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Data governance in the age of generative AI

AWS Big Data

Data is your generative AI differentiator, and a successful generative AI implementation depends on a robust data strategy incorporating a comprehensive data governance approach. Data governance is a critical building block across all these approaches, and we see two emerging areas of focus.

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Avoid generative AI malaise to innovate and build business value

CIO Business Intelligence

Capturing the “as-is” state of your environment, you’ll develop topology diagrams and document information on your technical systems. GenAI requires high-quality data. Ensure that data is cleansed, consistent, and centrally stored, ideally in a data lake. Assess your readiness.

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Data Mesh 101: How Data Mesh Can Be Used in an Organization

Ontotext

Domain teams should continually monitor for data errors with data validation checks and incorporate data lineage to track usage. Establish and enforce data governance by ensuring all data used is accurate, complete, and compliant with regulations. Not all organizations are ready to make that investment.

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Data Preparation and Data Mapping: The Glue Between Data Management and Data Governance to Accelerate Insights and Reduce Risks

erwin

Organizations have spent a lot of time and money trying to harmonize data across diverse platforms , including cleansing, uploading metadata, converting code, defining business glossaries, tracking data transformations and so on. But the attempts to standardize data across the entire enterprise haven’t produced the desired results.

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Data Profiling: What It Is and How to Perfect It

Alation

For any data user in an enterprise today, data profiling is a key tool for resolving data quality issues and building new data solutions. In this blog, we’ll cover the definition of data profiling, top use cases, and share important techniques and best practices for data profiling today.

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How gaming companies can use Amazon Redshift Serverless to build scalable analytical applications faster and easier

AWS Big Data

A data hub is a center of data exchange that constitutes a hub of data repositories and is supported by data engineering, data governance, security, and monitoring services. A data hub contains data at multiple levels of granularity and is often not integrated.

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What Is a Data Catalog?

Alation

Figure 1 illustrates the typical metadata subjects contained in a data catalog. Figure 1 – Data Catalog Metadata Subjects. Datasets are the files and tables that data workers need to find and access. They may reside in a data lake, warehouse, master data repository, or any other shared data resource.