Time-traveling through your data architecture: Using data agents to understand change

Your sales reports are off, and you suspect a data definition change. Sure, the update was probably logged… buried in some system, lost in a sea of emails, or mentioned in a meeting you missed. If only there were a single, proactive way to see these critical changes.

In complex data ecosystems, understanding how things change is just as important as knowing how things work. That's why keeping tabs on changes in the rules and shapes of your data within Hasura's Data Delivery Network (DDN) and analyzing changes with data agents like PromptQL, isn't just nice to have – it's essential for robust governance and intelligence.

Hasura's DDN implements a Universal Data Access Layer (UDAL) through a supergraph architecture. It serves as both orchestrator and guardian in decentralized data environments to:

• Validate data contracts and relationships across domains
• Orchestrate operations while enforcing security policies
• Provide abstraction from underlying technical implementations
• Identify semantic ambiguity and imprecision in your data model

But to unlock its full potential, your architecture needs one critical capability: a temporal database that tracks how metadata evolves.

Think of your metadata as the "constitution" of your data ecosystem. It defines what's possible, who can do what, and how domains interact. When this constitution changes, you need to know:

• When did access policies change and who modified them?
• How have data contracts evolved over time and between environments?
• Which semantic definitions have drifted from their original meaning?
• What composition rules have been added, modified, or deprecated?

Without tracking those changes over time, you're flying blind through your data history, unable to understand how you arrived at your current state, and good luck figuring out where you're headed.

Semantic drift is inevitable as your business evolves. Temporal tracking identifies patterns like:

• accountBalance becomes accountBalanceAsOf as temporal context becomes necessary
• tradingVolume gets clarified to specify the aggregation method and period
• customer may add qualifiers within a domain to prevent misunderstandings (for example, customer may become consumerBankingCustomer)
• trade, transaction, and deal emerge as synonyms needing reconciliation

By tracking these changes, you gain powerful insights into how your organization thinks about data – and where confusion might arise as concepts evolve.

The magic happens when you combine temporal metadata with operational analytics to understand:

1. Usage pattern analysis: How actual data consumption evolves in response to contract changes
2. Quality impact analysis: Correlations between contract modifications and quality issues
3. Cross-domain intelligence: Patterns in how domains interact and evolve together
4. Predictive analysis: Potential impacts of proposed changes based on historical patterns

The power lies in integrating the temporal database with your supergraph, enabling data agents like PromptQL to translate historical data into actionable intelligence through natural language queries, such as “What schema changes preceded our recent data quality issues?”

See this process in action!

Beyond incident analysis, data agents leveraging AI, like PromptQL, excel at identifying semantic problems – field names that misrepresent data, similar terms that should be differentiated, or different terms that could be harmonized. You can proactively address semantic inconsistencies by combining AI-powered analysis with historical tracking.

Plus, it hooks up with Neo4j for graph analytics, showing you relationship patterns and dependencies you'd miss otherwise. You get a really clear, complete picture of how your data world works.

Temporal tracking simplifies governance by letting you compare states between any points, track policy lifecycles, understand contexts behind changes, and provide auditors with comprehensive historical evidence – all without external versioning systems.

This is invaluable for governance teams enforcing standards. With historical context and analysis powered by AI-driven data agents, they can provide early warning of issues before problematic patterns become deeply embedded in your data ecosystem.

Some argue that source control already tracks changes. But this misses a crucial distinction: Your supergraph represents the integrated state of all contracts, rules, and relationships.

Source control shows individual pieces evolving in isolation. Temporal tracking shows how everything fits together at any point – the "ground truth" of what's possible and how it has evolved.

Implementing temporal tracking doesn't require specialized databases. Hasura's solution uses a straightforward pattern that works with the databases you're already using, proving you can add powerful historical tracking to the technology you already have.

To get started, Hasura has open sourced the Temporal Data Catalog Connector on GitHub, providing implementation details and code for your Hasura DDN using familiar database technologies.

The payoff is substantial: better governance, deeper insights, and proactive management of your data ecosystem, facilitated by intelligent data agents. In increasingly complex data environments, this historical context isn't just valuable – it's essential.

After all, to truly understand where you're going, you need to know where you've been.

Want to see how intelligent data agents go beyond tracking changes to proactively ensure financial trust? Dive into the next blog: Scaling Financial Trust with Intelligent Data Agents to explore AI-driven risk detection and compliance at scale.

In Part 2 of this series, we explore how intelligent data agents can transform financial trust by enabling scalable risk detection and compliance. Stay tuned to see how AI-driven insights are redefining transparency in finance!