Systems | Development | Analytics | API | Testing

Tax is strategy, risk, technology, and real business impact all rolled into one. Our video series brings together practitioners sharing what’s actually working right now. From transfer pricing pressure and global regulatory shifts to AI, ERP realities, and elevating tax’s voice in business decisions, these conversations focus on practical insight you can actually use. Real perspectives. Straightforward conversations. No fluff.

From 51 API Calls to 10 Lines of SQL: Accessing Workday Data With Simba Connect Accessing Workday data through raw APIs means dealing with pagination, rate limits, nested JSON parsing, authentication logic, and dozens of sequential API calls — just to build a simple HR dashboard.

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.

Microsoft retired the Access Salesforce connector in October 2025. Here's how to keep your data flowing with the Simba Salesforce ODBC Driver. If you've been using Microsoft Access to connect to Salesforce, you need a new solution. The good news? Simba has been powering Microsoft's Salesforce connectivity under the hood for years. Same proven driver, same reliable performance. The switch is simple: update one connection string and your queries keep running, your reports stay intact, and your stakeholders don't even need to know anything changed.

How do you connect Snowflake and Apache Iceberg to embedded analytics without adding complexity? In this video, we demonstrate:→ Setting up the Simba Snowflake ODBC driver via system DSN→ Why pushdown queries matter for performance→ Building governed, reusable metrics in Logi Symphony→ Delivering fast, interactive dashboards on live retail data The result: unified sales and inventory analytics without the ETL pipelines, Python scripts, and custom services that create support headaches.

Why do large language models hallucinate? It's not a modeling problem. It's a data and context problem. This video breaks down why AI fails in enterprise environments and what it takes to get reliable, verifiable answers from your AI systems. When AI doesn't have governed access to live data, no understanding of your business rules, and no guardrails to keep it grounded, hallucinations aren't just likely. They're inevitable.