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6 key components of a successful data strategy
These six elements are essential parts of an enterprise data strategy that will help meet business needs for information when paired with a solid data architecture.
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Hybrid search demands reshape retrieval frameworks for AI
As AI workloads mature, enterprises face multiple data platform choices to improve search and retrieval capabilities while meeting governance and operational demands.
News
Starburst intros AI assistant to boost analysis, exploration
Built with capabilities that enable it to reason, act and observe, AIDA improves on text-to-SQL translation tools to provide contextually aware query responses.
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Converged architecture is enterprise AI's missing foundation
Fragmented AI infrastructure fails in production. Converged data architecture with unified governance offers the path to reliable, scalable enterprise AI.
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Data science and analytics Evaluate
How business leaders can make a data-literate culture stick
Data literacy is an ongoing, interactive process. With executive support, a data-literate culture eases backlogs, improves AI outcomes and fosters better decision-making.
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Database Management Evaluate
The database is the new battleground for enterprise AI
Agentic AI puts new pressure on enterprise databases, exposing gaps in the data access, security enforcement and tooling fragmentation that block production deployment.
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Data Warehousing News
Latest AWS data management features target cost control
As the volume and complexity of enterprise data estates increase, and the size of data workloads grows due to AI development, the tech giant aims to help users reduce spending.
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Data Management Strategies Evaluate
Hybrid search demands reshape retrieval frameworks for AI
As AI workloads mature, enterprises face multiple data platform choices to improve search and retrieval capabilities while meeting governance and operational demands.
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Data Integration Manage
6 key components of a successful data strategy
These six elements are essential parts of an enterprise data strategy that will help meet business needs for information when paired with a solid data architecture.
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Data Governance Get Started
Data governance regulations that executives should know
Growing national and international regulatory compliance demands aim to protect consumer data. Organizations must adhere to regulations or face noncompliance risks.
Sponsored Sites
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Generative AI
Enhancing Generative AI for Independent Software Vendors (ISVs)
Transform your software and deliver enhanced experiences to your end users when you integrate with the Amazon Q Business index. Access secure, multi-app data through an API to deliver smarter, context-rich experiences to your end users. Join leading providers in revolutionizing applications with composable AI features.
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AWS & Red Hat
Simplify App Modernization and Innovation with Red Hat OpenShift Service on AWS
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Storage Buying Considerations 2026
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Evaluate
Hybrid search demands reshape retrieval frameworks for AI
As AI workloads mature, enterprises face multiple data platform choices to improve search and retrieval capabilities while meeting governance and operational demands.
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Converged architecture is enterprise AI's missing foundation
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Data governance for AI requires a cross-functional approach
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AI, analytics push data-in-use protection up priority list
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Problem Solve
Data domain ownership, data mesh chart path to AI-ready data
That AI initiative won't get off the ground without timely, reliable data. Bringing technology and team practices into sync reduces delays and boosts data readiness.
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SLM vs. LLM: Rightsize data architecture to optimize AI use
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AI data governance guidance that gets you to the finish line
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How to cut data loss risks when employees leave
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Manage
6 key components of a successful data strategy
These six elements are essential parts of an enterprise data strategy that will help meet business needs for information when paired with a solid data architecture.
-
The database is the new battleground for enterprise AI
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Q&A: The gap between AI ambitions and data readiness
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Data lineage documentation matters for enterprise reliability
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Data Management/Data Warehousing Basics
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Get Started
Data governance regulations that executives should know
Growing national and international regulatory compliance demands aim to protect consumer data. Organizations must adhere to regulations or face noncompliance risks.
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Get Started
How big data collection works: Process, methods, challenges
Before big data can be used in analytics and AI applications, data teams must collect it from various sources. Here's how to create and manage an effective collection process.
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Get Started
Understanding the layers of the AI‑ready modern data stack
Data infrastructure and practices need an upgrade for the AI era. A modern data stack takes a layered approach that aligns teams and delivers governed, trusted data.
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Vendor Resources
- AI Security Starts Here –eBook
- Make the switch from Citrix to Omnissa Horizon –Product Overview
- Managing Endpoint Complexity with Device Lifecycle Strategies –Video
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News
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Data integration
Starburst intros AI assistant to boost analysis, exploration
Built with capabilities that enable it to reason, act and observe, AIDA improves on text-to-SQL translation tools to provide contextually aware query responses.
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Data integration
Snowflake broadens open-source embrace, ups Iceberg support
To make data more interoperable across systems and usable for AI, the vendor is participating in projects that address data quality, integration, governance and discovery.
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Data management strategies
Redpanda launches streaming engine optimized for AI
By integrating workload types, the vendor is simplifying data streams for AI while also differentiating itself from its closest competitors.
Search Data Management Definitions
- What is customer data integration (CDI)?
- What is data analytics (DA)?
- What is an entity relationship diagram (ERD)?
- What is data as a service (DaaS)?
- What is data stewardship?
- What is a pivot table? How to use in Excel and Sheets
- What is Microsoft SSIS (SQL Server Integration Services)?
- What is data quality and why is it important?