How Yellowfin AI Analytics Helps Teams Turn Live Data Into Faster, Better Business Decisions

Slow data creates slow action. That is the real problem. A report delivered on a weekly cadence can miss a sales dip, a churn spike, or a supply issue that started yesterday. By the time the team sees it, the cost is already there. Corporate leadership and “The C-Suite” cares about revenue protection, customer experience, efficiency, and speed to decision. Those goals depend on live data, not stale snapshots.

New: Close The Gaps In Your Reporting Stack With Custom Integrations

Most teams work across dozens of tools, and not all of them connect to their reporting workflows out of the box. There are always sources that fall outside the native integrations list: an internal tool your team built, a platform specific to your industry, or a piece of software that a vendor hasn’t prioritized supporting yet. When that data isn’t directly available, teams get it in however they can.

Ep 72 | The Data Governance Coach: From Data Error to Insight

In the world of enterprise AI, the pressure on data has changed. What used to be “good enough” now gets amplified by faster decisions, and therefore, faster mistakes. Governance is fundamental in ensuring data trust and integrity. In this episode of The AI Forecast, Paul Muller sits down with The Data Governance Coach, Nicola Askham, to share her pragmatic perspective and assert that governance only delivers value when it’s simple enough for people to use and embedded into day-to-day work.

Scaling AI with Trust: Real-Time Access to Governed Data

Most AI strategies aren't failing because of models—they’re failing because data is fragmented, siloed, and hard to access. In fact, nearly 8 and 10 organizations say incomplete data access is holding them back. Moving the data drives up cost, introduces latency, and increases compliancy and security risks. Cloudera has introduced the Workflow Data Fabric Zero Copy Connector for ServiceNow to solve this. It allows you to securely leverage nearly 30 exabytes of data under management to power agented workflows without moving the data from wherever it lives.

Why every data role needs Open Data Infrastructure

Analysts, data engineers, ML engineers, and data scientists don’t work the same way; they shouldn’t have to. Today’s data ecosystem includes more roles, more tools, and more specialized workflows than ever before. The days of limiting access to a single warehouse or lake — controlled by a small group of data engineers or analysts — are over.

Integrate.io Delivers Cloud-Native SharePoint Integration: Write Directly to SharePoint from Your Data Pipeline | March 2026

We're excited to announce a new cloud-native capability that deepens Integrate.io's integration with the Microsoft 365 ecosystem. SharePoint is now available as a write destination in the package designer. This release enables data teams to push transformed, pipeline-processed data directly into SharePoint Online libraries and lists through a fully cloud-native, API-first connection.

Integrate.io Launches Native Reverse ETL Capabilities: Configurable Request Throttling for the REST API Destination | April 2026

We're excited to share our latest feature enhancement that improves reliability and control for outbound data delivery across the platform. This release introduces configurable request throttling on the REST API destination, giving data teams a native way to respect target API rate limits directly within their pipeline configuration.

Data Products for Qlik Analytics - Data Quality -Semantic Types - Part 5

In this video, we dive into how Click Data Products combine Data Quality, Semantic Types, and business context to create trusted, scalable, and reusable data assets. We explore how semantic types in Qlik help classify and validate data using meaningful business definitions — improving consistency, discoverability, and confidence in analytics and AI-driven insights.

Resource Governance and GPU Quota Enforcement Across AI Teams

Resource governance is primarily an operational discipline, but it has direct security implications that are usually overlooked. This post covers what those implications are, what Kubernetes provides natively, where it falls short for AI workloads, and how ClearML addresses both dimensions. This is the third post in our four-part series on Kubernetes Security for Enterprise AI Environments.