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Solutions Review Executive Editor Tim King curated this list of notable data management news for the week of February 27, 2026.<\/strong><\/em><\/p>\n Keeping tabs on all the most relevant big data and data management news can be a time-consuming task. As a result, our editorial team aims to provide a summary of the top headlines from the last week in this space. Solutions Review editors will curate vendor product news, mergers and acquisitions, venture capital funding, talent acquisition, and other noteworthy big data and data management news items.<\/p>\n For early access to all the expert insights published on Solutions Review<\/em>, join Insight Jam<\/em><\/a><\/strong><\/span>, a community dedicated to enabling the human conversation on AI.<\/p>\n Actian Adds Data Observability Agents To Keep AI From Acting On Bad Data<\/strong><\/p>\n Actian has introduced Data Observability Agents and an MCP server that continuously validate data as it lands in lakehouses like Apache Iceberg, Delta Lake, and Hudi, flagging anomalies and explaining root causes in plain language. A suite of agents\u2014including Validation, Incident Diagnosis, Lineage, Data Insight, Orchestration, Routing, and Help\u2014work together to move from detection to remediation, with write capabilities that let AI agents trigger fixes instead of just raising alerts. The goal is to give agentic AI workflows real-time quality signals so autonomous systems can check whether data is trustworthy before they act.<\/p>\n Read on for more\u2192\u00a0https:\/\/www.actian.com\/company\/press-releases\/actian-introduces-data-observability-agents-for-the-agentic-ai-era\/<\/span><\/a><\/p>\n Ataccama Adds Agentic Data Observability To Its Data Trust Platform<\/strong><\/p>\n Ataccama has introduced Agentic Data Observability in Ataccama ONE, extending its data quality and governance stack into pipeline monitoring and incident workflows for regulated enterprises. The new capabilities unify observability with automated data quality rules, lineage, catalog, and ownership, so teams can move straight from anomaly detection to triage and remediation in a single workflow. Powered by the ONE AI Agent acting as a digital data steward, the platform surfaces issues with business context and suggests where to apply fixes, aiming to protect AI models and critical reporting from bad data before it spreads.<\/p>\n Read on for more\u2192\u00a0https:\/\/www.ataccama.com\/news\/ataccama-launches-agentic-data-observability-to-extend-market-leading-data-trust-platform<\/span><\/a><\/span><\/p>\n Collate\u2019s Semantic Intelligence Graph Grounds AI Agents In What Enterprise Data Actually Means<\/strong><\/p>\n Collate has introduced its Semantic Intelligence Graph, which turns scattered enterprise metadata into a standards-based semantic layer that AI agents and models can understand and traverse. By connecting business concepts to specific tables, schemas, and fields using OpenMetadata extensions, RDF graphs, and shared ontologies, the platform gives LLMs a consistent \u201cmap\u201d of data assets, relationships, and context. New AI Studio and SDK capabilities let teams build agents for documentation, governance, data quality, and SQL generation that are grounded in this shared meaning, aiming to move AI from brittle pilots to trusted, explainable production systems.<\/p>\n Read on for more\u2192\u00a0https:\/\/www.globenewswire.com\/news-release\/2026\/02\/24\/3243453\/0\/en\/Collate-Launches-Semantic-Intelligence-Graph-to-Help-AI-Understand-Enterprise-Data.html<\/span><\/a><\/p>\n Confluent Intelligence Bridges Streaming Data And Enterprise AI Agents<\/strong><\/p>\n Confluent has expanded Confluent Intelligence with new Streaming Agents that use the Agent2Agent (A2A) protocol to trigger and coordinate external AI agents directly from real-time Kafka streams. By connecting Anthropic\u2019s Model Context Protocol (MCP), agent frameworks like LangChain, lakehouses such as Databricks and Snowflake, and SaaS platforms including Salesforce and ServiceNow, the system aims to turn streaming analytics into automated actions. The release also adds Multivariate Anomaly Detection to spot correlated issues across multiple metrics, so AI agents can autonomously prevent outages or escalate problems to humans before they impact customers.