Systems | Development | Analytics | API | Testing

The Five Pillars of AI Compliance Excellence

95% of AI pilots are failing. Here's why the other 5% are winning. While most organizations scramble to retrofit compliance into their AI implementations, leading finance teams are building it in from the start—and gaining a major competitive edge. Three insights that caught my attention: → Vendor solutions succeed at 2x the rate of internal builds (67% vs 33%)—your team's expertise matters more than you think.

Data Products for Qlik Analytics - SaaS in 60

Qlik Data Products for Analytics is how you turn raw data into something people can actually trust and reuse. It’s built right into Qlik Cloud Analytics and is designed for analytics teams, data producers, and even AI initiatives. Instead of everyone rebuilding datasets over and over, teams can publish curated, governed, analytics-ready data products that include business context, quality checks, and our patented Qlik Trust Score. People discover them in a marketplace, plug them straight into dashboards, apps, or AI workflows, and move fast with confidence. The big value? Less duplication, lower cost, faster app development, and insights you can actually trust.

The Data Hiring Dilemma: Scaling Analytics Without Expanding Headcount

The volume of data businesses process is surging exponentially, while budgets for human capital remain constrained. For many CTOs and Data Leaders, a default response to escalating data demands can be an accelerated hiring cycle; get more people. Yet, relying on recruitment to solve challenges around scaling analytics is no longer easily feasible; it can be a significant bottleneck.

Beyond RAID and Mirroring: A Next-Generation Approach to Data Resilience

Imagine being forced to buy twice the storage you'll ever use, or watch your AI workloads grind to a halt when petabyte-scale data growth from training models exhausts capacity mid-project? Many teams remember when a few well-tuned arrays and RAID groups felt like more than enough, long before AI pipelines and container sprawl started eating capacity for breakfast. And then there’s reliability.

Embedded Analytics as a Revenue Generator: Turning BI Into Product Revenue

BI is Not a Cost Center The Hidden Barriers Between Embedded Analytics and Revenue Turning Embedded Analytics Into a Scalable Revenue Stream Why YellowfinBI Maps Well to Revenue-Grade Embedded Analytics Proving ROI: Revenue Stories That Survive Finance Review Conclusion: Packaging Embedded Analytics as Revenue FAQ.

Cortex Code CLI expands to support any data, anywhere

Cortex Code CLI is expanding capabilities to accelerate your enterprise data lifecycle inside Snowflake! Introducing dbt and Apache Airflow support, expanded model choice across Claude Opus 4.6, Sonnet 4.6, and GBT 5.2. New enterprise-grade governance controls, and a self-serve subscription option. See how Cortex Code CLI helps you ship workflows faster, integrate data systems, and build with confidence using natural language.

Retrieving Metadata from a ThoughtSpot Cluster Using CS Tools

Retaining total clarity over your metadata doesn't have to be a manual burden. While traditional BI tools often demand more time than they save, this technical walkthrough explores how to leverage CSTools—a Python-based utility—to bypass the UI and fetch metadata directly via REST APIs. Automating your cluster oversight is essential for reliable, code-first tracking of users and groups at scale. By using CSTools, you can eliminate the manual "busy work" and offload the repetitive tasks that weigh down your data team.