Top 8 Ways to Improve Decision Making with Better Data Quality

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Six million pages were found for the term “data quality” in the search engine results. This demonstrates the importance of data quality in decision-making contexts and the crucial role it plays in determining the outcome.

Understanding the data is key to its classification and qualification for the best possible use in the specific scenario.

Understanding Data Quality

Data of high quality is reliable, consistent, and scalable. Data should be useful in planning, operations, decision-making, and strategic growth initiatives.

Poor quality data can lead to delayed system deployments, damaged reputations, reduced productivity, flawed decision-making, and lost revenue. In 2026, organizations continue to face these challenges as data volumes grow exponentially across industries.

A report by the Data Warehousing Institute shows that poor customer data costs U.S. companies approximately $611 billion annually. Insufficient data quality is also a major reason for losses in 40% of companies, according to the research.

Seven Characteristics that Define Data Quality

Accuracy — Does this data accurately reflect the real-world object?

Integrity: Have the data remained unchanged and undamaged between updates?

Consistency — Does the information across all systems remain consistent?

Completeness — How complete are the data?

Validity — Does the information correspond to a particular format or range as defined by the business?

Timing: Are the details up-to-date? Is it possible to use it for decision-making?

Accessibility — Are the data easy to access, understandable and usable?

Data quality is determined by several elements. Each organization chooses which features to prioritize based on its needs. This can vary depending on the industry, stage of growth, or current business cycle. Identifying the key elements when evaluating data helps organizations use it effectively to reach their business goals.

Ways to Improve Data Quality

Recognize the Importance of Data Quality

Data drives business. Organizations must empower primary users—not IT departments—to determine the parameters of quality data. Business intelligence closely tied to underlying data increases the likelihood that companies can adopt effective methods for selecting crucial data.

Avoid Thinking in Isolation

Accuracy standards differ across data types. Data quality is not a one-size-fits-all policy. Different data sources can provide varying quality and metrics. For example, 80 percent accuracy for sentiment analysis of social media data is often sufficient, whereas industries such as BFSI require much higher precision and fine-tuning before analysis.

Focus on Each Stage of the Data Journey

With a holistic approach to adopting a data strategy for the enterprise, every organization can become data-driven, maximize technology investments, and reduce costs. In 2026, data remains a valuable strategic asset in such situations.

Don’t Store Unnecessary Data

Every day, organizations capture and use data across a range of operations. The greater the volume of data, the higher the margin of error. Accepting that data isn’t always perfect is essential. This mindset helps businesses recognize limitations, build on successes, and quickly identify issues before they escalate.

Take Responsibility

The quality of data varies between organizations based on their size, financial health, and data strategies. Poor data quality is everyone’s responsibility within the company. This is a business problem, not solely an IT issue. Companies can increase efficiency, reduce costs, and improve decision-making by taking ownership of data quality.

Use Data Pipeline Design to Avoid Duplicate Data

A copy of data can be either a complete or partial duplicate created from the same source. Human errors cause most data duplications. Incorrect reporting can lead to lost productivity and wasted marketing budgets. To avoid duplicate data, an enterprise-level data pipeline must be established and shared throughout the organization.

Implement a Data Governance Strategy

The best way to improve data quality is to clearly define who, what, how much, where, when, and why. These policies should be followed by everyone within the company. Documenting the policies so they can be easily accessed helps improve business performance and security.

Invest in Internal Training

This can be a transformational approach. Good data quality requires experience and expertise, which can be challenging for entry-level executives. Formal training helps teams manage data effectively, recognize its intrinsic value, and encourages executives to learn basic concepts, principles, and quality management practices to gain a competitive advantage. This builds understanding of the benefits of high-quality data and the potential costs of poor data quality.

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Conclusion

According to KPMG Global’s study, 84% of CEOs are concerned about data quality when making decisions. It is vital to establish trust and transparency about data quality, as CEOs hold ultimate decision-making authority. In 2026, this focus enables organizations to save time, reduce costs, make informed business decisions, and obtain accurate analytics that improve business performance.

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