<\/p>\n Read on for more\u2192\u00a0https:\/\/www.businesswire.com\/news\/home\/20260226559724\/en\/Confluent-Intelligence-Expands-Real-Time-Business-Data-to-Enterprise-AI<\/span><\/a><\/span><\/p>\n Precisely Adds AI Agents To Turn Data Integrity Workflows Into A Conversational Experience<\/strong><\/p>\n Precisely has expanded its Data Integrity Suite with new AI agents for data quality, data enrichment, and location intelligence that work alongside its Gio AI Assistant. The agents can recommend and generate data quality rules, normalize and standardize messy records across sources, verify and geocode addresses, and enrich datasets with additional attributes\u2014while keeping humans in the loop for approvals. Precisely frames this as delivering \u201cAgentic\u2011Ready Data\u201d: trusted, context\u2011rich data foundations that can reliably power downstream AI, analytics, and automation initiatives at enterprise scale.<\/p>\n Read on for more\u2192\u00a0https:\/\/www.precisely.com\/press-release\/precisely-expands-data-integrity-suite-with-new-ai-agents-for-enhanced-data-quality-data-enrichment-and-location-intelligence\/<\/span><\/a><\/span><\/p>\n Snowflake Extends Cortex Code CLI Beyond Snowflake To Support dbt, Airflow, And \u201cAny Data Anywhere\u201d<\/strong><\/p>\n Snowflake is expanding Cortex Code CLI\u2014its AI coding agent for local development\u2014so it can now assist with dbt and Apache Airflow workflows, not just Snowflake-native pipelines. Developers get secure, context-aware AI help for model development, debugging, and optimization directly inside their preferred tools while Snowflake keeps governance and access controls consistent across environments. The company frames the move as a step toward standardizing AI-assisted development across the entire data lifecycle, enabling teams to build and maintain production-grade pipelines wherever their data lives.<\/p>\n Read on for more\u2192\u00a0https:\/\/www.snowflake.com\/en\/news\/press-releases\/snowflake-cortex-code-expands-towards-supporting-any-data-anywhere\/<\/span><\/a><\/span><\/p>\n Tray Launches Data Engineering To Unblock The \u201cAI Supply Chain\u201d<\/strong><\/p>\n Tray.ai has released Tray Data Engineering, a new capability that combines in\u2011flight SQL-based data transformation with integration and AI agent development on a single orchestration platform. A built\u2011in SQL Transformer lets teams ingest millions of records from formats like Parquet and JSON, then reshape and join them using ANSI SQL inside Tray workflows, eliminating separate ETL scripts and databases. Those engineered data streams can power Tray Agents and MCP tools or be pushed to platforms like Snowflake, Databricks, BigQuery, and Redshift, with one-step, serverless processing to keep performance high and costs predictable.<\/p>\n Read on for more\u2192\u00a0https:\/\/tray.ai\/company\/news\/data-engineering-ai-orchestration<\/span><\/a><\/span><\/p>\n Treasure Code Turns The Treasure Data CDP Into An Agentic, AI-Native CLI<\/strong><\/p>\n Treasure Data has launched Treasure Code, an AI-native command-line interface that lets technical teams and AI agents operate the entire Treasure Data CDP \u201cas code.\u201d The tool uses Claude-powered assistance to help users describe desired outcomes in natural language and generate production-ready SQL, workflows, segments, and configurations that can be reviewed and deployed with DevOps-style controls. With roughly a quarter of customers already using Treasure Code, the company positions it as a way to offload repetitive CDP operations to agents while keeping humans in the loop for verification and governance.<\/p>\n Read on for more\u2192\u00a0https:\/\/www.businesswire.com\/news\/home\/20260223231487\/en\/Treasure-Data-Unveils-Treasure-Code-Bringing-Agentic-AI-to-Customer-Data-Operations<\/span><\/a><\/span><\/p>\n VAST Polaris Aims To Be The Global Control Plane For AI Data Infrastructure<\/strong><\/p>\n VAST Data has introduced Polaris, a global control plane that provisions, operates, and orchestrates distributed VAST AI OS deployments across public clouds, neoclouds, and on\u2011prem data centers. Polaris abstracts infrastructure location while VAST\u2019s DataSpace abstracts data location, giving enterprises a single logical environment for AI pipelines even as training, inference, and data collection run in different regions under varying compliance and cost constraints. Built as a secure, multi-tenant Kubernetes-based service with RBAC and audit logging, Polaris provides intent-driven lifecycle management so teams can deploy and upgrade multi-cluster AI infrastructure via one API and console.