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Reconsider the AI readiness gap in data and analytics
The AI readiness gap is widely framed as temporary, but examining it across three distinct enterprise layers suggests it might be more permanent than the market suggests.
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How to develop an enterprise data strategy: 12 key steps
Here are 12 to-do items for data leaders developing a data strategy to help their organization use data more effectively for analytics and business decision-making.
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Data quality, fast failures and quick wins key to AI success
Despite many organizations struggling to realize value from their development initiatives, best practices can help, according to industry experts at Domo's annual user conference.
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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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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 News
Redis unveils Feature Form to improve AI, ML workloads
Unified batch and streaming pipelines and multi-tenant capabilities that allow teams to isolate work in shared database instances highlight new ML management environment.
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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
Reconsider the AI readiness gap in data and analytics
The AI readiness gap is widely framed as temporary, but examining it across three distinct enterprise layers suggests it might be more permanent than the market suggests.
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Data Integration News
Data quality, fast failures and quick wins key to AI success
Despite many organizations struggling to realize value from their development initiatives, best practices can help, according to industry experts at Domo's annual user conference.
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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.
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Collaboration
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Evaluate
Reconsider the AI readiness gap in data and analytics
The AI readiness gap is widely framed as temporary, but examining it across three distinct enterprise layers suggests it might be more permanent than the market suggests.
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Hybrid search demands reshape retrieval frameworks for AI
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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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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.
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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
How to develop an enterprise data strategy: 12 key steps
Here are 12 to-do items for data leaders developing a data strategy to help their organization use data more effectively for analytics and business decision-making.
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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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Database management
Redis unveils Feature Form to improve AI, ML workloads
Unified batch and streaming pipelines and multi-tenant capabilities that allow teams to isolate work in shared database instances highlight new ML management environment.
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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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