Most organizations know where their data lives. Far fewer can prove how it got there, what happened to it along the way, or whether the number in the executive report reflects what the source system recorded. Below is our perspective explaining what data lineage is, why it sits at the intersection of governance, AI readiness, and regulatory compliance, and what happens when organizations treat it as optional.
Here is our perspective on why financial institutions struggle to trust their own data: the problem is not a single flaw but the compounding effect of poor data quality, absent governance, and architectures that were never designed to produce consistent answers. Until all three are addressed together, data distrust will persist regardless of how much is invested in analytics and AI.
Moving an on-premises data warehouse to Microsoft Fabric is not a lift-and-shift exercise, but an architectural decision with long-term consequences for cost, governance, and analytics capability. Read more as we cover the business case, the migration approaches, the phase sequencing, and the risks that derail most programs before they reach production.
Here is our perspective on automating manual reporting: most organizations are not struggling because they lack ambition, but because they started with the wrong reports, skipped the data foundation step, or automated the format without fixing the process underneath. The roadmap that works is simpler than most programs suggest, and it starts well before any tool is selected.
Most organizations have more data than they can use and less insight than they need. Our perspective below explains what decision intelligence is, why siloed data is the primary obstacle to it, and how CEOs and COOs can close the gap between data investment and decision quality.
Financial services firms invest heavily in data modernization, yet consistently underestimate the difficulty of execution. Our perspective examines the most common failure patterns across banking, insurance, and wealth management and provides a practical framework for CIOs and CTOs to avoid them.
Most organizations deploy RPA when they need AI or reach for AI when a simple bot would do. Here is our perspective as we provide a practical, three-question framework for choosing between RPA, AI, or a hybrid approach, and a 30/60/90-day pilot structure to get each option right from the start.
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.