<\/p>\n Read on for more\u2192\u00a0https:\/\/www.vastdata.com\/press-releases\/vast-data-introduces-polaris-to-orchestrate-globally-distributed-ai-data<\/span><\/a><\/p>\n Thought Leaders:\u00a0<\/em>The Hidden Crisis Killing Your Data & AI Strategy by Samir Sharma<\/strong><\/p>\n I spoke with a C-suite leader this week who captured something I\u2019ve been seeing everywhere: \u201cI feel like I\u2019m shouting about transformation from the stage while my team sits in the audience wondering what I\u2019m talking about.\u201d He\u2019s not alone. What I\u2019m calling \u201cvelocity whiplash\u201d is the most under reported, over experienced pain point in enterprise AI today. Leaders are racing ahead while their organisations stand still. The gap between them is growing into a credibility crisis that\u2019s quietly destroying AI transformations. You might think that\u2019s too strong, but, it is something I\u2019m continuing to hear.<\/p>\n Read on Solutions Review<\/em><\/a><\/p>\n Insight Jam<\/em> Launches Mesh Lab Episode 2 to Build a Framework for Work & Learning in the Age of AI: The Human Advantage<\/strong><\/p>\n Insight Jam<\/em> Mesh Lab is a year-long, monthly series of expert-led discussions<\/a><\/strong><\/span> dedicated to exploring and shaping the next generation of frameworks for work and learning in the age of AI. Rather than reacting to tools or trends, Mesh Lab is designed to initiate a sustained, cross-sector conversation about how human capability itself must be re-architected as AI becomes embedded across education, enterprise, and leadership.<\/p>\n Watch on Insight Jam<\/em><\/a><\/p>\n NEW Series Live: The Human Conversation with Doug Atkinson Featuring David Linthicum<\/b><\/p>\n David Linthicum, former Deloitte Cloud Chief Strategy Officer and 35-year AI veteran, joins Doug to discuss why 90% of AI projects fail, which industries are most vulnerable to disruption, and what happens when the corporate ladder loses its bottom rungs.<\/p>\n Watch on YouTube<\/a><\/p>\n Thought Leaders:\u00a0<\/em>Why Value is Misunderstood Across Organizations by Samir Sharma<\/strong><\/p>\n For the CEO, value is action. It\u2019s about executing strategy with confidence, allocating resources where they create the most leverage, improving revenue, reducing unnecessary spend, and anticipating risks before they materialize. If the board\u2019s expectations of value aren\u2019t clear, the CEO is forced to chase outputs instead of outcomes.<\/p>\n Read on Solutions Review<\/em><\/a><\/p>\n Solutions Review <\/i>Surpasses 250K on YouTube to Power Unmatched Media Momentum in Enterprise Tech & the Human Impact of AI<\/b><\/p>\n Solutions Review<\/em>\u00a0has officially surpassed\u00a0250,000 subscribers on YouTube<\/a><\/strong>, a milestone that\u2019s about more than just the number. This moment marks the payoff of a deliberate, two-year strategy\u00a0centered on authoritative video<\/a><\/strong>,\u00a0human-led insight<\/a><\/strong>, and an early recognition of how AI search engines ingest information, assign trust, and surface sources at scale.<\/p>\n Read on Solutions Review<\/em><\/a><\/p>\n Solutions Review<\/em> Editors Launch New ‘Human-AI Ledger’ Newsletter to Chronicle the Human Impact of AI<\/strong><\/p>\n Artificial intelligence is everywhere\u2014but understanding its human impact has never been more urgent. That’s where Solutions Review<\/em>‘s latest newsletter, The Human-AI Ledger<\/i><\/a><\/strong><\/span>, comes in. Designed to chronicle how AI is reshaping the way we learn, work, lead, and live, this newsletter explores the soft side of AI\u2014the non-technical, deeply human dimensions that determine whether this technological revolution helps us thrive or leaves us behind.<\/span><\/p>\nTop Data Management News for the Week Ending February 27,<\/strong> 2026<\/strong><\/h2>\n
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<\/a>Watch this space each week as our editors will share upcoming events, new thought leadership, and the best resources from Insight Jam<\/em>, Solutions Review’s enterprise tech community<\/a><\/strong><\/span> where the human conversation around AI is happening. The goal? To help you gain a forward-thinking analysis and remain on-trend through expert advice, best practices, predictions, and vendor-neutral software evaluation tools.<\/p>\n