluate previous tax burdens and forecast future tax liabilities while potentially uncovering ways to minimize the taxes owed
- Modeling different scenarios and probabilities to help the finance department manage and mediate risk
- Providing the most profitable avenues for investing, thereby contributing to the wise stewardship of company resources
- Highlighting areas of financial inefficiency and providing avenues for using resources more effectively
Of the many types of data analysis tools, three categories stand out as foundational: Excel, business intelligence (BI) applications, and R and Python.
Excel is part of the Microsoft Office suite, a software package common to most PCs. But don’t underestimate its robust data analysis tools. Finance professionals rely on a number of its features, from the creation of forms to PivotTable, which analyzes statistics from complex data sources.
Excel is one of the most versatile data analysis tools available, with countless plug-ins that can be downloaded to enhance its efficiency. For those seeking a tool they can customize to meet their data analysis needs, Excel is square one.
For additional insight into using Excel, consider these resources:
- PC Magazine, “22 Excel Tips for Becoming a Spreadsheet Pro.” Boost your knowledge of Excel’s numerous functions and amenities.
- Simplilearn, “7 Tips to Improve Your Basic Microsoft Excel Skills.” This article provides some entry-level Excel tips and shortcuts.
BI tools allow finance professionals to extrapolate actionable business conclusions from a data set.
These tools clean up data, model data and provide the finance team with easy-to-digest visuals. Data visualizations make it easy to identify financial trends, look for pain points, and clearly identify areas for course correction or improvement. Additionally, these visualizations are easy to share with other members of the executive team, providing them with understandable data analysis.
Some of the most popular BI programs are Power BI, FineReport and Tableau.
Learn more about how to get started with BI programs.
- Tableau, “7 Tips and Tricks from the Dashboard Experts.” This guide offers some intermediate-level tips for using Tableau.
- CIO, “7 Power Tips for Microsoft Power BI.” This article provides insight into effectively using Power BI.
R and Python
If Excel is a basic data analysis tool, and BI tools are more intermediate, then R and Python are the more advanced and sophisticated options. Professionals use these programming languages to conduct highly customized and complex statistical analysis. Specifically, they use these languages to build algorithms that perform regression analysis, classify data clusters and more.
R and Python Resources
Discover some additional resources:
- Toward Data Science, “Step-by-Step Guide to Creating R and Python Libraries (in JupyterLab).” This guide walks you through some of the technicalities of R and Python.
- Geeks for Geeks, “10 Essential Python Tips and Tricks for Programmers.” Increase your aptitude in Python with these programming basics.
Additional Data Analysis Tools
For those seeking additional information about data analysis tools, here are some resources.
- Towards Data Science, “Top 6 Data Analytics Tools in 2019.” This guide goes into great detail about several recommended data tools, including the ones mentioned above.
- Codersera, “R vs. Python for Data Science in 2019.” This article explores the differences between R and Python and highlights applications for each.
Benefits of Data Analysis in Finance
Data use has become increasingly prevalent in the business world. According to an Investopedia article, 84% of business leaders believe companies that don’t engage in data analysis lose a competitive edge in the market.
Just a few examples of these benefits include:
- Customer profitability insights. Data analysis helps companies see the revenues generated per customer, the cost of customer acquisition, and which customers or customer groups are most profitable. These actionable insights guide a business’ efforts to best allocate marketing dollars, streamline its sales process or refine its messaging.
- Sales predictions. Predictive modeling can help business leaders to identify trends in sales and gain clarity, whether they’re navigating high points, low points or somewhere in between.
- Analysis of cash flow. Data analysis tools can also help finance professionals trace cash flow, looking for areas where a business can stem losses or invest revenues more responsibly.
Data analysis has a number of specific applications. For example, algorithmic trading helps a company to automate its investments in a way that minimizes risk, drawing from a history of investment data. Lenders can use predictive modeling to help weigh the risks involved with different borrowers and set rates accordingly. Similarly, insurance companies can use predictive modeling to price their products appropriately.
