The paradox of data quality

• Pursuing perfect data quality can drain resources and detract from other priorities, making a balanced, risk-based approach more effective.
• Focusing on high-impact data with proactive measures upstream ensures essential data is accurate without over-investing in low-priority areas.
• A strong data quality foundation with automated tools and clear standards helps maximize ROI, reduce risks, and improve decision-making.

The paradox of data quality lies in balancing accuracy with practicality. While quality data is critical for informed decision-making, striving for perfection is costly and unsustainable. According to Pepar Hugo, Senior Data Engineer at Lumenalta, an excessive focus on flawless data can create “analysis paralysis,” consuming valuable resources that could be better allocated to higher priorities. To counter this, organizations are encouraged to address data issues upstream—akin to refining a production mold—thereby reducing recurring errors and enhancing efficiency.

Implementing a risk-based approach, organizations categorize data by impact. High-impact data, which influences core decisions like financial reports, is managed rigorously with validation, cleanups, and real-time monitoring. Moderate-impact data, crucial but less critical, requires less stringent oversight, while low-impact data, like social metrics, can follow basic quality checks. This framework optimizes resources, minimizes risk, and ensures critical information meets standards without overextending efforts on less vital data.

Automation tools bolster this strategy, enabling efficient data cleansing, validation, and lineage tracking. Companies can maintain reliable data quality by embedding clear roles, data quality standards, and robust issue resolution into a comprehensive governance framework. Ultimately, a risk-based approach offers practical balance, ensuring data is “good enough” for its purpose while supporting sustainable operations and strategic decision-making.

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