As enterprises accelerate investments in AI, they’re hitting a familiar wall: the data isn’t ready. Despite powerful models and cloud-native infrastructure, many initiatives stall or fail — not because of weak algorithms, but because the underlying data and metadata are fragmented, undocumented, or inaccessible.
IBM and Forrester have echoed a common frustration: 80% of AI project time is spent on data preparation. This is especially true in enterprises, where data often lives in legacy applications, sprawling ERP systems, or highly customised SaaS environments.
Without clear visibility into what data exists, how it’s structured, or what it means, AI teams waste time hunting for insight instead of creating it.
Metadata is not just about documentation. For AI projects, it underpins:
- Data discovery
- Data lineage and explainabiity
- Governance and regulatory compliance
- Model accuracy and reliability
However, most organisations treat metadata as an afterthought. Without accessible, up-to-date business metadata, even the best AI models risk being trained on incomplete or misinterpreted data.
Enterprise data systems aren’t built for discovery:
- SAP ECC has over 90,000 tables — many unused, undocumented, and unlinked.
- Salesforce evolves constantly through customisations and AppExchange components.
- Legacy systems (e.g., COBOL) are still mission-critical but poorly documented.
In these environments, traditional cataloguing tools fall short. They don’t surface business-level metadata, map relationships, or keep pace with change.
For AI to succeed, organisations need:
- Automated metadata discovery from SAP, Oracle, Salesforce, and legacy platforms
- Relationship mapping between tables and business entities
- Metadata provisioning into catalogs and ML environments
- Change tracking to reflect agile development cycles
Manual efforts are too slow and costly. Without automation, AI teams are flying blind.
Before investing in AI, ask:
- Do we know where our critical data lives?
- Can we access business-level metadata?
- Are system relationships and dependencies documented?
- How long does metadata discovery take — and how much does it cost us in delay?
- Can our data scientists trust the data?
If you’re unsure about any of these, your AI project may be at risk before it starts.
Metadata is foundational — not optional. Implementing a scalable metadata discovery and management solution helps:
- ⏩Accelerate project timelines
- 📈 Improve model accuracy
- 🛡 Ensure compliance and governance
- 🔄 Reduce risk and rework
In complex enterprise environments, metadata isn’t a side project — it’s the key to unlocking the full value of AI.