Data quality is a business priority. Every day your business gathers data, including confidential and sensitive information regarding customers, target markets, internal finances, and more. With continually evolving privacy and compliance regulations, such as CCPA (California Consumer Privacy Act), CPRA (California Privacy Rights Act) and GDPR (General Data Protection Regulation), maintaining regulatory standards is necessary for avoiding fines, but it also helps you protect your business and retain your customer trust.

Is your business prepared to prevent potentially devastating complications that could wipe out significant revenue? Low-quality data is a disaster waiting to happen, and the best time to determine your plan is before disaster strikes.

Consequences of low-quality data

The consequences of low-quality data include:

  • Diminished reputation.
  • Fines for inaccurate or late reporting to regulatory bodies.
  • Inferior decision-making.
  • Loss of revenue.

And that’s not all. It costs employee time to investigate, identify, and correct problems, and in the worst cases, even equipment and inventory can be casualties.

Regulatory compliance

The most obvious consequence of noncompliance is the regulatory fines your company can incur. Data quality is required to meet standards for privacy, security, accuracy, reporting, and shareability. Did you know, for example, that GDPR requires businesses to correct inaccurate or incomplete personal data?

But there are benefits to complying with data quality rules and regulations besides avoiding fines. Adhering to regulations helps you ensure overall data quality, which leads to higher efficiency, a streamlined customer experience, and increased revenue over time.

Following regulations establishes the minimum baseline for your data quality. From there, you can improve upon your data quality until it reflects your desired management criteria. Getting ahead of regulatory compliance, rather than letting it catch you off guard, can be the difference between floundering and flourishing.

Data quality management criteria

Analyzing your data quality needs, and the risks associated with neglecting them, allows you to create the best management criteria for maintaining and optimizing your company’s data quality. General management criteria include:

  • Accuracy. One of the primary factors for data quality management is data accuracy. Data that doesn’t accurately reflect its source is useless at best and catastrophic at worst. Inaccurate data takes time and effort to track and correct. So, protocols for regularly updating data and verifying its accuracy are an essential piece of your management criteria puzzle.
  • Consistency. When gathering data, it should be internally consistent regarding what is recorded and how. Standardize forms, field rules, and use consistent formats and formulas for dates, measurements, and other information. You should also ensure data is consistent with reality. If a record shows an employee’s birth year occurring after the year they were hired, for instance, it is inconsistent with reality.
  • Duplication. Duplicate records occur when data is collected from multiple sources. Duplicates get a bad rap, but it’s important to identify and evaluate them carefully to determine and optimize their value.
  • Completeness. Empty fields in data sets and/or missing data sets are examples of completion issues that should be addressed in your company’s data quality management criteria.

Formal, standardized data quality management is necessary for regulatory compliance regarding data privacy and security.

Data quality management solution

Unaddressed data quality issues can quickly get expensive, whether in regulatory fines, inefficient business activity, poor business decisions, or all of the above. Data management criteria and compliance regulations head off potential data issues in your business. But a total data quality management solution — designed to meet the unique needs of your company — organizes and optimizes your data to correct existing quality issues and avoid future problems.

Learn more about total data quality management solutions for your business at