More About the Uses of Data Analysis
The Balance and Investopedia provide two additional resources regarding data analysis and its business benefits:
- The Balance, “Applications of Big Data in Finance.” This article shows how data analysis can be used in consumer credit, insurance and other financial services.
- Investopedia, “How Big Data Has Changed Finance.” This article highlights some of the ways data can make financial decision-making more efficient.
Data Analysis Educational Resources
Learning to effectively manipulate data analysis tools takes time and practice. Students and professionals can take advantage of a number of educational resources to hone their skills and expand their knowledge.
A few examples are:
- IDEA. The IDEA Academic Program is geared toward financial auditors and provides a range of data analysis tools with which students can familiarize themselves.
- Galvanize Academic Network. Professors can join the Galvanize Academic Network to access course content and case studies that equip their students for real-world success in financial analysis.
- Alteryx. Alteryx provides students in accredited programs with free training in data analysis tools, with a focus on career advancement.
- Tableau Academic Programs. Tableau is one of the most popular BI tools in the world. Students can use the desktop version for free to learn this important software.
Tips for Using Data Analysis in Finance
To reap the benefits of data analysis in business, professionals must not only access the right tools but also develop the right techniques and practices.
Core Techniques for Business Analysis
Some of the most basic frameworks for business analysis in finance include:
- Ratio analysis. Ratio analysis refers to comparing one metric with another, such as the cost of manufacturing a product compared with the revenue generated from that product.
- Comparative financial statements. An important way to illuminate a business’s financial trends and patterns is to compare one financial statement with another. For example, an income statement can be compared with a cash flow statement, showing how income is managed in the company.
- Common size statements. Finance professionals should also be adept at taking two dissimilar financial statements and converting them into a common “language.” For example, they may have two different statements that they convert into a profit-and-loss (P&L) percentage, ensuring they’re not making apples-and-oranges comparisons.
- Trend analysis. By looking at long-term trends, finance professionals can get a better sense of whether a business’s financial health is improving or declining.
- Financial analysis ratios. Financial analysis ratios allow analysts to see how different components of a financial statement compare with one another.
Analyzing Financial Statements
The ability to analyze a financial statement is fundamental for any accountant or finance team member. There are three basic ways to perform this analysis:
- Horizontal analysis. Horizontal analysis entails comparing different pieces of financial information over a certain time period, such as a fiscal year or a sales quarter.
- Vertical analysis. Vertical analysis involves the proportional evaluation of different items on a company’s financial statement, which makes it easy to compare line items with one another.
- Ratio analysis. Ratio analysis lets the finance professional compare metrics. A simple example is the current ratio, which shows a company’s current assets divided by its current liabilities. This analysis offers a snapshot of the company’s financial health.
Accountants or finance team members should be ready to analyze the company’s financial data, whether that means performing a trend analysis or a structural analysis, as opposed to simply reporting raw numbers without context.
Additional Tips for Financial Analysis
Those who wish to further hone their financial analysis skills may benefit from the following resources:
- Investopedia, “Financial Statement Analysis.” Investopedia takes a closer look at some of the frameworks for financial statement analysis that we mentioned above.
- The Balance, “How Do You Do Financial Statement Analysis?” This article provides additional guidance for employing different methods of financial statement analysis.
Data Analysis for Careers in Finance
The job outlook is promising for professionals with data analysis skills who are seeking a career in finance. According to the U.S. Bureau of Labor Statistics (BLS), there were 1,424,000 accounting and auditing jobs in 2018. Furthermore, the BLS projects employment in the field to grow by 6% between 2018 and 2028.
There were 876,300 management analyst jobs in 2018, according to the BLS. Even more promising, the bureau projects employment in this field to grow by 14% between 2018 and 2028 — nearly three times the average for all occupations.
Discover the Value of Data Analysis Tools
Those who wish to add value to an organization through their financial acumen need to be comfortable working with large volumes of data — and using that data to make prudent, informed business decisions. Mastering some basic data analysis tools is an important step in that direction.