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Data marts involved the creation of built-for-purpose analytic repositories meant to directly support more specific business users and reporting needs (e.g., 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.
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.
In the previous blog , we discussed how Alation provides a platform for data scientists and analysts to complete projects and analysis at speed. In this blog we will discuss how Alation helps minimize risk with active datagovernance. So why are organizations not able to scale governance? Meet Governance Requirements.
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. A high overall score indicates that a dataset is reliable, easily accessible, and relevant.
In Part 1 and Part 2 of this series, we described how data warehousing (DW) and businessintelligence (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 […].
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 businessintelligence and data science use cases. Reduce data duplication and fragmentation.
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. If any of these is missing, the client data is considered incomplete.
A data catalog is a metadata repository of information sources across the enterprise, including data sets, businessintelligence reports, visualizations, and conversations. Early on, analysts used data catalogs to find and understand data more quickly.
It asks much larger questions, which flesh out an organization’s relationship with data: Why do we have data? Why keep data at all? Answering these questions can improve operational efficiencies and inform a number of dataintelligence use cases, which include datagovernance, self-service analytics, and more.
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