November, 2023

Remove data-quality-data-governance
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Nailing Data Basics Rivals the Allure of AI—and Other Surprising Takeaways from Customers Down Under

Domo

Data matters. AI also shone a harsh light on data quality, revealing biases and gaps in Australian data sources—ones that were previously hidden by existing analytical regimes. The post Nailing Data Basics Rivals the Allure of AI—and Other Surprising Takeaways from Customers Down Under first appeared on Blog.

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Data Governance in Financial Services: How to Ensure Data Integrity for Risk Management and Reporting

Astera

Introduction   As financial institutions navigate intricate market dynamics and heighten regulatory requirements, the need for reliable and accurate data has never been more pronounced. This has spotlighted data governance—a discipline that shapes how data is managed, protected, and utilized within these institutions.

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Data Profiling: Types, Techniques and Best Practices

Astera

Clean and accurate data is the foundation of an organization’s decision-making processes. However, studies reveal that only 3% of the data in an organization meets basic data quality standards, making it necessary to prepare data effectively before analysis. This is where data profiling comes into play.

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Data Pipeline Monitoring: Metrics and Best Practices

Astera

A report by KPMG indicates that only 35% of executives report a high level of trust in an organization’s use of data and analytics. The other 65% face difficulties transporting and using data. This highlights the need for effective data pipeline monitoring. What is Data Pipeline Monitoring?

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How to Overcome the Pain Points of Your CRM

However, as a company, sales stack, and database grow, it becomes difficult to uphold structure and governance to keep a CRM up-to-date. Combatting low adoption rates and data quality. When used effectively, a CRM can be the lifeblood of your sales team – keeping everyone organized, efficient, and at peak productivity.

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Data Vault 2.0: What You Need to Know

Astera

With rising data volumes, dynamic modeling requirements, and the need for improved operational efficiency, enterprises must equip themselves with smart solutions for efficient data management and analysis. This is where Data Vault 2.0 It supersedes Data Vault 1.0, It supersedes Data Vault 1.0, Data Vault 2.0

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The Cool Kids Corner: Data Quality Is Not a Fish You Can Catch 

Dataversity

This is my monthly check-in to share with you the people and ideas I encounter as a data evangelist with DATAVERSITY. This month we’re talking about Data Quality (DQ). Read last month’s column here.)