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This week’s issue

Wednesday, Nov 11, 2026 · 14 items · new issue every Wednesday, 8am ET
The Feed is our editor’s pick across the whole data-work space this week. For news focused on your tools — including items that don’t make this general Feed — set up Personalize →
Agentic AI, MCP & the context layer 3 items
Vendor newsAgentic AI, MCP & the context layerAmazon Web ServicesDataiku
Amazon Web Services (AWS) · Read source ↗

Amazon Web Services (AWS) has introduced AgentCore on its Bedrock platform, allowing users to manage AI agents with the help of Dataiku's integration. This collaboration aims to streamline the deployment and control of AI agents within enterprise environments.

MyDataWork POV — The promise of controlling AI agents via Amazon Bedrock AgentCore with Dataiku sounds appealing, but let's not gloss over the complexity involved. AWS touts seamless integration, yet the challenge will be how effectively these agents can be customized to specific business needs without becoming unwieldy. The claim of streamlined deployment is alluring, but until we see how these systems handle real-world variability, skepticism remains warranted. The devil may not be in the details, but it's certainly lurking in the execution.
Discussion happens on Reddit — no comments are hosted here.
AnalysisAgentic AI, MCP & the context layer
Forrester Blogs · Read source ↗

The article discusses the potential of AI agents in advertising, emphasizing that they should not just automate existing processes but reimagine them. It suggests a new model where humans define strategy and AI handles execution, leveraging connected data for better decision-making.

MyDataWork POV — The true potential of agentic AI in advertising lies in creating entirely new possibilities. By letting AI handle the execution, we free up human creativity for strategy and innovation. This is a transformation of roles, not merely a tweak. Connected data becomes the backbone, providing the insights both humans and machines need to thrive. It's a shift from patching inefficiencies to constructing a more dynamic, responsive advertising ecosystem.
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Agent Studio offers a structured approach to scoping these agentic use cases. By framing the problem and designing the flow visually, users can ensure that AI is applied where it truly adds value, preventing mere automation of inefficiencies. Explore MyDataWork ↗
Discussion happens on Reddit — no comments are hosted here.
Product launchAgentic AI, MCP & the context layerIBMMicrosoft
Microsoft Source · Read source ↗

IBM and Microsoft have introduced a collaborative AI agent platform aimed at driving industry-specific transformations. The platform is positioned to leverage AI for tailored solutions across different sectors.

MyDataWork POV — IBM and Microsoft's new AI agent platform promises industry-specific transformation, yet the specifics remain murky. It's easy to announce a platform; it's harder to demonstrate its industry-specific efficacy. How exactly will these agents adapt to the nuanced workflows of different sectors? The claim lacks grounding without clear, practical examples of successful deployment. We've heard this tune before: promises of transformation without the sheet music to understand what it actually sounds like when performed in the real world. Until proven, it's just another ambitious claim.
Discussion happens on Reddit — no comments are hosted here.
Data engineering & the warehouse/lakehouse 3 items
NewsData engineering & the warehouse/lakehouseFivetran
Fivetran Blog · Read source ↗

Fivetran's blog discusses the concept of a 'context layer' that transforms raw data into a comprehensive digital brain for organizations, enhancing the intelligence of both people and AI agents.

MyDataWork POV — Fivetran's vision of a unified data framework is a compelling step toward making data truly actionable. By transforming raw data into a digital brain, organizations can enhance both human and AI decision-making. This focuses on building a cohesive ecosystem where data interconnects seamlessly, enabling smarter insights and more informed actions. The concept of a digital brain serves as a practical approach to enhancing the intelligence of enterprise operations.
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MyDataWork's AI-powered features align well with Fivetran's foundational concept. By recommending external datasets and identifying automation candidates, MyDataWork can help users build their own digital brain, enriching their use cases and streamlining business processes. Explore MyDataWork ↗
Discussion happens on Reddit — no comments are hosted here.
Product launchData engineering & the warehouse/lakehouseAWS
AWS Big Data Blog · Read source ↗

Amazon Bedrock AgentCore, combined with AWS Glue, introduces an open-source solution for automating ETL pipeline creation. The system leverages AI agents to handle data ingestion, from file uploads to generating queryable tables, with foundation models suggesting field mappings and testing transforms.

