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We're writing down everything we learn. 980+ articles, how-to guides, and resources on data governance, context engineering, enterprise AI and more
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Learn the most common context failures that break AI agents in enterprise data teams—metadata gaps, permission and safety issues, stale context, weak observability, and missing governance links—plus how to fix them.
February 9, 2026Learn how to use data lineage to triage data quality incidents faster: start from the symptom, trace upstream and downstream, pinpoint the likely break point, run RCA and impact analysis in parallel, and connect lineage with metadata and observability.
February 9, 2026Understand llm context window limitations, why they exist, and how they impact quality, cost, and latency, with tactics to design efficient LLM workflows.
February 9, 2026Ontology-first AI architecture starts by modeling business concepts and relationships as the backbone for AI systems. Learn why it's emerging, core components, a customer health example, comparisons, a 30-day checklist, pitfalls, and FAQs.
February 9, 2026
Dashboards vs. AI analysts: What 5 analytics leaders learned about accountability, context layers, and the foundations that make AI actually work.
February 6, 2026
Intelligence is now a commodity, and context is the real competitive moat. Leaders from Snowflake, Glean, Foundation Capital, Microsoft, and Atlan debate its future.
February 6, 2026Ensure accurate, consistent data across domains with multi-domain data quality. Explore how to eliminate silos, improve trust, and power analytics and AI at scale.
February 4, 2026
Discover why context is the missing layer between data and trustworthy AI. This primer breaks down how the context layer grounds AI in business meaning, governance, and trust.
February 3, 2026
Data lineage shows exactly where your data comes from, how it transforms, and which reports break when sources change. See why teams save 50% on debugging time.
February 2, 2026BigQuery's Dataplex lineage provides table‑and-column‑level visibility inside BigQuery but operates with multi‑hour delays and limited cross‑tool coverage. Learn when native lineage is enough, and when you need a control plane like Atlan for end‑to‑end, cross‑platform lineage.
February 1, 2026
Data catalog migrations fail 60-70% without planning. Learn metadata-first approaches, phased execution, and how catalogs reduce migration risk.
February 1, 2026Unity Catalog automatically captures job lineage for tables. Configure lineage tracking in 5 steps: enable Unity Catalog, register tables, verify permissions, view in Catalog Explorer, and query system tables. Track which jobs consume your data in 15-30 minutes.
February 1, 2026
Implement Snowflake data governance through RBAC, automated tagging, dynamic masking, lineage tracking, and quality monitoring with DMFs. Learn proven practices.
February 1, 2026Complete this 7-step data governance checklist before Snowflake migration to reduce failure rates from 83% to under 30%. Covers RBAC, policies, lineage, and compliance across 6-12 months.
February 1, 2026Learn about data quality dimensions, challenges, essential quality indicators and best practices for metadata-driven automation across your organization.
January 30, 2026OpenAI's data agent works because of six context layers. Here's what that reveals about why enterprise AI agents fail—and the three types of context you need.
January 30, 2026
Use Atlan's supply–demand use case framework to build a unified data & AI context layer that drives trusted decisions, efficient AI, and compliance.
January 30, 2026Discover who should own the context layer in enterprise AI. Learn federated ownership models, CDO leadership roles, and governance frameworks for AI success.
January 30, 2026Understand the difference between context layers and semantic layers, how they work together, and which of these do your AI initiatives need.
January 30, 2026Discover why context preparation is replacing traditional data prep as the foundation for AI success. Learn core principles, RAG implementation strategies, and how context layers help.
January 30, 2026Learn what conversational analytics is for business intelligence, how "talk to data" translates questions to SQL on live warehouses, and why context drives 3x higher accuracy.
January 30, 2026Discover the best data quality tools in 2026, including Atlan, Anomalo, and Monte Carlo. Explore benefits, essential features, and use cases.
January 30, 2026Understand the difference between ontologies and semantic layers for data management. Compare features, use cases, and integration approaches for AI-ready enterprises.
January 30, 2026
AI systems rarely fail at once – semantic drift and context rot are often imperceptible. Atlan & Artie keep context fresh for reliable data and AI governance.
January 29, 2026
