Dynamic Decision Making through Data Visualization

Mar 01, 2017
Pennsylvania CPA Journal

Vast amounts of data are generated every day – about 2.5 quintillion bytes according to estimates from IBM.1 This “big data” originates from numerous sources: from individual social media posts and text messages, to corporate supply chain management activities and financial transactions. Businesses are gaining valuable insights through the analysis of all the data they can gather.

According to a CGMA survey of more than 2,000 finance professionals around the world, 87 percent of respondents said that data analytics will transform the way business is done in the next 10 years.2 The same survey, however, found that only 48 percent believe that they have the skills within their organizations to take advantage of the opportunities that data presents. This article focuses on how CPAs can leverage the power of data analytics coupled with data visualization to improve decision-making.

A major factor in gaining a competitive advantage in today’s globalized business environment is the quality of a firm’s decision-making.3 Imagine the strategic decisions that a CFO could make with the ability to predict the optimal price to charge a customer on a particular day. Suppose controllers could accurately predict the collectability of a receivable based on a customer’s credit score. Or think about the level of assurance that an auditor could obtain by analyzing entire data sets rather than samples. All of the aforementioned scenarios are possible today thanks to data analytics and visualization software, and you do not need to be trained as a data scientist or information technology (IT) professional to leverage these tools.

Data Analytics

Data analytics is the process of examining big data for the purpose of gaining insight. There are four key characteristics of big data, often described as the four Vs: volume, variety, velocity, and veracity. Volume is the number of records in the data set. Variety refers to the types of data, and the data set must contain more than one file type to be considered big data. Velocity describes the speed with which the data is generated. Notably, a key characteristic of big data is that it constantly grows – often at a rapid pace. Veracity refers to the certainty of the data. Large amounts of data may not be useful for decision-making unless the data is clean and accurate.

In recent years, data analytics has gained immensely in popularity. Advances in technology, the decreasing cost of data storage, and innovation in the marketplace has earned the practice wide recognition from businesses. Data analytics provides managers with greater insight into business operations, creating a positive reinforcing cycle. As more meaningful insights are obtained, businesses start tracking and capturing new and additional data points to analyze. The results are more robust data sets and insight, leading to additional questions and theories, requiring more information to be captured and analyzed to answer them.

Data analytics can be applied in many disciplines, such as marketing, monitoring business transactions, or even identifying potential fraud and corruption. It opens new windows of opportunity for generating business intelligence and helping businesses run more efficiently and effectively. There is a wide variety of data analytics software tailor-made for specific industries, including health care analytics to identify waste and reduce costs at hospitals, energy analytics to predict surges in electricity usage to ensure adequate supply, and weather analytics to anticipate environmental conditions to help farmers improve crop yields.

By using data analytics, businesses can considerably reduce the time, effort, costs, and, most importantly, likelihood of errors associated with manual review. In addition, data analytics can be executed in real time on an entire population instead of relying on a sample, providing valuable insights to help businesses detect nefarious activity. For example, companies like Visa can review every single credit card transaction in real time and use rule-based monitoring systems to flag suspicious transactions. Banks can leverage real-time transaction monitoring systems to identify potential money laundering and terrorist financing.

Data analytics not only streamlines operations, it is also more useful and efficient for detecting fraud compared with traditional, manual methods. Instead of spending time performing detailed transactional testing, data analytics allows auditors to focus on performing higher-level risk analysis. Moreover, data analytics allows auditors to review the entire universe of transactions and attain greater confidence about whether or not material misstatements exist in the financial statements.

Data analytics can give auditors access to real-time information, allowing them to proactively get in front of issues and quickly devise remediation strategies. For example, during an internal audit, running data analytics on disbursements, sales activity, product returns, and expense report data can tease out information and suspicious patterns that are indicative of potentially fraudulent activities and behavior. Trend analysis of payment data can help CPAs immediately identify peaks and valleys indicating abnormal payments and any other anomalies in the data that warrant additional investigation.

One of the most critical characteristics of data analytics are traceability and repeatability. It provides a documented step-by-step audit trail of executed analyses to demonstrate how the conclusion was formulated. This can be a valuable benefit for independent parties, such as regulators, in reviewing the work of auditors. If necessary, an independent party can repeat the analyses and reproduce the same results.

