Systems | Development | Analytics | API | Testing

Build a Custom OBDC Driver as a Server

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.

Why Healthcare Organizations Need Governed AI Analytics

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.

Microsoft and Disclosure Management: A Smarter Way to Work

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.

Why Enterprise Teams Are Doing xP&A Planning Directly in Their BI Tools

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?

How to Connect Power BI to Amazon DataZone (Without a JDBC Bridge)

Amazon DataZone is a powerful data management service that lets teams catalog, discover, and govern data across AWS environments. But when it comes to connecting your BI tools, options are limited. Data teams trying to connect Power BI to Amazon Datazone often hit the same wall when every guide, forum thread, and AWS doc points you toward a JDBC bridge or driver. However, Power BI doesn’t speak JDBC natively, which quietly costs data teams time, stability, and patience.

Raising the Bar: Can Your Charts Do This?

Visualizations in business intelligence software are often dismissed as a “commodity”, interchangeable and easy to overlook. But what this perspective ignores is that visualizations are a gateway to better understanding data. Instead of parsing through raw data, they make key details and trends visible so that users can easily interpret the insights derived from all the data gathering, preparation, and analysis.

Data Integration Tools Aren't the Problem. Your Source Data Is.

Data integration tools are designed to move and join data. But what they’re not designed to do is burn half their capacity cleaning up what arrives at the input. When a source exposes a schema built for application performance rather than analytics, the pipeline must compensate: Anything typed as a string because it was easier at build time gets cast into numbers or dates before a calculation can touch it. The difficult truth is this is cleanup and not value-added integration work.

The 5 Pillars of AI-Ready Data (Explained in 60 Seconds)

Most organizations are investing heavily in AI—but the outputs still aren’t reliable. The reason often isn’t the model. It’s the data pipeline behind it. Disconnected systems, inconsistent preparation, and limited governance make it difficult for AI to produce accurate answers. Before AI can deliver real value, the data feeding it must be structured, contextualized, and governed. In this animation, we break down the 5 Pillars of AI-Ready Data and show how data moves through a connected pipeline before it reaches AI.

The 5 Pillars of AI Ready Data

Most AI failures aren’t model problems. They’re data pipeline problems. Disconnected systems. Inconsistent preparation. No governance at query time. This short animation walks through the 5 Pillars of AI-Ready Data and shows how data needs to move through a structured pipeline before it can power reliable AI. 5 Pillars of AI-Ready Data Access → Prep → Context → Governance → Monitoring Five stages. One connected flow.

Oracle ERP Dashboard: How to Get Live Data Out of Your ERP and Into Dashboards That Actually Work

If you’ve spent any time working with Oracle ERP data, you know this tale: your dashboards look polished, but the numbers inside them are hours or days old. The promise of modern cloud ERP was real-time business intelligence, yet most finance and operations teams are still clicking through static reports, waiting on IT for extracts, and making decisions based on business data that no longer reflects what’s actually happening.