Remove Data Integration Remove Data Lake Remove Data Quality Remove Document
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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 governance in the age of generative AI

AWS Big Data

Working with large language models (LLMs) for enterprise use cases requires the implementation of quality and privacy considerations to drive responsible AI. However, enterprise data generated from siloed sources combined with the lack of a data integration strategy creates challenges for provisioning the data for generative AI applications.

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

erwin

A company can’t effectively implement data governance – documenting and applying business rules and processes, analyzing the impact of changes and conducting audits – if it fails at data management. The problem usually starts by relying on manual integration methods for data preparation and mapping.

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Getting started with AWS Glue Data Quality from the AWS Glue Data Catalog

AWS Big Data

AWS Glue is a serverless data integration service that makes it simple to discover, prepare, and combine data for analytics, machine learning (ML), and application development. Hundreds of thousands of customers use data lakes for analytics and ML to make data-driven business decisions.

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Top Graph Use Cases and Enterprise Applications (with Real World Examples)

Ontotext

However, this information is typically stored in disparate locations, often hidden within departmental documents or applications. As such, most large financial organizations have moved their data to a data lake or a data warehouse to understand and manage financial risk in one place.

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How Tricentis unlocks insights across the software development lifecycle at speed and scale using Amazon Redshift

AWS Big Data

Additionally, the scale is significant because the multi-tenant data sources provide a continuous stream of testing activity, and our users require quick data refreshes as well as historical context for up to a decade due to compliance and regulatory demands. Finally, data integrity is of paramount importance.

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How data stores and governance impact your AI initiatives

IBM Big Data Hub

To optimize data analytics and AI workloads, organizations need a data store built on an open data lakehouse architecture. This type of architecture combines the performance and usability of a data warehouse with the flexibility and scalability of a data lake.