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3 Major Ways Visualization Can Aid Data Analytics in Accounting


A fundamental aspect of accounting is helping clients comprehend what financial information means in the context of past, present and future business outcomes. Translating complex financial data and analyses into understandable information for business users can be challenging. However, advancements in data analytics and data visualization tools can make this and other aspects of accounting more efficient, impactful and beneficial.

The online Master of Accountancy Data Analytics/Data Visualization Track from Northern Kentucky University reflects the increasing importance of accounting data analytics technologies. The program combines the study of advanced accounting topics with the exploration of data analytics and visualization, business intelligence (BI) and business analytics. With this integrated knowledge, students are prepared to excel in the dynamic role accountants play in today's data-driven business world.

Here are three important ways data visualization and analytics help accountants in their professional roles:

Provide Clients with a Snapshot They Can Understand

Accounting has always been a practice of gathering, organizing and analyzing data. Advancements in computing have vastly increased both the amount of data available and the capabilities of data analytics technologies. Logically, accountants use these technologies to make their work more efficient and draw more complex insight from the data stories they uncover.

Accountants are tasked with communicating insight from financial data analyses to their clients. Yet, clients may not possess a high level of data literacy, making effective communication difficult. This is where data visualization can be beneficial.

Through the use of advanced software, accountants can render complex data into simple visual forms, using colors and shapes to demonstrate relationships between data. A visualization could be a traditional pie chart, graph or 2D area visualization (such as a dot distribution map to demonstrate market penetration in different regions).

The types of data, user preferences or competencies, analysis goals and other variables will determine what kind of visualization would be most useful. The intent is to help users understand data analysis in a form that best conveys the information clients need.

Help Clients Visualize What May Come

Visualizations of financial data can help clients easily understand what has happened (descriptive analytics) and why it happened (diagnostic analytics). This is essential for monitoring and responding to business performance metrics, operational efficiency and optimization goals, customer behavior and more.

However, clients also want to understand what is likely to happen (predictive analytics) and what they should do about it (prescriptive analytics). Driven by artificial intelligence (AI) technologies like machine learning, modern data analytics software can analyze information from myriad sources to provide such advanced data analyses.

Using predictive data analytics and tools like time series visualizations, accountants can help their clients get a good idea of potential trends. Applying prescriptive analytics to data, accountants can show clients probable outcomes given different variables, like the potential financial return on an investment in equipment or employee training.

Visualizing these outcomes projected over time can help clients make informed decisions based on value-added insights. This function could be an advanced or more dynamic form of forecasting, integrating accounting, business analytics and BI.

Empower Clients to Explore Data on Their Own

Data visualization and technologies like Tableau, Power BI, and IDEA also help business users understand data and gain insight through modern BI tools. Businesses may embed these applications within various office software platforms or make them accessible through dashboards.

Business users can use these tools themselves to produce visual displays of information. With BI applications that utilize natural language processing, users can employ spoken language to "ask" questions about data, triggering predictive or prescriptive analyses in visual forms. BI tools can anticipate a user's preferences and likely data analysis requests by anticipating trends or anomalous factors that might interest users.

Integrating and utilizing such tools requires collaboration among accountants, data scientists, information systems professionals, business analysts and others. These professionals also help coach top leadership and managers to use such technologies effectively, derive insight and make accurate data-driven decisions.

AI-driven analytics software can also automate much of the mundane reporting processes that consume accountants' working hours. This allows accountants to spend more time on other essential, hands-on tasks that directly benefit clients.

Taken together, modern data analytics can help accountants discover financial insights more effectively and efficiently than ever before. The diverse types of data visualization methods help accountants communicate those insights to clients clearly, quickly and in usable form. In turn, clients can make informed business decisions based on accurate financial information and analyses they can see and easily understand.

Learn more about Northern Kentucky University's online Master of Accountancy Data Analytics/Data Visualization Track program.


Sources:

International Federation of Accountants: Building Data Science and Analytics Capabilities in Finance and Accounting

Oracle AI & Data Science Blog: 4 Types of Data Analytics

Surgent CPA Review: Data Analytics 101: Applying Excel and Big Data to Accounting

Tableau: Data Visualization Beginner's Guide: A Definition, Examples, and Learning Outcomes

The Datalabs Agency: The Most Common Types of Data Visualization & Examples


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