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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 DataGovernance application.
A well-designed data architecture should support businessintelligence and analysis, automation, and AI—all of which can help organizations to quickly seize market opportunities, build customer value, drive major efficiencies, and respond to risks such as supply chain disruptions.
This is the practice of creating, updating and consistently enforcing the processes, rules and standards that prevent errors, data loss, data corruption, mishandling of sensitive or regulated data, and data breaches.
And because data assets within the catalog have quality scores and social recommendations, Alex has greater trust and confidence in the data she’s using for her decision-making recommendations. This is especially helpful when handling massive amounts of big data. Protected and compliant data.
The implementation of a data vault architecture requires the integration of multiple technologies to effectively support the design principles and meet the organization’s requirements. Having model-level data validations along with implementing a dataobservability framework helps to address the data vault’s data quality challenges.
We already know that a data quality framework is basically a set of processes for validating, cleaning, transforming, and monitoring data. DataGovernanceDatagovernance is the foundation of any data quality framework. It primarily caters to large organizations with complex data environments.
And the desire to leverage those technologies for analytics, machine learning, or businessintelligence (BI) has grown exponentially as well. Read our eBook 4 Ways to Measure Data Quality and learn more about the variety of data and metrics that organizations can use to measure data quality.
Multiple data applications and formats make it harder for organizations to access, govern, manage and use all their data for AI effectively. Scaling data and AI with technology, people and processes Enabling data as a differentiator for AI requires a balance of technology, people and processes.
In its essence, data mesh helps with dataobservability — another important element every organization should consider. With granular access controls, data lineage, and domain-specific audit logs, data catalogs allow engineers and developers to have a better view of their systems than before. Train the teams.
Data democratization has become a hot topic lately with advances in technology such as AI and machine learning, cloud storage and scalable server capacity, and improved integration. Then add self-service businessintelligence tools that are accessible to virtually anyone.
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