Data Analytics in Accounting: 5 Comprehensive Aspects


Data Analytics in Accounting FI

Analytical thinking has long been a part of Accounting. Accounting skills have progressed over time from pencil and paper to typewriters and calculators, and finally to spreadsheets and Accounting software. Accounting Data Analytics is a new skill set that is becoming increasingly important in almost every aspect of Accounting. Today, Accounting firms are incorporating Data Analytics into their Business Models in order to stay ahead of the competition.

Accounting Data Analytics has aided in identifying the patterns and metrics that would help in strategic decision making and draw suitable conclusions. Moreover, companies can utilize these valuable insights to make improvements in several areas such as improving internal processes, identifying risks, monitoring Business Performance, etc. Thus, Data Analytics in Accounting has boosted the Accounting industry and raised competition in the market. 

This article will give you a comprehensive guide to Data Analytics in Accounting. You will also explore the increasing need for Accounting Data Analytics. It will also give you an understanding of the key tools used in Accounting Data Analytics and the challenges you might face while implementing them. Read along to know about the role of Data Analytics in Accounting.

Table of Contents

Introduction to Data Analytics in Accounting

Data Analytics in Accounting
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More than 2.5 quintillion bytes of data are generated every day. Thus, extracting critical data is becoming increasingly important for businesses to make strategic decisions. Accountants utilize Data Analytics to assist firms in gaining meaningful and actionable insights from their financials. Also, Accounting Data Analytics can help in managing risks and identifying process improvements that can improve efficiency. Data Analytics in Accounting has transformed the Accounting sector and increased the competition in the market.  

For example, Data Analytics in Accounting can help Tax Accountants to quickly examine complex tax concerns about investment scenarios. Thereafter, investment decisions can be made quickly and allowing businesses to react faster to opportunities and outsmart their competitors and the market. The Accounting industry is rapidly expanding, and understanding the role of Data Analytics in Accounting has become very crucial.

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Need for Data Analytics in Accounting 

In the current business climate, almost every industry is being driven by Big Data, including Accounting. Accounting Data Analytics can help to make effective business decisions and meet client expectations. Below are some of the benefits of incorporating Data Analytics in Accounting:

1) Monitoring and Improving Business Performance

Every industry must regularly evaluate their Business Performance if they want to stay profitable. Accounting Data Analytics can help to ensure that the company is running smoothly, goals are being met, and performance is being maintained or improved. This knowledge is critical for a company’s long-term viability and survival.

2) Improving the Client Experience

Data Analytics in Accounting can be used to improve Client Experience by examining parameters such as the turnover of tax returns, the time it takes to perform an audit, or general client satisfaction surveys. This can help the company to bring new clients and improve the client Retention Rate. Retention Rate is one of the important business metrics that refers to the percentage of clients retained by the company over a given period of time.   

3) Identifying and Managing Risks

A Risk can originate from a multitude of sources both inside and outside the company. Anyone in the Accounting department of a company must know how to deal with risk. Data Analytics in Accounting can help in analyzing the areas of risk that are faced by the company and use Predictive Analytics to make business decisions around specific risks.

4) Generating Higher Profit Margins

Data Analytics in Accounting can be used to uncover the behavioral patterns of your customers. These patterns can aid businesses in developing Analytical Models, which can then be used to discover investment opportunities and improve Profit Margins. Thus, Accounting Data Analytics also helps in generating higher Profit Margins.

5) Cash Flow Analysis

Data Analytics in Accounting can also assist in tracking your Cash Flow and identifying places where a company can reduce losses or invest profits more wisely. Thus, Accounting Data Analytics has a profound impact in analyzing the Cast Flow and transforming the business.

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Emerging Approaches for Data Analytics in Accounting 

There are plenty of Accounting Data Analytics approaches that can help with the auditing process. A few of the approaches include:

1) Deep Learning

Deep Learning
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Deep Learning represents the deeper structure of events and situations in numerous layers of the neural network by combining the information with more advanced methods. For example, existing data can be utilized to construct automated algorithms for specific audit judgments, such as Lease Categorization, Bad Debt Estimations, etc. Moreover, some companies outsource Deep Learning projects to research centers, such as IBM Watson. Prominent Audit companies are investing significantly in Deep Learning and Artificial Intelligence. Deep Learning is gaining importance in Accounting Data Analytics. 

