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Alation 2022.2: Open Data Quality Initiative and Enhanced Data Governance

Alation

generally available on May 24, Alation introduces the Open Data Quality Initiative for the modern data stack, giving customers the freedom to choose the data quality vendor that’s best for them with the added confidence that those tools will integrate seamlessly with Alation’s Data Catalog and Data Governance application.

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Data Integrity for AI: What’s Old is New Again

Precisely

The magic of the data warehouse was figuring out how to get data out of these transactional systems and reorganize it in a structured way optimized for analysis and reporting. But those end users werent always clear on which data they should use for which reports, as the data definitions were often unclear or conflicting.

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Effective strategies for gathering requirements in your data project

Dataconomy

Define data needs : Specify datasets, attributes, granularity, and update frequency. Address data governance : Ensure requirements include compliance with regulations like GDPR or CCPA. Key questions to ask: What data sources are required? Are there any data gaps that need to be filled?

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How data engineers tame Big Data?

Dataconomy

Creating data pipelines and workflows Data engineers create data pipelines and workflows that enable data to be collected, processed, and analyzed efficiently. By creating efficient data pipelines and workflows, data engineers enable organizations to make data-driven decisions quickly and accurately.

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Unlocking the 12 Ways to Improve Data Quality

Pickl AI

This proactive approach allows you to detect and address problems before they compromise data quality. Data Governance Framework Implement a robust data governance framework. Define data ownership, access rights, and responsibilities within your organization. How Do You Fix Poor Data Quality?

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Data architecture strategy for data quality

IBM Journey to AI blog

The right data architecture can help your organization improve data quality because it provides the framework that determines how data is collected, transported, stored, secured, used and shared for business intelligence and data science use cases. Reduce data duplication and fragmentation.

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Avoid These Mistakes on Your Data Warehouse and BI Projects: Part 3

Dataversity

In Part 1 and Part 2 of this series, we described how data warehousing (DW) and business intelligence (BI) projects are a high priority for many organizations. Project sponsors seek to empower more and better data-driven decisions and actions throughout their enterprise; they intend to expand their […].