Preparing your data for AI is only half the challenge. Intenda CTO Jaco van Niekerk explores why enterprise AI also requires governance, validation, traceability and human oversight.

Over 25 years helping organisations solve data challenges across a range of industries, we’ve found that while business objectives differ, the underlying data challenges are often very similar. Discover the four recurring issues we’ve encountered, and why recognising them is the key to building future-ready data foundations.

Companies often struggle to consolidate information distributed across multiple systems, repositories and historical data stores. This use case explores how Fraxses enabled a major European retail bank to modernise general ledger consolidation through governed, scalable and point-in-time reporting.

Many enterprise AI initiatives struggle not because of weak models, but because of fragmented, inconsistent data. Discover why data architecture is the true foundation of successful AI.

A first look at what’s coming in the next Fraxses release, from a refreshed UI and smarter Discovery to improved caching and new data synchronisation capabilities.

Fraxses is driving digital transformation at a national auditing authority by automating data integration, accelerating processes once measured in hundreds of hours to minutes, and establishing new benchmarks for efficiency, consistency and public accountability across government audits.

Fraxses is transforming audit at a state-owned utility by enabling full-population assurance, faster cycles and significant year-on-year savings, setting a new standard for efficiency, accuracy and accountability in large-scale public sector audits.

Intenda’s Data Intelligence solution uses ontologies and Common Data Models to unify, structure, and govern enterprise data, enabling secure integration, automation and AI-powered insights.

Our Data Intelligence solution leverages the power of AI, making LLMs work seamlessly within an enterprise’s ecosystem by providing relevant and precise context to business-specific data.

The Pangolin release introduces a series of front-end enhancements and improved interoperability with Microsoft Azure.

Our new white paper, Modern Data Management for FMCG Analytics: Data Lake vs. Ontology Platform, weighs up the pros and cons of Azure Data Lake Storage and a modern ontology platform, before recommending which approach is optimal for FMCG businesses.

The Cheetah release offers streamlined architecture, support for native Spark drivers, and improved resource utilisation and scalability. These and other backend enhancements are facilitated by the integration of Apache Kyuubi into Fraxses.