MyDataWork POV — Amazon Bedrock's AgentCore is a refreshing stride towards simplifying ETL processes. By automating the pipeline creation and integrating human approval gates, it bridges the gap between manual oversight and AI efficiency. The use of foundation models to propose field mappings is particularly intriguing, as it promises to reduce the mundane grunt work analysts often face. This focuses on automation while freeing data professionals to concentrate on more strategic tasks, enhancing their value in the process.
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Agent Studio users can leverage this kind of automation to scope out which ETL processes would benefit most from agentic intervention, using its five-step flow to define and refine use cases before implementation. Explore MyDataWork ↗
Discussion happens on Reddit — no comments are hosted here.
Product launchData engineering & the warehouse/lakehouseWorkdayAWS
AWS Big Data Blog · Read source ↗

Workday has announced a new integration of its Data Cloud with AWS, allowing developers to access Workday's governed data without creating data copies or using fragile pipelines. This integration aims to streamline the use of AWS analytics and AI services with Workday's data.

MyDataWork POV — The appeal of bi-directional, zero-copy access to Workday's data via AWS is enticing, yet the practical implementation is still unproven. The claim of eliminating 'brittle pipelines' is bold, yet it glosses over the complexity of maintaining data integrity across platforms. How Workday plans to ensure seamless data governance while integrating with AWS's sprawling ecosystem is still an open question. Until proven, this integration is more of an optimistic blueprint than a guaranteed outcome.
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MyDataWork users could leverage this integration by cataloging the resulting data flows from Workday and AWS into their warehouses, ensuring visibility and continuity as their data stack evolves. Explore MyDataWork ↗
Discussion happens on Reddit — no comments are hosted here.
BI & analytics tools 2 items
Product launchBI & analytics toolsGoogle Cloud
Google Cloud — Data & AI · Read source ↗

BigQuery measures has reached general availability, aiming to unify BI metrics with knowledge graphs to enhance AI accuracy. The goal is to help AI agents answer complex business questions by understanding both the 'what' through governed metrics and the 'why' through relationship networks.

MyDataWork POV — BigQuery's move to unify BI metrics with knowledge graphs is a step forward for those who need precise AI-driven insights. For teams already invested in Google's ecosystem, this integration could streamline complex queries. However, if your current workflow doesn't hinge on Google's stack, this might be more of a curiosity than a necessity. The real value lies in its potential to reduce the friction between metrics and relationships, but only if you're already playing in Google's sandbox.
Discussion happens on Reddit — no comments are hosted here.
Vendor newsBI & analytics toolsTeradata
The Futurum Group · Read source ↗

Teradata's Q3 FY 2026 results highlight the introduction of Sovereign AI, a move that aims to capture new customer markets. This development is part of Teradata's strategy to expand its offerings and address the growing demand for AI-driven solutions.

MyDataWork POV — Teradata's leap into Sovereign AI generates more skepticism than confidence. While opening new markets sounds appealing, the term 'sovereign' suggests a level of autonomy and control that can be a double-edged sword. The risk lies in the potential for regulatory backlash and the complexity of managing AI systems that promise sovereignty. Without clear guidelines and oversight, this could lead to unintended consequences, particularly in regions with stringent data privacy laws.
Discussion happens on Reddit — no comments are hosted here.
AI platforms — data science & ML 3 items
AnalysisAI platforms — data science & MLdbt
dbt Labs Blog · Read source ↗

The article discusses the progression from trusted data to trusted AI and agents, emphasizing the importance of data governance and context to enable controlled AI autonomy. It suggests that achieving AI maturity involves building a reliable data foundation.

MyDataWork POV — The urgency here is unmistakable: organizations need to stop treating governance as a checkbox and start viewing it as the bedrock of AI maturity. The narrative of autonomy is often draped in promise, but it’s the gritty, mundane task of building trusted data systems that determines whether AI agents act with genuine control or spiral into chaos. This week, the spotlight rightly lands on governance as the unsung hero of AI success — not a mere footnote, but the main act.
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Agent Studio's scoping capabilities can guide users in defining AI use cases that align with the trusted data foundation, ensuring that only well-governed, contextually aware scenarios are pursued, thus supporting controlled AI autonomy. Explore MyDataWork ↗
Discussion happens on Reddit — no comments are hosted here.
AnalysisAI platforms — data science & MLSAP
SAP News (Data & Analytics) · Read source ↗

The article highlights that AI in supply chain management should not only provide insights but also enable actionable decisions. It emphasizes that companies with a deep understanding of their operations are better equipped to leverage AI effectively.

