BUILDING PROFICIENCY IN DATA ANALYTICS By MARC D. MINTZ, CPA, CITP, CGMA
MARC MINTZ & ASSOCIATES, LLC
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?
8
SUMMER 2022 | NEW JERSEY CPA
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.
