Systems | Development | Analytics | API | Testing

Last April FloQast, an American maker of accounting software, published something unusual: a detailed engineering post on Amazon Web Services’ machine-learning blog, co-authored with AWS personnel, explaining precisely how its AI-powered transaction-matching feature works under the hood. The post described cloud infrastructure, model selection, and the specific technique (generating matching rules from user-supplied examples) that powers its AutoRec product.

You’ve heard the pitch: AI is going to revolutionize finance. It’s going to write your variance commentary, spot anomalies before you do, answer questions about your data in plain English, and free your team from the drudgery of month-end prep so you can focus on what actually matters: strategy, decisions, and moving the business forward. It’s easy to see why you’d believe the hype.

Your analytics team built a report. It worked fine in development, but when it went into production, users began to complain about loading time. Your team has checked the database and looked at the dashboard configuration, but nobody can find the problem. There’s a good chance the cause is a cross join, and there’s an even better chance it’s executing in the wrong place.

The results of Info-Tech’s Head-to-Head: Business Intelligence & Analytics – Enterprise are in. In the report, Info-Tech and SoftwareReviews evaluated 360 verified enterprise end-user reviews to set benchmarks for vendor capabilities, product experience, and the full customer relationship. The study evaluates 11 areas amongst software vendors that are especially critical to making and fostering strong, long-term relationships between software providers and their customers. These include.