IBM Big Data Hub

AUGUST 23, 2023

Because of this, when we look to manage and govern the deployment of AI models, we must first focus on governing the data that the AI models are trained on. This data governance requires us to understand the origin, sensitivity, and lifecycle of all the data that we use. Some of these risks include: 1.

Data Governance 58

Data Governance Enterprise Modeling Risk 58

IBM Big Data Hub

DECEMBER 16, 2022

It also serves as a governance tool to drive compliance with data privacy and industry regulations. In other words, a data catalog makes the use of data for insights generation far more efficient across the organization, while helping mitigate risks of regulatory violations. Protected and compliant data.

Metadata 52

Metadata Data Quality Data-driven Data Governance 52

IBM Big Data Hub

JANUARY 9, 2024

Companies still often accept the risk of using internal data when exploring large language models (LLMs) because this contextual data is what enables LLMs to change from general-purpose to domain-specific knowledge. In the generative AI or traditional AI development cycle, data ingestion serves as the entry point.

Enterprise 83

Enterprise Data Integration Data Quality Contextual Data 83

erwin

JULY 17, 2019

EA and BP modeling squeeze risk out of the digital transformation process by helping organizations really understand their businesses as they are today. In fact, data professionals spend 80 percent of their time looking for and preparing data and only 20 percent of their time on analysis, according to IDC. Do it faster.

Digital Transformation 98

Digital Transformation Strategy Metadata Data-driven 98