Why Data Quality Matters to Different Users Across the Enterprise

Few would question the value of data quality in the enterprise today. A survey by Wakefield Research of data management professionals found that over half of the respondents indicated that 25% or more of their revenue was affected by data quality issues, underscoring the direct impact of data quality on a company's bottom line. ​That same survey found an increase in data downtime, partially explained by a 166% increase in the average time to resolve data quality issues, rising to an average of 15 hours per incident, highlighting the significant operational inefficiencies poor data quality can cause.

Data quality is often discussed in broad terms, but its true impact varies significantly depending on the user. While a data governance leader might be concerned with regulatory compliance, a data analyst focuses on ensuring the accuracy of reports. Understanding the importance of data quality for different personas within an enterprise helps organizations optimize their data management strategies, improve decision-making, and drive efficiency.

The role of data quality for different enterprise users

Data quality influences various roles within an organization uniquely, shaping responsibilities, priorities, and the effectiveness of decision-making processes. Each persona depends on accurate, consistent, and timely data to fulfill their specific functions. Highlighting these distinct needs can help enterprises tailor their data quality strategies effectively to support all stakeholders.

Chief Data Officer

Chief Data Officers (CDOs) define and drive organizational data strategies, prioritizing data as a strategic asset. Their effectiveness relies heavily on the quality of the data available, as accurate data forms the foundation of successful data-driven initiatives and digital transformations. Poor data quality undermines strategic goals, potentially jeopardizing high-level initiatives and diminishing trust across the organization. Ensuring robust data quality enables CDOs to confidently implement strategic initiatives that align closely with business objectives.

Chief Information Officer

Chief Information Officers (CIOs) oversee technology infrastructures and systems that depend on quality data for optimal performance. CIOs require reliable data to justify technology investments, enhance system performance, and support security and compliance. Poor data quality can lead to operational disruptions, inflated costs, and compliance issues, affecting both IT effectiveness and overall enterprise efficiency. Maintaining high-quality data ensures CIOs can confidently invest in and manage technology assets, enabling the enterprise to leverage technology effectively.

Data governance leader

Data governance leaders ensure organizational compliance, security, and consistent application of data standards. Their role depends critically on the quality and integrity of the data. Without high-quality data, compliance efforts become significantly more challenging, increasing risks of regulatory penalties and reputational damage. Reliable data quality empowers governance leaders to confidently enforce policies, ensuring both compliance and operational integrity across the enterprise.

Catalog program manager

Catalog program managers oversee data management and governance, ensuring data is properly classified, accessible, and aligned with enterprise standards. Poor data quality hinders their ability to enforce governance policies, creating inconsistency and confusion across teams. Inaccurate or incomplete data complicates the construction and maintenance of reliable catalogs. Ultimately, maintaining high-quality data empowers catalog program managers to effectively manage enterprise data, ensuring streamlined processes and consistent governance.

Technical data steward

Technical data stewards ensure datasets are properly documented, validated, and maintained across business functions. They rely on high-quality data to ensure records are consistent and usable. Poor data quality increases troubleshooting efforts, pulling stewards away from proactive maintenance and strategic tasks. Creating a solid data-quality foundation in the enterprise allows data stewards to focus on enhancing data usability, consistency, and reliability.

Technical data analyst

Data analysts create reports and insights critical for informed decision-making. Their effectiveness hinges on accurate, timely, and complete data. Poor quality data leads to misleading analytics and potentially flawed business strategies. Ensuring high-quality data enables analysts to generate reliable insights, driving accurate business decisions and improving organizational outcomes.

Business insights consumer

Executives, marketing teams, and operational managers use data-driven dashboards and BI tools to inform strategic and tactical decisions. High-quality data ensures confidence in decision-making processes, improving operational efficiency and effectiveness. Conversely, poor data quality can lead to misguided decisions, financial losses, customer dissatisfaction, and compliance risks. Ensuring data accuracy and reliability allows business consumers to make confident, informed decisions that support organizational success.

Technical data engineer

Data engineers build and maintain data pipelines that facilitate seamless data movement within the organization. High-quality data is crucial for efficient processing, pipeline reliability, and system performance. Poor data quality results in processing errors, system inefficiencies, and wasted resources. Maintaining clean and reliable data ensures engineers can build robust, efficient systems that streamline data accessibility and enhance operational efficiency.

Aligning data quality with user needs

To ensure that data quality meets the needs of every persona, enterprises should:

• Implement strong data governance: Establish policies that define data standards and ensure compliance.

• Invest in automation: Use tools that continuously validate, clean, and monitor data quality.

• Enable cross-functional collaboration: Foster communication between technical and business users to align expectations.

• Prioritize data literacy: Train users across departments to understand and effectively use data.

By addressing the unique needs of each user persona, organizations can maximize the value of their data, fostering a culture of data-driven excellence and building a strong foundation for smarter, more effective decision-making.

Curious to learn how a data catalog can empower you to improve data quality for all your data producers and consumers? Book a demo with us today.