IBM Journey to AI blog

APRIL 20, 2023

This trust depends on an understanding of the data that inform risk models: where does it come from, where is it being used, and what are the ripple effects of a change? Moreover, banks must stay in compliance with industry regulations like BCBS 239, which focus on improving banks’ risk data aggregation and risk reporting capabilities.

ETL 217

ETL Data Discovery Automation Metadata 217

Marktechpost

JULY 18, 2023

Extract, Transform, and Load are referred to as ETL. ETL is the process of gathering data from numerous sources, standardizing it, and then transferring it to a central database, data lake, data warehouse, or data store for additional analysis. Involved in each step of the end-to-end ETL process are: 1.

ETL 52

ETL Data Integration Business Intelligence Automation 52

The MLOps Blog

MAY 17, 2023

However, efficient use of ETL pipelines in ML can help make their life much easier. This article explores the importance of ETL pipelines in machine learning, a hands-on example of building ETL pipelines with a popular tool, and suggests the best ways for data engineers to enhance and sustain their pipelines.

ETL 59

ETL ML Machine Learning Data Scientist 59

IBM Journey to AI blog

JANUARY 5, 2023

The right data architecture can help your organization improve data quality because it provides the framework that determines how data is collected, transported, stored, secured, used and shared for business intelligence and data science use cases. As previously mentioned, a data fabric is one such architecture.

Data Quality 130

Data Quality Metadata Big Data ETL 130

IBM Journey to AI blog

JULY 3, 2024

Align your data strategy to a go-forward architecture, with considerations for existing technology investments, governance and autonomous management built in. Look to AI to help automate tasks such as data onboarding, data classification, organization and tagging.

Data Quality 112

Data Quality Metadata Business Intelligence AI 112

Pickl AI

JULY 25, 2024

Enhanced Data Utilisation Effective ingestion unlocks the full potential of data by making it available for advanced analytics, machine learning, and artificial intelligence applications, driving innovation and business growth. Data Ingestion Tools To facilitate the process, various tools and technologies are available.

Data Ingestion 52

Data Ingestion ETL Data Quality Data Integration 52

IBM Journey to AI blog

OCTOBER 30, 2024

IBM watsonx™ can be used to automate the identification of regulatory obligations and map legal and regulatory requirements to a risk governance framework. This approach enables centralized access and sharing while minimizing extract, transform and load (ETL) processes and data duplication.

Prompt Engineering 238

Prompt Engineering Prompt Engineer ETL Machine Learning 238