Systems | Development | Analytics | API | Testing

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.

It’s no secret that businesses everywhere are striving to use AI to automate time-consuming or repetitive tasks. By far one of the most time-consuming tasks business people face is reporting. Whether due to custom exports draining developer time, BI tools that can’t handle document output, or driver maintenance wasting time and focus, reporting can be draining to an organization’s most valuable resources.

With the Simba Technologies SimbaEngine SDK, you can build your own custom OBDC, OLEDB, JDBC, or ADO.NET driver to connect your data source to any application, but did you know that you can create a driver that runs on a server with the switch of a configuration setting? You can convert a SimbaEngine SDK ODBC driver into a server by switching build configurations in Visual Studio within Windows or adding BUILDSERVER=exe to your makefile in Linux, then configuring a registry or INI file.

For healthcare organizations, AI governance is a must-have that can’t be ignored. To safeguard sensitive patient information, healthcare is subject to a variety of different regulations, for example HIPAA in the United States and GDPR in the European Union. As healthcare organizations implement AI, it brings a balance of efficiencies and risks.

Finance teams don’t need another system to learn. They need their existing tools to work better. That’s the core idea behind Next Generation Disclosure Management from insightsoftware — insightsoftware’s narrative reporting solution that plugs directly into Microsoft Word and Excel, bringing live data, built-in controls, and a full audit trail into the environment your team already knows by heart. No new interfaces. No copy-paste marathons.

Most enterprise finance teams already have a BI platform they trust. Power BI and Qlik Sense power the dashboards that executives review every day. They’re where analysts spend their days, where the business goes to answer questions, and where the organization has invested years of development and governance work. So why, when it comes time to planing, forecasting, and budgeting, does everyone abandon that environment and disappear into a tangle of spreadsheets?