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.