For CPAs providing consulting services, data analytics can be used as a targeted exercise to run high-impact analyses to identify areas with the highest risk. This enables project teams to narrow the investigative focus and significantly save time. This is referred to as a top-down approach, where data analytics is applied to the universe of data to cull it down to a targeted population. Data analytics can also offer a holistic analysis, or viewing a problem in a broader context, when information is linked and consolidated from disparate systems and data sources. Through data aggregation and analysis techniques, information is no longer isolated, allowing data relationships and correlations to become more easily identifiable.

Data Visualization

Insights from data analysis are not always easy to glean, and can be equally difficult to share. But the analysis can be enhanced with visualization. “A picture is worth a thousand words” is a common expression that underscores the important connection between visual imagery and cognition. Humans can make sense of an image much quicker, and often more effectively, than text. In fact, the brain is capable of processing visual images in as little as 13 milliseconds.4 Another study found that presentations using visual aids are 43 percent more persuasive than unaided.5 Data visualization is simply the representation of data in the form of a picture or graph.

For decades, CPAs have created visual representations in the form of bar charts and pie graphs to communicate financial results. Today, a rapidly growing field of data visualization takes advantage of the intersection of visual imagery, big data, and advances in technology. CPAs should leverage data visualization given their important role in communicating and using data for decision-making.

Data visualization facilitates communication and understanding of complex relationships, and it can help managers identify patterns. Making sense of large, complex data sets for decision-making is one of the challenges facing business managers today.6 CPAs who can effectively communicate insights gained through data by using visualization tools stand poised to gain a competitive advantage.

Sophisticated and inexpensive software has made data visualization accessible to just about anyone with a computer and an Internet connection. But Scott Berinato, in the Harvard Business Review, warns users to avoid the temptation to simply “click and viz” without understanding the purpose and goals of visualization.7 Berinato suggests that users consider two questions: Is the information that you have conceptual or data-driven; and do you need to make a declaration or explore something? If the answer to the first question is conceptual, then you will want to visualize qualitative information. If data-driven, you are likely going to plot quantitative information.

Berinato describes four types of visual communication: idea illustration, idea generation, visual discovery, and every day “dataviz.” The goal of idea illustration is to clearly communicate complex information by relying on visual metaphors or basic geometric shapes.8 For example, consultants will often use a metaphorical tree in diagramming decisions that can be made. As another example, organizational charts and flow charts often use geometric shapes and hierarchies to communicate the relationships of reporting responsibilities and the flow of processes.

The second type of visual communication is idea generation, or creative thinking, to solve problems and capitalize on business opportunities. Idea generation is useful for various business professionals – from finance managers looking to make strategic cost reduction decisions, to independent auditors holding brainstorming meetings to discuss the risk of fraud. Incorporating visualization as part of the idea-generation process can lead to higher-quality idea conception.

Visual discovery, the third type of visual communication, can be broken down into two categories: testing hypotheses and open-ended exploration.9 In testing hypotheses, data visualizations can serve as confirmation that a suspected relationship between variables exists. For example, an auditor that suspects fraudulent revenue recognition could use data visualization to compare a population of sales invoices to shipping documents to confirm whether or not recorded sales are supported by evidence of shipping. The second category of visual discovery, open-ended exploration, is useful for spotting trends, making sense of complex data, and performing deep analysis.

Finally, Berinato describes everyday dataviz as simple line charts, pie graphs, and bar charts that can be generated in spreadsheet software such as Excel. The data for this type of visualization is simple and low volume. The goal is typically communicating a simple message for formal presentations. Examples include quarterly profitability, income versus expense, and expenditures by percentages. Despite the simplicity, it is important that CPAs are proficient at creating and communicating through everyday dataviz because poorly designed graphs can result in unnecessary questions and failure to communicate the point.


The execution of data analytics requires certain skills and software to perform. According to an EY survey, 58 percent of CFOs and finance leaders believe they need to increase their understanding of digital technologies and data analytics as a strategic priority.10 Fortunately, there is a plethora of resources and off-the-shelf software, such as SQL (Structured Query Language) and Python, to run data analytics and conduct various forms of forensic analysis. SQL and Python are programming languages used for accessing, manipulating, and analyzing data contained in databases.

The majority of CPAs are well versed in Excel, but when comparing the technical abilities of SQL and Python to Excel, the latter is less efficient and incapable of handling high-volume data analytics.

