Data Quality Management: Mitigating Data Corrosion in the Age of Big Data Analytics

Data quality management is not a one-time task but an evolving process. By continually adapting tools and tactics to meet the growing complexity of modern datasets, operators can protect their data from unseen issues, enhance workflow efficiency, and gain a competitive edge through faster, more confident decision-making.

In today’s data-driven world, poor-quality data can corrode performance just as surely as rust weakens pipelines. Internal and external forces can create “pinholes” within our data without us ever seeing a problem. This severely reduces data quality and ultimately leads to failures in analysis. Therefore, the same diligence that maintains our pipelines should be used to protect the integrity of our data.

As the cathodic protection (CP) industry transitions from paper-based methods to advanced digital systems, maintaining accurate, consistent, and actionable data has become essential. Pipeline operators have started to adopt relevant technology for gathering and storing CP data. However, those technologies are not always fully leveraged to improve data quality.

This whitepaper examines how effective data quality management practices can safeguard the integrity of CP data, ensuring that organizations extract maximum value from their technology investments. Download your free copy now.

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