Financial institutions still struggle with data quality , despite double-digit growth in data spending over the past few years (according to Forrester and Gartner studies). The insatiable appetite for data has driven incredible growth in cloud and lakehouse adoption, yet data maturity improvements remain flat or only slightly improved. Why can't we get data quality right? The

What if we could fundamentally shift how we ensure trust in financial systems? Rather than adding more bespoke code and narrowly focused solutions for each risk or compliance issue, what if we could create a dynamic, intelligent system that proactively identifies and mitigates risk and compliance issues? This question drove my exploration of data agents using Hasura's technology, with results that point toward an exciting north star for financial integrity. The challenge: achieving scalable

The final piece of the puzzle: Data access layer In the previous installments of this series (part 1 , part 2 ), we explored how financial institutions can break free from the data doom loop by leveraging data products and data contracts. By implementing these concepts, firms can take ownership of their data, enha

Learn why tracking the evolution of your data architecture in Hasura's Data Delivery Network (DDN) isn't just nice to have – it's essential for robust governance and intelligence.

In Part 1, we covered the data doom loop – the cycle of complexity, inefficiency, and eroding trust in financial data that persists despite massive investments. The solution isn’t another sweeping architectural overhaul. It’s about fixing how data is defined, accessed, and governed at the source. That’s where data products and data contracts come in. These concepts aren’t just buzzwords – they’re pragmatic, enforceable w

Financial firms that successfully escape the data doom loop are shifting toward a pragmatic, business-driven approach focused on three key elements.

This last-mile perspective transforms anomaly detection from a data quality tool into a strategic business capability. So, instead of just catching errors, it becomes a tool for validating the business's narrative across all data domains

What's becoming increasingly clear is that active metadata isn't just another tool in the modern data stack – it's a key enabler of several contemporary data management strategies.

Imagine a simple, standardized, data-source-agnostic data quality service that could complement existing approaches and dramatically improve data health through rapid feedback loops.

GraphQL and OpenAPI define agreements between data consumers and providers, but they have significant differences in capabilities and contract specifications. Whichever you use, it's essential to be intentional, understand your use case, and design for the best outcome.

Leveraging the supergraph architecture to achieve data governance objectives is a strategic play that delivers a solid return on investment. It reduces complexity, lowers costs, and improves metadata accuracy.