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4 trends that will shape data management and AI in 2026
The tendencies that influence the next 12 months build on what began last year, including rising mergers and acquisitions and the need to simplify developing and deploying agents.
Evaluate
Data architecture vs. information architecture: How they differ
Data architecture and information architecture are distinct but related disciplines that work in tandem to support an enterprise's data and business strategies.
Manage
Data governance roles span the enterprise
Data governance is a team effort, though recent AI developments have caused data and AI teams to converge. As a result, organizations have created new roles in these teams.
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4 trends that shaped data management, analytics in 2025
With each relating to agentic AI development, tendencies included nearly universal support for MCP and rising emphasis on semantic modeling, among others.
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Data science and analytics News
Ng: Biggest benefit of AI may be unlocking unstructured data
Tech entrepreneur Andrew Ng says that in addition to autonomous action, one of the most beneficial applications of AI is enabling easy access to valuable unstructured data.
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Database Management Evaluate
Different types of database management systems explained
The various types of database software come with advantages, limitations and optimal uses that prospective buyers should be aware of before choosing a DBMS.
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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 News
4 trends that will shape data management and AI in 2026
The tendencies that influence the next 12 months build on what began last year, including rising mergers and acquisitions and the need to simplify developing and deploying agents.
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Data Integration News
4 trends that shaped data management, analytics in 2025
With each relating to agentic AI development, tendencies included nearly universal support for MCP and rising emphasis on semantic modeling, among others.
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Data Governance Evaluate
2026 will be the year data becomes truly intelligent
As AI moves into production, enterprises are redefining data management around shared meaning, operational trust and system coherence rather than standalone capabilities.
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Artificial Intelligence
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Evaluate
2026 will be the year data becomes truly intelligent
As AI moves into production, enterprises are redefining data management around shared meaning, operational trust and system coherence rather than standalone capabilities.
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Data architecture vs. information architecture: How they differ
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Different types of database management systems explained
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Why data semantics matters for context-aware systems
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Problem Solve
10 data governance challenges that can sink data operations
No organization can successfully use data without data governance. Address 10 data governance challenges to avoid financial loss, derailed operations and reputation damage.
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7 data cleansing best practices
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6 dimensions of data quality boost data performance
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Data lake governance: Benefits, challenges and getting started
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Data governance roles span the enterprise
Data governance is a team effort, though recent AI developments have caused data and AI teams to converge. As a result, organizations have created new roles in these teams.
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How to build a data catalog: 10 key steps
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How AI governance manages risk at scale for enterprises
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Data contracts help build trustworthy data products for AI
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Data Management/Data Warehousing Basics
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Get Started
10 essential roles for a data management team in 2026
These 10 essential data management roles help leaders design teams that support governance, scale and trusted analytics across modern enterprise data environments.
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Get Started
How to build a data catalog: 10 key steps
A data catalog helps business and analytics users explore data assets, find relevant data and understand what it means. Here are 10 important steps for building one.
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Get Started
What is customer data integration (CDI)?
Customer data integration (CDI) is the process of defining, consolidating and managing customer information across an organization's business units and systems to achieve a "single version of the truth" for customer data.
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Vendor Resources
- Dell AI Data Platform: Unlocking AI’s Potential with Your Enterprise Data –White Paper
- Get the latest research: State of Observability 2025 –Research Content
- 2025 CLOUD SECURITY REPORT –Research Content
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Data management strategies
Databricks adds $4B funding round; IPO could be next
With a valuation well over $100 billion and strong revenue growth from both data management and AI, the vendor could be preparing to go public.
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Data management strategies
EnterpriseDB updates WarehousePG with features that fuel AI
The PostgreSQL database specialist's data warehouse update targets AI development by adding predictable pricing, real-time data processing and improved governance capabilities.
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Data management strategies
Couchbase adds agentic AI development suite to Capella DBaaS
AI Services, which includes an integration with Nvidia and governance capabilities -- among other features -- represents the vendor's growth beyond its database roots.
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?