Building Proficiency in Data Analytics

Anyone with a degree or extensive experience in statistics knows that data analytics has been around for a long time. So, what is data analytics, why has it become so prevalent and how can CPAs leverage it?

Briefly stated, data analytics is the process of manipulating raw data into actionable information for better decision making. There are several factors that have recently accelerated this science across broad swaths of businesses. These include real-time access to petabytes (1 quadrillion bytes) of data, software tools that provide data scientists swift and diverse analysis of data, and advances in artificial intelligence that allow systems to evolve with minimal human intervention. While there are countless areas where data analytics can be applied, let’s review three that are particularly well suited to CPAs.

Financial Analytics

Gone are the days when businesses could wait for annual, quarterly or even monthly historical financial statements to effectively implement tactical decisions to maximize profitability and cash flow. Financial analytics allows interested stakeholders to view and manipulate near real-time data to see how actual results measure against budgeted amounts. Budgets can then be revised in a timelier manner to reflect changing conditions.

Financial analytics also enables what-if scenario planning that can model antici­pated results based on changes in selected variables including inflation, interest rates and specific industry economic growth patterns. Dashboards allow visualization of data that can trigger alerts when metrics are not being met. Advanced statistical tools and analysis allow management to move beyond decision making based on conjecture. Predictive analytics can be used to track and estimate cash flow, forecast sales activity and evaluate financial trends on both the balance sheet and income statement.

Operational Analytics  

Operational analytics moves beyond high-level financial statements and drills into the granular details that drive business performance. Tracking and then applying incremental changes into business processes dissects workflow to its most basic elements. Although operational analytics looks different depending on the industry where it is being applied, the benefits are similar. Increased productivity, better utilization of resources, improved customer experiences and faster decision making are all byproducts of successful operational analytical projects.

In a manufacturing environment, data is gathered from disparate devices (the internet of things), enterprise resource planning (ERP) systems, time-tracking tools and outside benchmarking resources. This information is validated, aggregated and analyzed to improve labor productivity, production yields, machine down-times, supplier performance, product quality and a host of additional metrics. This becomes an iterative process that continuously mea­sures, suggests adjustments and improves each step in the manufacturing process.

Audit Data Analytics  

Audit data analytics introduces software tools and techniques that perform analysis of complete datasets thereby eliminating reliance on small, random samples. Use of these systems eliminates repetitive tasks and tedious data entry, freeing auditors’ time for more complex testing to discover data anomalies and perform more extensive risk analysis. Audit data analytics can be introduced early in the audit process to allow discovery of potential problems while datasets are current and evolving.

Professional judgement and experience are never replaced by these digital tools. Assessing the quality of the raw data under audit, then extracting, transforming and loading the cleansed data into the analytical tools being used requires seasoned professionals with a high degree of expertise and acumen.

The following websites highlight the features and capabilities of audit data analytics software:

• CaseWare IDEA Audit Software and Data Analysis Software — idea.caseware.com/products/idea
• Wolters Kluwer TeamMate Analytics for Audit — wolterskluwer.com/en/solutions/teammate/teammate-analytics

How to Become a Data Scientist

There are many different avenues that one can pursue within data analytics with varying levels of hands-on participation and required training. An excellent option for an intense learning experience, that also offers 61 CPE credits, is the AICPA’s Data Analytics Certificate Bundle. The complete course work includes five separate certificate programs presented via online lectures and also include hands-on learning labs which provide intense practical application of the concepts and software tools presented.

The introductory-level Data Analytics Core Concepts Certificate is the starting point for this program. With information technology as the field of study, participants begin to understand what data analytics is and how it can be applied in a business environment. For those simply seeking a high-level overview of how data analytics can be used under different scenarios, this might be the only course required.

All of the remaining courses are regarded as intermediate level. The Application of Data Analytics Essential Certificate and the Forecasting and Predictive Analytics Certificate each provide a heavy dose of statistics for people interested in moving beyond the roles of champion and business analyst. These programs lay the foundation for professionals seeking to become data engineers and data scientists specializing in extracting, transforming and loading (ETL) data and then performing all of the necessary manipulation to provide end users with actionable insights. These two programs offer significant hands-on activities with a wide assortment of software tools and programming languages that go beyond any single vendor’s prescribed solution.

Additional certificates in Data Modeling and Data Visualization fall under the fields of study in both specialized knowledge and information technology. These two programs offer CPAs and finance professionals the most useful tools for manipulating and presenting data to executives, managers and other interested stakeholders. Work product from these programs provides information and insights to formulate, implement and fine tune operational strategies. NJCPA members can save 25 percent on each of the AICPA data analytics certificates with code DATAA25 (offer expires Aug. 31, 2022). Visit njcpa.org/ certificates for more information and to enroll.

Evolution in data storage, broadband communications, mobile technology, artificial intelligence and database capabilities are continuously changing the standard operating processes in almost every aspect of business practice. Decision making and management styles that continue to rely on guesstimates, gutfeel and seat-of-the-pants prognostication are destined to go the way of the dodo bird. Data analytics has emerged as a reasoned, scientific method that will raise the level of business management in countless situations. Like any business practice, care must be taken to understand and work around potential shortcomings that arrive with any business science, including data analytics. That said, reliance on new tools, techniques and analysis will continue to proliferate throughout accounting, business and every aspect of our daily lives.

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Marc D. Mintz Marc D. Mintz, CPA.CITP, CGMA, is the managing member of Marc Mintz & Associates, LLC. He is a former NJCPA Trustee and a past president of the Passaic County Chapter. Marc can be reached at marc@marcmintz.com.