Data Integrity: The Last Mile Problem of Data Observability

Data Integrity

Data quality issues have been a long-standing challenge for data-driven organizations. Even with significant investments, the trustworthiness of data in most organizations is questionable at best. Gartner reports that companies lose an average of $14 million per year due to poor data quality.  Data observability has been all the rage in data management circles for a…

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Why do Data Quality Programs Fail?

Data Quality Programs

Fortune 1000 organizations spend approximately $5 billion each year to improve the trustworthiness of data. Yet only 42 percent of the executives trust their data. According to multiple surveys, executives across industries do not completely trust the data in their organization for accurate, timely business critical decision-making. In addition, organizations routinely incur operational losses, regulatory…

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How to Scale Your Data Quality Operations with AI and ML

scale data quality operations

How can you cost-effectively scale your data quality operations as your business scales? The key is to employ artificial intelligence and machine learning technology that can take on an increasing amount of the data quality management chores as they learn more about your organization’s data. It’s all in the about making the best use of…

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Autonomous Cloud Data Pipeline Control: Tools and Metrics

data pipeline monitoring tools and metrics

Errors that get seeded in the data as it flows through the data pipeline, propagate throughout the organization and are responsible for 80% of all errors impacting the business users. High-quality data is essential to the success of any business or organization. It’s important to monitor your data pipeline to guard against missing, incorrect, old/not-fresh,…

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What is Data Monitoring and Why Do You Need It?

First Eigen

What is Data Monitoring and Why Do You Need It? Organizations leverage information to make decisions that affect people and revenue. Data monitoring plays a role by ensuring that any data submitted is reliable and meets the criteria of an organization. It keeps checking the data to verify that it still holds to the rules…

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