AWS Machine Learning Blog

APRIL 3, 2025

By analyzing their data, organizations can identify patterns in sales cycles, optimize inventory management, or help tailor products or services to meet customer needs more effectively. This tool democratizes data access across the organization, enabling even nontechnical users to gain valuable insights.

AWS 94

AWS Data Governance Data Silos SQL 94

IBM Journey to AI blog

MAY 9, 2023

Supporting the data management life cycle According to IDC’s Global StorageSphere, enterprise data stored in data centers will grow at a compound annual growth rate of 30% between 2021-2026. [2] ” Notably, watsonx.data runs both on-premises and across multicloud environments.

Data Warehouse 92

Data Warehouse AWS AI AI 92

IBM Journey to AI blog

MAY 15, 2024

According to International Data Corporation (IDC), stored data is set to increase by 250% by 2025 , with data rapidly propagating on-premises and across clouds, applications and locations with compromised quality. As a result, users boost pipeline performance while ensuring data security and controls.

Data Pipeline 85

Data Pipeline ETL SQL Database 85

phData

MAY 25, 2023

Fivetran Fivetran is an automated data integration platform that offers a convenient solution for businesses to consolidate and sync data from disparate data sources. With over 160 data connectors available, Fivetran makes it easy to move supply chain data across any cloud data platform in the market.

Data Silos 52

Data Silos System Architecture Cloud Data Data Analyst 52

Alation

NOVEMBER 16, 2021

Data growth, shrinking talent pool, data silos – legacy & modern, hybrid & cloud, and multiple tools – add to their challenges. According to Gartner, “Through 2025, 80% of organizations seeking to scale digital business will fail because they do not take a modern approach to data and analytics governance.”.

Data Governance 52

Data Governance Data Scientist SQL Data Silos 52

phData

SEPTEMBER 27, 2024

Some modern CDPs are starting to incorporate these concepts, allowing for more flexible and evolving customer data models. It also requires a shift in how we query our customer data. Instead of simple SQL queries, we often need to use more complex temporal query languages or rely on derived views for simpler querying.

Data Modeling 52

Data Modeling Data Models Apache Kafka Data Lakes 52