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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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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.

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How Can The Adoption of a Data Platform Simplify Data Governance For An Organization?

Pickl AI

Falling into the wrong hands can lead to the illicit use of this data. Hence, adopting a Data Platform that assures complete data security and governance for an organization becomes paramount. In this blog, we are going to discuss more on What are Data platforms & Data Governance. Wrapping it up !!!

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Unlocking the 12 Ways to Improve Data Quality

Pickl AI

Whether you are a business executive making critical choices, a scientist conducting groundbreaking research, or simply an individual seeking accurate information, data quality is a paramount concern. The Relevance of Data Quality Data quality refers to the accuracy, completeness, consistency, and reliability of data.

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Comparing Tools For Data Processing Pipelines

The MLOps Blog

A typical data pipeline involves the following steps or processes through which the data passes before being consumed by a downstream process, such as an ML model training process. Data Ingestion : Involves raw data collection from origin and storage using architectures such as batch, streaming or event-driven.

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Principal Financial Group uses AWS Post Call Analytics solution to extract omnichannel customer insights

AWS Machine Learning Blog

In this post, we demonstrate how data aggregated within the AWS CCI Post Call Analytics solution allowed Principal to gain visibility into their contact center interactions, better understand the customer journey, and improve the overall experience between contact channels while also maintaining data integrity and security.