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2) Blockchain and Smart Contract

Blockchain Technology
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Blockchain can be termed as a Data Structure that stores transactional records while ensuring Security, Transparency, and Decentralization. This Blockchain can be used to store programs that run when predetermined conditions are met. These programs are known as Smart Contracts. These smart contracts can be beneficial for the company in numerous ways. 

For example, If an outlier exceeds 100% of the median value of the transactions, the contract between the auditor and the firm may specify that it must be stopped and investigated by human eyes, the Blockchain can easily identify such outliers and direct them to an auditor.

3) Predictive Analytics

Predictive Analytics
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Predictive Analytics is the use of advanced Analytic tools to find real-time insights and forecast future occurrences using historical data. Accountants can utilize well-tested and highly accurate Predictive Analytic models to transform the way your business operates. Thus, Predictive Analytics has the potential to greatly assist firms in achieving new heights.

Key Tools Used in Accounting Data Analytics

Data Analysis Tools
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Accounting Professionals can use a variety of tools to examine a company’s financial situation from numerous angles. The Data Analytics tools can be broadly categorized into 4 types:

1) Excel

Excel Logo
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Excel is a spreadsheet application for Windows, macOS, Android, and iOS that is created by Microsoft. It provides a varied range of features including Calculations, Pivot Tables, Graphing Tools, etc. It is one of the robust Data Analysis tools in the market that supports multiple plug-ins to enhance the efficiency and user experience. It is a widely used tool in almost every business or organization.

2) Business Intelligence Tools

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Business Intelligence Tools allow Accounting Professionals to extract actionable business insights from a data set. These tools help in cleaning the data, modeling data, and deliver easy-to-understand visualizations. These visualizations provide in-depth insights and help in identifying the areas of improvement. Moreover, these tools provide easy to share features with other members. Some of the popular BI tools include  Tableau, Power BI, FineReport, etc. 

3) Proprietory Tools

Proprietory Tools
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A Proprietary Tool can be termed as a tool that belongs solely to a company. Internally, these are often developed and used by the owner to generate and sell products or services to the user or consumer. Large corporations and firms typically employ Proprietary Tools like Interactive Data Extraction and Analysis (IDEA) and w. Moreover, these tools are constantly evolving based on the requirements.

4) R and Python

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R and Python are advanced and sophisticated Accounting Data Analytics tools used in many companies. These programming languages are used to do highly customized and advanced statistical analyses. Moreover, these languages are used to create algorithms that perform Regression Analysis, identify Data Clusters, and other tasks.

Major Challenges Faced in Implementing Data Analytics in Accounting

Data Analytics in Accounting has helped large enterprises and businesses scale up and achieve optimal Revenue and Growth rates. However, there are few major challenges that you may face while implementing Accounting Data Analytics, these include:

1) Inaccurate Data

Manual data entry is the major key cause of Inaccurate Data. Inaccurate Data may lead to serious consequences and may also influence the decisions. However, acquiring a centralized system with a validation check can be used to eliminate these issues.

2) Lack of Support

For Accounting Data Analytics, organizational support is a must. Every employee plays an important role in the Data Analytics process. It will be very difficult to generate any actionable information if they do not submit data for analysis. Thus, there should be proper coordination among the teams.

3) Lack of Expertise

Due to a Lack of Expertise, several firms struggle with analysis. Employees may lack the skills or knowledge to conduct in-depth Data Analysis. However, this challenge can be mitigated by emphasizing Analytical Ability during the hiring process.


This article gave a comprehensive guide to Data Analytics in Accounting. It also provided an outline of the importance and emerging approaches in Accounting Data Analytics. You also got insights into the key tools and challenges that you might face while implementing it. Thus, Data Analytics plays a very critical role in the field of Accounting.

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Share your experience of learning about Data Analytics in Accounting in the comments section below!

Former Research Analyst, Hevo Data

Ayush is a Software Engineer with a keen interest in data analysis and writing technical content. He has authored numerous articles covering a wide array of subjects in data integration and infrastructure.

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