Things you Should Know About Data Analytics in Accounting

“Big data” is the most used word of the 21st century. The term is used to describe the massive portfolio of data that is growing exponentially. Big Data will have a tremendous impact on enhancing productivity, profits and Risk Management.

But big data in itself yields limited value until it is analyzed and processed.

Analytics is the process which will help you to make meaningful conclusions. A lot of industries have recognized the potential that big data and analytics provide. One such industry where we can see the significant potential is the Accounting world.

The data that exists is scattered, often unused until it is pulled for an audit or a tax return. There was a time when only samples were selected and verified during an audit but now with the advent of data analytics auditors are able to analyze the business 24*7.

To understand the changing customer behavior and market trends Chief Financial Officers (CFOs) and finance leaders are embracing accounting software equipped with “Big data” and analytical techniques. The predictive power of data analytics enables CFO’s to make financial decisions based not on what happened in the past but what is likely to happen in the future.

So why is data analytics so important in accounting?

Accountants use accounting software empowered with data analytics to help businesses uncover the most valuable insights associated with finance, identify process improvements that can increase efficiency and better manage risks. Data analytics gives them the necessary tool-set to strengthen their partnership with business leaders.

A few examples

  • Auditors who are working both externally and internally can shift from a sample based model to model that allows continuous monitoring where larger data sets can be analyzed and verified. The outcome is less margin of error and more precise recommendations.
  • Tax accountants can also use data science to quickly analyze complex taxation questions related to investment scenarios.
  • Accountants use big data to study the consumer behavioral patterns. These help businesses build analytic models which can help them identify investment opportunities and generate higher profit margins.

Four Types of Data Analytics every accountant should be aware of

 1- Descriptive Analytics = “What happening?”

This one is used most often and includes classification and categorization of information. Accountants keep an eagle eye on the flow of money through their organization – they take care of the revenue and expenses, inventory counts and sales tax collected. Accurate generation is a trademark of solid accounting practices and for this accurate report generation compiling and verifying large amounts of data is important.

2- Diagnostic Analytics = “Why did it happen?”

Diagnostic is primarily used to monitor the changes in the data. Accountants on daily basis analyze the variances in the data and calculate the historical performance. A lot of accountants use variance analysis for budgeting. There is also specialized accounting software available in the market that can be used for detecting high-risk transactions.

3- Predictive Analytics = “What’s likely to happen”?

In the predictive analysis, data is used to access the likelihood of future outcomes. Accountants are experts in building forecasts and identifying patterns and trends that shape those forecasts.

4- Prescriptive Analysis =”What should we do?”

Simulation and optimization of data are done to answer questions such as “what should we do?” It is something that is not just limited to recommendations and goes beyond that. It is actually executing actions or taking decisions that are right for a particular situation. The prescriptive analysis is used to answer” What steps or interventions need to be taken to achieve the required outcome?”

Descriptive and diagnostic analytics usually depend on analytics tools that have the capacity to handle the manipulation of large sets of data. Examples include SQL, Oracle DB, Hadoop, Tableau, Microsoft Access, R, python etc.

Predictive and Prescriptive analysis rely on analytics tools that have mathematical modeling capabilities. Examples include SAS, R, Python, optimization tools like Garrobi, Riverlogic; simulation tools like Analogic, natural Language processing tools like Natural Language Toolkit or OpenNLP.

 

The Future

It is expected that in the coming years there will be a tremendous use of analytical tools. Accounting professionals will delve themselves deeper into the combined analysis of structured data such as financial statements and unstructured data such as the large volume of client contracts. They will provide more extensive data analytics and quality business insights.

Machine learning, Artificial Intelligence, robotics, blockchain will have a major impact on the profession. But the fact also cannot be denied that “all these technologies in one or another way depends on data”. If you can’t analyze data you cannot do machine learning, you cannot do robotics and you cannot do blockchain.

Therefore it is important that the accountants to take a deep dive into the pool of data and enable themselves to ask better questions and get better answers.

To make the most out of data analytics such that it suits the needs of the accountants as well as the organization, a clear plan should be carved out that articulates the three components parts- data, analytics, and people. All the three components should be integrated and aligned to create business value.

Building a strong data foundation is the secret of any data analytics strategy. Achieving this might require investments and possible reorganization of existing data structures. Apart from this, it is also important for the organization to hire talent with the appropriate skill-set and abilities.

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