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Data integrity vs. data quality: Is there a difference?

IBM Journey to AI blog

When we talk about data integrity, we’re referring to the overarching completeness, accuracy, consistency, accessibility, and security of an organization’s data. Together, these factors determine the reliability of the organization’s data. In short, yes.

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Enrich your AWS Glue Data Catalog with generative AI metadata using Amazon Bedrock

Flipboard

Metadata can play a very important role in using data assets to make data driven decisions. Generating metadata for your data assets is often a time-consuming and manual task. First, we explore the option of in-context learning, where the LLM generates the requested metadata without documentation.

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The importance of data ingestion and integration for enterprise AI

IBM Journey to AI blog

The entire generative AI pipeline hinges on the data pipelines that empower it, making it imperative to take the correct precautions. 4 key components to ensure reliable data ingestion Data quality and governance: Data quality means ensuring the security of data sources, maintaining holistic data and providing clear metadata.

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9 data governance strategies that will unlock the potential of your business data

IBM Journey to AI blog

To maximize the value of their AI initiatives, organizations must maintain data integrity throughout its lifecycle. Managing this level of oversight requires adept handling of large volumes of data. Just as aircraft, crew and passengers are scrutinized, data governance maintains data integrity and prevents misuse or mishandling.

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ApertureData Secures $8.25M Seed Funding and Launches ApertureDB Cloud to Revolutionize Multimodal AI

Unite.AI

The funding will allow ApertureData to scale its operations and launch its new cloud-based service, ApertureDB Cloud, a tool designed to simplify and accelerate the management of multimodal data, which includes images, videos, text, and related metadata. ApertureData’s flagship product, ApertureDB , addresses this challenge head-on.

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How data stores and governance impact your AI initiatives

IBM Journey to AI blog

Among the tasks necessary for internal and external compliance is the ability to report on the metadata of an AI model. Metadata includes details specific to an AI model such as: The AI model’s creation (when it was created, who created it, etc.)

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A Beginner’s Guide to Data Warehousing

Unite.AI

ETL ( Extract, Transform, Load ) Pipeline: It is a data integration mechanism responsible for extracting data from data sources, transforming it into a suitable format, and loading it into the data destination like a data warehouse. The pipeline ensures correct, complete, and consistent data.

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