Author: David R. Peters, CPA, CFP, CLU, CPCU
This article originally appeared in the Winter 2026 issue of the South Carolina CPA Report
In today’s business landscape, data isn’t just a buzzword—it’s an asset. Yet, for many organizations, data is still viewed as a line-item expense, rather than the strategic goldmine it truly is. Why should CPAs and business leaders care? Because data represents future sales, operational efficiency, and market insight. It’s our job to protect and leverage these assets to drive success.
Rethinking Data: Asset or Expense?
Does your company treat data as an asset, or is it just another cost on the income statement? The answer can reveal a lot about your organization’s approach to innovation and growth. Businesses that recognize data’s value are better positioned to uncover new revenue opportunities, cut unnecessary costs, and understand their market position. Data is the fuel for strategic decision-making.
The Many Faces of Data
Not all data is created equal, and understanding its types is crucial for harnessing its power:
- Internal Data: Generated within the organization—such as accounting systems, payroll, and client billings or employee evaluations.
- External Data: Sourced from outside, including industry reports, customer surveys, Glassdoor reviews, IRS metrics, and website analytics.
- Structured Data: Organized in a defined format, making it easy to analyze—think financial statements or survey results.
- Unstructured Data: Free-form and messy, like Facebook comments, blog posts, or open-ended survey feedback.
- Streaming Data: Real-time information, such as stock ticker updates or IT reports.
- Static Data: Snapshot data captured at a point in time, like purchase orders, CRM records, or point-in-time sales figures.
The Social Media Challenge
Social media presents both a challenge and an opportunity. Most of its data is unstructured, making it difficult and time-consuming to capture and analyze. Feedback can be a minefield, and objectivity is often questionable. Yet, ignoring this data means missing valuable insights into customer sentiment and market trends.
Why Companies Want Data
The motivations are clear: data helps companies find future revenue opportunities, eliminate waste, boost efficiency, and understand their competitive position. But to unlock these benefits, organizations must ask the right questions and make the right inquiries.
Types of Data Inquiries
There are several ways to interrogate your data:
- Answering Specific Questions: Where did our latest ad perform best?
- Confirming Suspicions: Is our target demographic still 18-34 year old males in Greenville?
- Making Predictions: Do customers who buy product A also buy product B?
- Exploratory Analysis: What new relationships can we uncover between variables?
Each inquiry type serves a different purpose, from validating beliefs to discovering new opportunities. The key is to match the inquiry to the business need.
Data Mining vs. Data Analytics
It’s important to distinguish between data mining and data analytics. Data mining is about finding patterns and connections in large datasets, often without concern for causation. It’s neutral in business but sometimes frowned upon in academic circles. Data analytics is about inspecting, cleaning, and transforming data to derive meaningful insights, combining logic and statistics for actionable results.
Beware of spurious correlations—just because two variables move together doesn’t mean one causes the other.
The Data Process: From Collection to Reporting
The journey from raw data to actionable insight involves three steps:
- Data Gathering/Collection: Methods matter—garbage in, garbage out.
- Data Processing: Getting data into one place and making sense of it.
- Data Reporting: Sharing insights in a way that drives decisions.
Before collecting data, ask: What does the company need? What would provide more insight? Cross-department conversations can clarify both current and future needs.
Capturing Data: Methods and Challenges
Capturing data is the critical first step in the analytics journey. Companies use a variety of methods, each with their own strengths and weaknesses:
- Surveys: Enable targeted questions and structured responses for easier analysis. However, they often draw extremes—very positive or negative—and responses can be highly subjective.
- Website Data: Tools like Google Analytics make it easy to collect data from user interactions online. While this data is plentiful and formatted, it can be skewed by bots or technical issues and may not reveal the true reasons behind user behavior.
- Informal Conversations: Candid discussions with customers or employees can yield honest insights and spark valuable follow-up questions. The challenge lies in recording and analyzing these unstructured responses.
- Customer Follow-Up Calls: Direct outreach offers deep insight into specific customer segments and satisfaction levels. Downsides include low response rates and the risk of frustrating customers.
Whatever the approach, be mindful of bias—small samples, confirmation bias, and missing context can skew results. Aim for relevant, reliable data to reflect the true business landscape.
Evaluating Data: Relevance and Reliability
Not all data is equally valuable. In academics, data quality is judged by relevance (does it address the business question?) and reliability (is the sample size sufficient and is the source trustworthy?). Consistency and availability must be accessible to inform decisions.
Making Data Work for Your Organization
Protecting data integrity starts with clear change processes—define who can modify information, approval steps, and acceptable scenarios. Restrict raw data access to architects and maintain pristine copies. Establish reporting governance: specify who can create reports and remove unused ones. Without these controls, duplicate data, incorrect formulas, and incomplete records undermine accuracy.
Timely data is often messy and costly, while perfectly accurate data is elusive. Sometimes, accuracy is in the eye of the beholder. Organizations must balance the need for speed with the need for precision, making trade-offs based on business priorities.
Data is defined by four dimensions: Volume (amount of data), Velocity (speed of generation), Variety (types of data), and Veracity (quality and trustworthiness).
Data analytics isn’t just for tech giants—it’s a vital tool for organizations of all sizes. Treating data as an asset, asking the right questions, and maintaining integrity can give your company a true competitive edge. The challenge is real, but so is the opportunity. Are you ready to make data work for you?
Disclosure: This article was generated using AI technology based on previously presented content. David Peters and the South Carolina Association of CPAs team have reviewed, edited, and verified the article for accuracy, quality, and relevance.
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