It’s no secret that financial institutions face enormous pressure to keep up with regulatory requirements, business innovations, and customer expectations – often all at the same time. One of the trickiest challenges? Data governance.
How do you make data accessible to analysts, developers, and AI applications across your firm without losing sight of security, compliance, and transparency? Fortunately, Hasura’s metadata-based approach provides an elegant solution, acting as a semantic layer for your data while preserving clear lineage and robust governance. Below, we’ll explore what that means, why it’s important, and how it can help make your enterprise more AI-ready.
Why data governance is such a headache for banks
Banks today face a perfect storm of data challenges. With petabytes flowing through multiple systems, three key issues stand out:
- Fragmented systems: Legacy platforms and departmental solutions create a maze of databases with conflicting standards. Finding a single source of truth becomes nearly impossible.
- Regulatory pressure: Banks must demonstrate data accuracy and lineage for stress testing, risk reporting, and privacy compliance. This spans frameworks from Basel regulations to GDPR, with steep penalties for non-compliance.
- Data quality concerns: Without proper data governance, inconsistencies multiply. Teams spend more time reconciling data than using it.
Enter Hasura and its metadata-based configuration: A promising approach to address all these pain points.
Hasura’s secret sauce: Metadata-based configuration
In a nutshell, Hasura automatically generates APIs (GraphQL and REST) from your data sources. The twist is that all of its configuration – schemas, permissions, relationships, and beyond – is stored in structured metadata. This metadata is:
1. Open and transparent: Every configuration is out in the open in human-readable files. It’s a compliance officer’s dream, making it easy to audit and verify.
2. Consistent and declarative: Permissions and access controls are defined in a single place, not scattered across different microservices, code repositories, or databases.
3. Extensible: You can tweak and transform the metadata, for example, to convert Hasura metadata from PostgreSQL to MongoDB (or to any other data source you’d like to support in the future).
Think of it like a blueprint for your data architecture. It simplifies governance by ensuring everyone speaks the same language and sees the same “source of truth.” In the same way architects use building codes to keep contractors aligned on what’s safe and legal, Hasura’s metadata keeps your data ecosystem aligned on what’s secure and compliant.
A semantic layer that’s AI-ready
Sure, we’re living in a data-driven world. But we’re now on the cusp of an even bigger jump: AI-driven insights. Hasura’s PromptQL solution provides a ChatGPT-like interface to your enterprise data. Couple that with APIs, and Hasura’s metadata model really shines:
1. Accessible, consistent data for AI and LLMs
• Data scientists and AI teams can access data via Hasura’s API endpoints, confident in consistent naming conventions and definitions.
• With a structured semantic layer, PromptQL can operate on your data in place, without the need to move and transform data to fit the unstructured data requirements of LLMs
2. Faster experimentation and model development
• Because Hasura’s metadata is declarative and easily version-controlled, spinning up a new environment or adding a new field for an AI proof-of-concept is frictionless.
• Teams can focus on building advanced analytics and natural language processing (NLP) applications rather than untangling database connection strings.
3. Fine-grained permissions for sensitive data
• Financial data is highly sensitive, making Hasura’s metadata-driven access controls essential for ensuring that only authorized users, applications, and AI tools can access the data they’re permitted to see.
When your data structure is stable, standardized, and clearly documented, your AI initiatives get off to a flying start – and you reduce the risk of running afoul of compliance in the process.
Practical example: Converting metadata from PostgreSQL to MongoDB
A shining example of Hasura’s metadata extensibility is the ability to convert metadata between different databases, say from PostgreSQL to MongoDB. Why is this such a game-changer?
- Reduced friction: Large banks with hundreds of microservices might prefer different databases for different tasks. A single tool to manage transitions prevents duplication of effort.
- Consistent access layer: Developers don’t have to learn a new approach or stand up a new service for each database. Hasura’s APIs remain uniform across the bank’s ecosystem.
- Data lineage: It’s no longer “mystery meat” data. You’ll know exactly how an _id field in MongoDB maps back to the id in PostgreSQL—great for those routine but oh-so-exciting compliance audits.
Expanding horizons: Other use cases for Hasura metadata
1. Centralized security and compliance
• Host your Hasura metadata in a secure repository. Integrate with your existing risk management systems to automatically enforce policies like data masking or encryption for specific fields.
2. Cross-department data collaboration
• With multiple lines of business, you can enforce standardized naming conventions and data definitions at the metadata level. This fosters synergy between departments and reduces miscommunication (no more “Wait, is balance a decimal or a string?” nightmares).
3. Real-time policy enforcement
• As soon as a compliance rule changes – say, a new regulation limiting which fields can be used to identify a customer – it can be updated in Hasura’s metadata. The effect is immediate and uniform across all APIs.
4. Risk management and stress testing
• By having a consistent blueprint of your data, you can easily run risk simulations across aggregated data sets without manually stitching them together. The metadata ensures that the correct fields are included or excluded in each scenario.
5. Audit-ready reporting
• Create dynamic dashboards that pull from Hasura APIs to show who accessed what data and when. This “paper trail” can drastically cut down the time it takes to prepare an audit response – or defend a finding from internal compliance.
Conclusion
Data governance is the unglamorous foundation on which the glamorous stuff – like advanced analytics, customer personalization, and next-gen AI – gets built. Hasura’s metadata-based approach transforms what can be a messy, untraceable web of scripts, APIs, and policies into a well-documented, transparent, and secure ecosystem.
If you’re tired of spending late nights combing through custom code trying to piece together a data lineage flow—imagine a better way. By embracing Hasura, your bank won’t just meet governance standards; you’ll exceed them, unlocking new value in everything from routine data queries to cutting-edge AI initiatives. After all, it’s a lot easier to harness the power of your data when you know exactly where it’s coming from, who’s using it, and how it’s being transformed.
Who knew compliance and data lineage could be this exciting? Well, perhaps “exciting” is still a stretch—but with Hasura’s metadata, at least it can be simpler, clearer, and more efficient than ever before. And in the world of risk-averse finance, that is something worth celebrating.
Ready to give Hasura a spin?
It might not make your audits a breeze, but it’ll certainly make them far less nerve-wracking. And who knows, maybe you’ll even impress a regulator or two with your well-documented data lineage and foolproof access control. Cheers to that!
