Latest AI and tech news
Ontology vs semantic layer decided with four questions. What a data ontology is, who owns what, and the vendor documentation behind every claim.
Nine Informatica alternatives for data governance, compared on catalog, lineage, quality, monitoring and published pricing, with every vendor claim cited to the documentation it was read from.
Unity Catalog governs everything inside Databricks and stops at its edge. What it covers, the documented limits on lineage and quality, and when it is genuinely enough.
Microsoft Purview vs Collibra for banks, insurers and healthcare: audit log retention, lineage as evidence, access control and cost, cited to vendor documentation and primary regulation.
OvalEdge vs Alation for a mid market data team, compared on licensing, stewardship load, time to first alert and how far each quality engine reaches, with every claim cited to vendor documentation.
When Snowflake Horizon is enough and when it is not: the edition gates, the coverage limits and a decision rule, every claim cited to Snowflake documentation.
Collibra vs Informatica compared on catalog, lineage, quality, observability, deployment and cost, with every claim cited to the vendor documentation it was read from.
Atlan vs Microsoft Purview as a data catalog: source coverage, lineage, classification limits and billing, every claim cited to the vendor documentation.
Alation vs Collibra compared on catalog, lineage, quality, governance and cost, with every claim cited to the vendor documentation it was read from.
Alation vs Atlan compared from both vendors own documentation: column level lineage, native data quality, the anomaly detection each one runs, and the gap they share.
Collibra fits regulated enterprises with a governance team. Atlan fits cloud native teams on Snowflake, Databricks or BigQuery. Here is how to decide, from the documentation.
The 9 capabilities GDPR and HIPAA each require of a data platform, cited to the article and CFR section that sets them, plus what no vendor can do for you.
An AI governance maturity model with five stages, a self assessment checklist and what to fix at each stage to advance enterprise AI governance.
What is an AI governance framework and how do you build one? A practical AI governance policy structure, controls and evidence pack for regulated teams.
Compare 17 AI governance tools and platforms for 2026, with pricing, strengths and trade offs for each. See which AI governance software fits your stack.
What data silos are, the three types and why they form, which ones to break and which to leave, and how to fix them by centralising context rather than moving data.
Compare 18 data lineage tools for 2026, with pricing, column level support and trade offs for each. See which data lineage software fits a regulated data team.
Compare OpenMetadata, DataHub and Amundsen against commercial data catalogs, with the real cost of self hosting and which one fits your team.
What a data retention policy is, the regulations that pull it in both directions, a seven step build process, and a ready to use schedule template.
What data privacy is, how it differs from security and governance, the seven GDPR principles, and six practices that protect personal data at scale.
Data mesh decentralizes ownership; data fabric unifies access. The four mesh principles, fabric architecture, a comparison table, and how to choose.
What data classification is, the four sensitivity levels, classification types by regulation, and how to automate labeling across your data stack.
Decube, Collibra, Atlan, Alation, OvalEdge and Informatica compared for banks on lineage, security review, access evidence and regulator timelines.
What Collibra costs in practice, when it is worth it, and how Decube, Atlan, Alation, OvalEdge and open source compare as Collibra alternatives.
Why data teams outgrow Monte Carlo and how Decube, Anomalo, Metaplane, Bigeye, Acceldata and open source tools compare on alerts, context and cost.
Why data teams look beyond Alation, the five criteria that matter, and how Decube, Atlan, Collibra, OvalEdge and open source catalogs compare.
Agent lineage traces an AI agent's decision back through tools, model, prompts and data. What it is, why data lineage stops short, how to start.
Decube, Monte Carlo, Bigeye, Metaplane, Anomalo, Acceldata and open source options compared honestly on detection, noise, lineage, coverage and price.
What an agent registry is, what it records, and how it stops shadow AI. Corrected EU AI Act dates and practical steps to build your agent inventory.
Decube, BigID, Purview, Securiti, OneTrust, Varonis, Collibra and OvalEdge compared on discovery coverage, classification accuracy, architecture and cost model.
Why OJK compliance has become a weeks-long data governance exercise—and how Decube makes traceability easier.
Learn what AI governance is, why it matters, how it connects to data governance, and how enterprise leaders can build a practical AI governance framework.
A native connector that extracts SAP HANA metadata on a regular schedule, so governance and catalog coverage don't stop at the edge of your ERP.
Master end-to-end data lineage with best practices for effective tracking and governance.
Give Cursor, Copilot, and Claude governed context on lineage, ownership, PII, and compliance. Decube's MCP Server connects AI to trusted data facts.
Data Products is one trusted data foundation for governance and AI — a governed, self-serve marketplace built on the catalog you already trust, so producers publish once and consumers stop guessing.
Discover best practices for designing and managing data marts to enhance decision-making in data engineering.
Discover best practices for optimizing SLAs software in data engineering for enhanced success.
Discover best practices for effective cloud data discovery to enhance visibility, compliance, and governance.
Master anomaly detection in time series data for improved insights and operational efficiency.
Discover best practices for implementing data validation software to enhance data integrity and efficiency.
Enhance cloud data quality with best practices for accuracy, integrity, and reliability.