How data analytics tools help accountants

Accountants play a crucial role in helping companies increase their revenue and minimise costs. They are expected to not only crunch the numbers but also use them to deliver clear, high-quality advice. Luckily, there’s more data available than ever before to help accountants deliver this business advice. 

In this guide we’re going to cover how data analytics tools can help you become a better accountant by looking at the following topics: 

• The importance of data analytics
• 4 types of data analytics
• Tools for data analytics
• Business analysis
• Financial analysis
• Benefits of using data analytics

Data analytics are essential for accountants to understand and to discover insights into a business’ financial state. By using data analytics, an accountant can find ways to increase efficiency, manage risk better and see where processes can be improved. Data analytics are becoming increasingly important as the role of accountants changes. Accountants are no longer expected to crunch numbers but must find ways to advise and inform employers or clients about their overall business decisions. By backing up their advice with data, they are ensuring that their advice is more effective and trusted by employers and clients. 

Before taking a look at some of the data analytics tools to help accountants, it’s important to take a quick look at the four types of data that they are used for:

1. Descriptive analytics

This is the most common and it includes the classification and categorization of information. Accountants use this data to report on the flow of money through a company, using tools to analyse:

• Revenue and expenses
• Inventory counts
• Sales tax collected

Descriptive analytics is all about compiling and verifying large amounts of data. 

2. Diagnostic analytics 

Diagnostic analytics are used to monitor any changes in data. Accountants will look at this data to analyse variances and calculate historical performance. Analysing historical data is a key indicator and is critical for building reasonable financial forecasts. 

3. Predictive analytics

Predictive analytics is about using data to analyse the chances of future events. One of the essential responsibilities an accountant has is building forecasts and identifying patterns that affect those forecasts.  

4. Prescriptive analytics

Perspective analytics involves using the data in forecasts to make recommendations for future growth or identifying potential downsides.  

Now let’s take a look at some of the tools that will help you with your accounting tasks.

• Excel: This is probably the most obvious accounting tool. However, Excel shouldn’t be underestimated. It’s become one of the go-to tools for accountants for years now. Over the years new features and plug-ins have been added to make it even better. It’s a very versatile app and can be used to analyse even the most complex sources of data. 

• Business Intelligence (BI) tools: These software tools collect and process huge amounts of unorganised data from internal and external systems. They are extremely useful tools to help organise data, model and provide accountants with easily understandable visuals, such as graphs, infographics, images, etc. Therefore, they make it easier for accountants to identify trends, pain points and areas for course correction or improvement. These visuals are also great to share with an employer or clients because of their clarity and easiness to digest. Some examples of BI tools include: Microsoft Power BI , FineReport and Tableau.  

• R and Python: When it comes to software tools for data analytics, these are probably some of the most advanced you get. R and Python are programming languages. They both have a large community of users and both have extensive catalogues of tools that can be used for data analysis. You can use these tools to conduct highly customisable and complex statistical analysis. The capabilities of R and Python can get really advanced and they have an almost infinite number of uses, from building algorithms to performing regression analysis. This is definitely the most advanced form of data analytics and might require some additional training to become proficient at it.  

Here are some basic techniques for analysing a business:

• Ratio analysis: is the comparison of one metric with another. For example, the cost of manufacturing a product compared with revenue generated from that product. 
• Comparative financial statements: this is a crucial way of highlighting a businesses’ financial patterns by comparing one financial statement with another.    
• Common size statements: this is the process of adapting two drastically different financial statements and converting them into a common “language”.
• Trend analysis: by looking at the long-term trends, you will be able to see whether a company’s financial health is improving or worsening. 
•  Financial analysis ratios:  Financial analysis ratios allow analysts to see how various parts of a financial statement compare with one another.    

Here are three basic ways to analyse a financial statement:

• Horizontal analysis: this involves comparing certain pieces of financial data over a period of time, such as a fiscal year or sales quarter.
• Vertical analysis: is the proportional evaluation of different items on a company’s financial statement, making it easier to compare line items with each other. 
• Ratio analysis: is a way to compare different metrics. An example of ratio analysis is the current ratio which shows a company’s current assets divided by its current liabilities. This analysis offers a brief overview of the company’s overall financial health. 

• Increased competitiveness: predictive analytics can help to increase a company’s competitiveness and revenue by allowing accountants to make more accurate and detailed forecasts. Forecasts allow the company to identify and react quickly to changes in the market which would have been difficult to see without the help of analytics. 

• In-depth financial analysis: these more in-depth data analytics tools can allow companies to link diverse financial and non-financial datasets. This will provide a more comprehensive reporting performance for the employer or client.

• Manage risk: data analytics techniques such as continuous auditing and monitoring can help accountants to assess the levels of risk within a company. 

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