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The importance of data ingestion and integration for enterprise AI

IBM Journey to AI blog

In the generative AI or traditional AI development cycle, data ingestion serves as the entry point. Here, raw data that is tailored to a company’s requirements can be gathered, preprocessed, masked and transformed into a format suitable for LLMs or other models. A popular method is extract, load, transform (ELT).

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What is Data Ingestion? Understanding the Basics

Pickl AI

Summary: Data ingestion is the process of collecting, importing, and processing data from diverse sources into a centralised system for analysis. This crucial step enhances data quality, enables real-time insights, and supports informed decision-making. This is where data ingestion comes in.

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Re-evaluating data management in the generative AI age

IBM Journey to AI blog

Enterprise data is often complex, diverse and scattered across various repositories, making it difficult to integrate into gen AI solutions. This complexity is compounded by the need to ensure regulatory compliance, mitigate risk, and address skill gaps in data integration and retrieval-augmented generation (RAG) patterns.

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Improving air quality with generative AI

AWS Machine Learning Blog

The solution addressed in this blog solves Afri-SET’s challenge and was ranked as the top 3 winning solutions. This post presents a solution that uses a generative artificial intelligence (AI) to standardize air quality data from low-cost sensors in Africa, specifically addressing the air quality data integration problem of low-cost sensors.

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Introduction to Apache NiFi and Its Architecture

Pickl AI

Summary: Apache NiFi is a powerful open-source data ingestion platform design to automate data flow management between systems. Its architecture includes FlowFiles, repositories, and processors, enabling efficient data processing and transformation. What is Apache NiFi?

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How IBM HR leverages IBM Watson® Knowledge Catalog to improve data quality and deliver superior talent insights

IBM Journey to AI blog

For instance, weekly talent reports generated for IBM’s CHRO and CEO needed to be 100% clear of inaccuracies in the data. What’s more, while the HR team members had scripts to check for data ingestion errors and data integrity, they lacked a solution that could proactively identified business errors within the data.

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Data architecture strategy for data quality

IBM Journey to AI blog

Both approaches were typically monolithic and centralized architectures organized around mechanical functions of data ingestion, processing, cleansing, aggregation, and serving. Learn more about the benefits of data fabric and IBM Cloud Pak for Data.