MyDataWork POV — The real concern with AI in supply chains is the gap between insight and action. Too often, AI systems flood companies with data but lack the mechanisms to translate this into tangible actions. Without actionable outputs, even the most insightful AI becomes a mere spectator. The risk is that businesses will drown in information, unable to harness it for real-world improvements. The focus must shift from data collection to enabling decisive, informed actions that drive operational success.
Discussion happens on Reddit — no comments are hosted here.
Case studyAI platforms — data science & MLMicrosoft
Microsoft Source · Read source ↗

Microsoft Indonesia is integrating AI into various workflows to enhance productivity and streamline operations. Employees are utilizing AI tools to transform their work processes, demonstrating the potential of AI to drive efficiency across different sectors within the company.

MyDataWork POV — For those in large, resource-rich enterprises like Microsoft, the integration of AI into diverse workflows is a logical step. It allows employees to harness AI's potential to streamline tasks and boost productivity. However, smaller organizations with limited resources might not find this approach as beneficial. The scale and infrastructure required to effectively implement such AI-driven transformations may not be justifiable for everyone. It's a move best suited for companies that can afford to experiment and iterate on a grand scale.
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MyDataWork users can benefit from AI-powered Use Case Recommendations to identify specific improvements in their workflows, similar to how Microsoft employees are leveraging AI to transform their processes. Explore MyDataWork ↗
Discussion happens on Reddit — no comments are hosted here.
Governance, catalog & semantic layer 3 items
ResearchGovernance, catalog & semantic layer
Forrester Blogs · Read source ↗

Forrester's data reveals a gap in AI governance strategy, with only 42% of AI decision-makers incorporating governance into their official AI strategies. While AI governance is gaining attention, its integration into enterprise strategies remains insufficient.

MyDataWork POV — AI governance is the linchpin that enterprises can't afford to overlook. The fact that only 42% of AI decision-makers have woven governance into their official strategies is a glaring oversight. Policies are multiplying, but without embedding them into the strategic fabric, they're just paper tigers. This focuses on ensuring AI initiatives are sustainable and aligned with business goals, not merely compliance. Ignoring this could lead to costly missteps that no amount of retroactive patchwork can fix.
Discussion happens on Reddit — no comments are hosted here.
Product launchGovernance, catalog & semantic layerDatabricksPower BI
Databricks Blog · Read source ↗

Unity Catalog Metric Views have been integrated into Power BI, providing a unified and governed semantic layer for consistent reporting and AI analysis. This aims to offer organizations a single source of truth for business semantics, such as revenue definitions.

MyDataWork POV — The integration of Unity Catalog Metric Views into Power BI sounds like a step toward simplicity, but here's the catch: a 'unified semantic layer' is only as trustworthy as the governance behind it. If this layer fails to account for the messy reality of disparate data sources and inconsistent data definitions within organizations, it risks becoming yet another layer of abstraction that obscures more than it clarifies. Without rigorous oversight, the promised clarity could easily turn into confusion, undermining the very trust it seeks to establish.
Discussion happens on Reddit — no comments are hosted here.
ResearchGovernance, catalog & semantic layer
CDO Magazine · Read source ↗

Gartner reports that the adoption of agentic AI is advancing more quickly than enterprise governance structures can keep up. This rapid pace of implementation raises concerns about the oversight and management of these AI systems within organizations.

MyDataWork POV — Agentic AI is racing ahead, leaving enterprise governance gasping for breath. Gartner's observation highlights a critical oversight: the unchecked enthusiasm for AI without a solid governance framework is a recipe for chaos. Enterprises are diving headfirst into AI adoption, but without the guardrails of governance, they're setting themselves up for potential pitfalls. Deploying AI requires that the process is controlled, ethical, and sustainable. The governance gap is the real issue that demands immediate attention.
Discussion happens on Reddit — no comments are hosted here.
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