No single platform does everything well. The organizations extracting the most value from their data investments in 2026 are those that have stopped looking for a single answer and started building a coherent stack. Azure and Fabric for integration and BI, Databricks for data engineering and AI, and Power BI as the governed consumption layer that connects it all to the business.
Financial services firms invest heavily in dashboards, yet senior leaders routinely leave those tools behind when it matters most. Examine our perspective on the four structural reasons financial dashboards fail at the executive level, and what it takes to build reporting that actually drives decisions.
Building a proof of concept is the easy part. However, getting AI into production reliably, repeatably, and with the governance to sustain it is where most programs stall. Here is our perspective on the structural reasons AI POCs fail to scale and a practical operationalization framework for CDOs and CDAOs navigating that transition.
Most automation programs stall not because the technology fails, but because organizations automate the wrong things first. Below, we share our perspective by providing a practical sequential framework to help enterprise leaders identify where automation can create the fastest, most durable return.
The reason two Power BI reports show different revenue numbers is rarely a technology problem but a design one that yields financial consequences. The following shares our perspective on what a data model is, why it matters to the CFO, and the right questions to ask before approving the next analytics investment.