Systems | Development | Analytics | API | Testing

Database Trends and Applications (DBTA) has released its 2026 Readers’ Choice Awards, a competition voted on by DBTA readers to recognize the best information management products, services, and solutions. This year, we’re proud to announce that Simba Intelligence was voted Best Semantic Layer Solution. Here, we discuss the award and why a semantic layer shouldn’t be an afterthought to your data and analytics strategy.

You’ve invested in SAP Business Data Cloud to unify your analytics. Your SAP data flows into its’s semantic models and Joule. But your Oracle financials, Microsoft Dynamics operations, and Workday HR data still sit isolated—separate analytics tools, separate metrics, separate dashboards. The result is an incomplete picture. Your analytics reflect part of how your business runs, not all of it.

Implementing a PXM solution provides numerous benefits to your organization, from improving efficiency to increasing sales, reducing returns, and promoting customer loyalty. Today, we’re going to explore these benefits in more detail. Interested in the distinctions between PIM and PXM? See our breakdown of how they differ (but are also similar) here.

Most AI content assumes your data is in the cloud. But for a meaningful segment of enterprises, cloud-only AI tools block them at the pass. For regulated industries like manufacturing and healthcare, data residency requirements, compliance mandates, security policies, and simple operational reality mean sensitive data must remain on-premises.

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.

Ever since AI-driven analytics burst onto the scene, product leaders have been racing to adopt it. Promoted as a way to stay ahead of the curve, AI analytics bring the promise of streamlined processes, personalized recommendations, and a more efficient user experience. But AI advancements aren’t without pitfalls, chief among them inaccuracies caused by AI hallucinations and pilot projects not making it to production.

If you’re a data engineer or architect who’s been handed a database modernization mandate, the conversation usually arrives pre-loaded with a conclusion. For example, if the legacy system needs to go or the data needs to move. When thinking about data migration, it’s important to ask yourself whether data migration is the right approach at all.