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Astera’s Guide to Insurance Data Quality and Governance

Astera

A strategic approach to data management is needed to meet these demands — particularly a greater focus on high data quality and robust governance to guarantee accuracy, security, and compliance. Adhering to robust governance frameworks allows insurers to ensure compliance with data privacy regulations.

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Data Observability vs. Monitoring vs. Testing

Dataversity

These products rely on a tangle of data pipelines, each a choreography of software executions transporting data from one place to another. As these pipelines become more complex, it’s important […] The post Data Observability vs. Monitoring vs. Testing appeared first on DATAVERSITY.

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Testing and Monitoring Data Pipelines: Part One

Dataversity

Suppose you’re in charge of maintaining a large set of data pipelines from cloud storage or streaming data into a data warehouse. How can you ensure that your data meets expectations after every transformation? That’s where data quality testing comes in.

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Power BI Governance, What Organisations Need to Know

BI Insight

Power BI is more than just a reporting tool; it is a comprehensive analytical platform that enables users to collaborate on data insights and share them internally and externally.

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Testing and Monitoring Data Pipelines: Part Two

Dataversity

While this technique is practical for in-database verifications – as tests are embedded directly in their data modeling efforts – it is tedious and time-consuming when end-to-end data […] The post Testing and Monitoring Data Pipelines: Part Two appeared first on DATAVERSITY.

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4 Key Takeaways for Your Data Quality Journey

Dataversity

The road to better Data Quality is a path most data-driven organizations are already on. The path becomes bumpy for organizations when stakeholders are constantly dealing with data that is either incomplete or inaccurate. That scenario is far too familiar for most organizations and creates a lack of trust in Data Quality.

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The Ultimate Guide to Modern Data Quality Management (DQM) For An Effective Data Quality Control Driven by The Right Metrics

Data Pine

1) What Is Data Quality Management? 4) Data Quality Best Practices. 5) How Do You Measure Data Quality? 6) Data Quality Metrics Examples. 7) Data Quality Control: Use Case. 8) The Consequences Of Bad Data Quality. 9) 3 Sources Of Low-Quality Data.