CIO Business Intelligence

OCTOBER 24, 2024

As they look to operationalize lessons learned through experimentation, they will deliver short-term wins and successfully play the gen AI — and other emerging tech — long game,” Leaver said. They predicted more mature firms will seek help from AI service providers and systems integrators.

ROI 127

ROI Data-driven Enterprise Experimentation 127

AWS Big Data

JANUARY 15, 2025

Need for a data mesh architecture Because entities in the EUROGATE group generate vast amounts of data from various sourcesacross departments, locations, and technologiesthe traditional centralized data architecture struggles to keep up with the demands for real-time insights, agility, and scalability.

IoT 110

IoT Machine Learning Metadata Data-driven 110

AWS Big Data

OCTOBER 19, 2023

Data lakes and data warehouses are two of the most important data storage and management technologies in a modern data architecture. Data lakes store all of an organization’s data, regardless of its format or structure. Various data stores are supported in AWS Glue; for example, AWS Glue 4.0

Data Lake 137

Data Lake Data Warehouse Visualization Snapshot 137

IBM Big Data Hub

JANUARY 17, 2023

This capability will provide data users with visibility into origin, transformations, and destination of data as it is used to build products. The result is more useful data for decision-making, less hassle and better compliance. Data integration. Data science and MLOps. AI is no longer experimental.

Data-driven 56

Data-driven Enterprise Data Governance Data Science 56

AWS Big Data

JULY 20, 2023

With data becoming the driving force behind many industries today, having a modern data architecture is pivotal for organizations to be successful. These robust capabilities ensure that data within the data lake remains accurate, consistent, and reliable.

Data Lake 98

Data Lake Analytics Snapshot Data Quality 98

CIO Business Intelligence

JUNE 16, 2022

A real-time data technology stack has to shrink this innovation gap for the business. . Analysts and data scientists need flexibility when working with data; experimentation fuels the development of analytics and machine learning models. Often, enterprise data ecosystems are built with a mindset that’s too narrow.

IT 135

IT Experimentation IoT Data-driven 135