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Most guidance on database replication focuses on what happens inside the pipeline: which replication strategy to use, how to handle consistency, how to monitor lag, and how to manage failover. That’s all worth understanding. But this isn’t where many replication problems start. They begin when a team realizes that one of their most important data sources can’t participate in the replication pipeline at all.

In 2026, insightsoftware was recognized in Gartner’s Magic Quadrant for Financial Close and Consolidation Solutions. The recognition focuses on insightsoftware’s JustPerform product, which offers a unified, process-first architecture integrating consolidation, reconciliation, and reporting with self-service dashboards and analytics.

When it comes to database performance, most focus on writing better SQL or tuning database parameters. Both matter. But there’s a third layer that’s crucial to consider: the driver sitting between your application and your data source. Drivers decide where query operations actually execute. Some operations get pushed down to the data source, a fast process. Others get processed in the driver layer itself, which takes more time.

AI workflows for manufacturing need to be deployed quickly. Quality control systems, predictive maintenance tools, and supply chain optimization algorithms may be going live, yet compliance infrastructure is lagging behind. The result is a familiar pattern: pilots that prove out technically but stall before production because they can’t clear audit, safety, or regulatory review.

Every year, Dresner Advisory Services publishes some of the most closely watched research in the enterprise performance management (EPM) space. Unlike analyst firms that rely heavily on vendor briefings, Dresner’s Wisdom of Crowds methodology gathers data directly from end users — the finance leaders, FP&A professionals, and CFOs who live inside these platforms every day.

For most finance teams, the journey from annual planning through to financial close is anything but smooth. Spreadsheets pile up, emails get lost in inboxes, and critical financial data lives in disconnected systems across business units. The result? Delayed decisions, eroded profitability, and a planning process that exhausts the very people responsible for driving the organization forward.

If your organization is still running SAP ECC, the clock is ticking. SAP has set 2027 as the end of mainstream maintenance for SAP ECC 6.0. This announcement means no more standard support, security patches, or bug fixes after that date. For large enterprises in manufacturing, food and beverage, pharma, chemicals, and logistics, the pressure to complete an S/4HANA migration before that deadline is becoming impossible to ignore. The risk isn’t just technical.

Product teams tasked with providing an AI analytics and BI platform to finance organizations see a unique set of challenges. Finance organizations are subject to SOX, GDPR, EU AI Act compliance on top of accurately closing the books and preparing for the potential of an audit. In a highly regulated industry like finance, product leaders building solutions for finance leaders need accurate insights they can trust that hold up to audits and regulatory scrutiny.

Most AI pilots fail to reach production not because the models don’t work, but because enterprises struggle with data governance. While pilot-phase AI projects demonstrate impressive results in controlled environments, they hit governance walls when moving to enterprise-scale deployments. This post examines why AI initiatives stall before production and provides a governance-focused approach for breaking the cycle.