Tableau is a popular software program that is robust in graphic visualization and reporting. A free version is also available called Tableau Public. There are dozens of other data visualization programs, including Microsoft BI, Domo, and Zoho Reports, to name a few. It is important to note that software is only as good as the person using it, and it can’t replace human judgment. Software is useless without the right people asking the right questions. CPAs that are comfortable approaching unfamiliar data can use data analytics and visualization as a successful approach to problem-solving and strategic decision-making.


Becoming proficient with data analytics and visualization requires an investment in education and training. Surely that is no easy task for busy professionals, but it could be a worthwhile investment. A CGMA survey found that 85 percent of respondents believe data analysis skills will enhance career options and employability.11

So how can CPAs prepare for the opportunities and challenges related to big data? The top three learning opportunities by potential for skill development are a data analytics degree, data analytics certificate, and proficiency in data visualization software.12 In Pennsylvania, there are several colleges and universities that offer analytics degrees at the graduate level.

Another option is to take data analytics and visualization courses offered through Coursera, an educational company that specializes in massive open online courses. Coursera partners with universities to offer content for free or at a low cost. For example, Coursera offers a data visualization course developed by the University of Illinois Urbana-Champaign. Students gain access to course materials for free, but they have the option to purchase the course and obtain access to graded materials, receive a grade after completing assessments, and earn a certificate of completion.

Many consulting firms also provide professional workshops focused on data analytics and visualization proficiency in a variety of formats, including online, traditional live-classroom instruction, accelerated 90-minute sessions, and multiple-week classes. The PICPA, too, has numerous courses available that cover data analytics.

The complexity and volume of data will only grow in the future, and surveys of business leaders highlight the importance of being able to gain insight from such data. By applying data analytics, CPAs can make sense of large data sets in a way that was not possible until recently. CPAs can add value to their organizations by leveraging the power of data analytics and visualization to recognize trends, predict outcomes, and test hypotheses. Ultimately, these skills improve the CPA’s decision-making and provide a competitive advantage in the marketplace.

1 “Bringing Big Data to the Enterprise,” IBM. www-01.ibm.com/software/data/bigdata/what-is-big-data.html
2 From Insight to Impact: Unlocking Opportunities in Big Data, CGMA (2013).
3 “Business Analytics and Decision Making: The Human Dimension,” CGMA (2016).
4 M. C. Potter, B. Wyble, C. E. Hagmann, and E. S. McCourt, “Detecting Meaning in RSVP at 13 ms per Picture,”
Attention, Perception & Psychophysics, 76(2), 270–9 (2014).
5 D. R. Vogel, G. W. Dickson, and J. A. Lehman, Persuasion and the Role of Visual Presentation Support: The UM/3M Study, 21 (June 1986).
6 Ramesh Sharda, Dursun Delen, Efraim Turban, Business Intelligence: A Managerial Perspective on Analytics (Upper Saddle River, NJ: Pearson, 2014).
7 S. Berinato, “Visualizations That Really Work,”
Harvard Business Review, 94(6), 92–100 (2016).
8 Ibid.
9 Ibid.
10 “Do You Define Your CFO Role? Or Does It Define You? The Disruption of the CFO’s DNA,” The DNA of the CFO, EY (2016). www.ey.com/Publication/vwLUAssets/EY-the-disruption-of-the-CFOs-DNA/$FILE/EY-the-disruption-of-the-CFOs-DNA.pdf
11 “Business Analytics and Decision Making: The Human Dimension,” CGMA (2016).
12 N. Tschakert, J. Kokina, S. Kozlowski, and M. Vasarhelyi, “The Next Frontier in Data Analytics,”
Journal of Accountancy, August (2016), 58–63.

Cory Ng, CPA, DBA, CGMA, is an assistant professor of instruction and the undergraduate program director of accounting at the Fox School of Business at Temple University in Philadelphia. He is also a member of the Pennsylvania CPA Journal Editorial Board. He can be reached at cory.ng@temple.edu or on Twitter @cngcpa.

Mason Pan is director at Control Risks in Washington, D.C., a professional services firm that specializes in compliance, forensics, and intelligence. He can be reached at mason.pan@controlrisks.com.

The authors gratefully acknowledge Steven G. Blum, CPA, CFE, CFF, a member of the Pennsylvania CPA Journal Editorial Board, for his efforts in coordinating this article.
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