IBM Big Data Hub

OCTOBER 16, 2023

It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. It encompasses risk management and regulatory compliance and guides how AI is managed within an organization.

Risk 73

Risk Modeling Management Metadata 73

erwin

APRIL 29, 2020

These tools range from enterprise service bus (ESB) products, data integration tools; extract, transform and load (ETL) tools, procedural code, application program interfaces (API)s, file transfer protocol (FTP) processes, and even business intelligence (BI) reports that further aggregate and transform data.

Metadata 111

Metadata Data Governance Key Performance Indicator Data Quality 111

TDAN

JUNE 15, 2021

We have seen an impressive amount of hype and hoopla about “data as an asset” over the past few years. And one of the side effects of the COVID-19 pandemic has been an acceleration of data transformation in organisations of all sizes.

Data Transformation 98

Data Transformation Management Risk Management Risk 98

CIO Business Intelligence

JUNE 14, 2022

I think that speaks volumes to the type of commitment that organizations have to make around data in order to actually move the needle.”. So if funding and C-suite attention aren’t enough, what then is the key to ensuring an organization’s data transformation is successful? Analytics, Chief Data Officer, Data Management

Data Transformation 145

Data Transformation Data-driven Data Strategy Strategy 145

Jet Global

MAY 9, 2024

Data pipelines are designed to automate the flow of data, enabling efficient and reliable data movement for various purposes, such as data analytics, reporting, or integration with other systems. This can include tasks such as data ingestion, cleansing, filtering, aggregation, or standardization.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52