Data access layer: Unlocking the full potential of financial data

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, enhance quality, and improve compliance.

But one crucial step is left to truly operationalize this strategy: the data access layer.

The data access layer serves as the bridge that connects your well-defined data products and contracts with the systems, teams, and tools that need them. It’s the piece that makes everything work together seamlessly – enabling flexible, controlled, and compliant access to data while maintaining the integrity of the contracts and ensuring governance is upheld.

In this post, we’ll deep dive into what a data access layer is, how it works, and why it’s essential for financial institutions looking to optimize data usage and ensure compliance at scale.

A data access layer is an abstraction layer that sits on top of your diverse data sources (from transaction systems and relational databases to data lakes and real-time feeds). It acts as a unified interface for consuming data products, operationalizing data contracts, and enabling access across multiple systems and teams.

Think of it as the entry point for all data interactions, providing an organized and consistent way to access the data you’ve defined in your data products and data contracts. It ensures that users can access the right data – at the right time, in the right format – without exposing unnecessary complexity or compromising compliance.

The introduction of a data access layer provides several key benefits that are crucial for financial institutions:

1. Consistent, compliant data access
   Financial institutions operate in highly regulated environments. Ensuring that data is accessed securely, per data sovereignty rules, and in a compliant manner is non-negotiable. The data access layer enforces access controls, tracks lineage, and ensures that all regulations are adhered to automatically.
2. Flexible access across domains
   Financial institutions are complex, with various teams needing access to different data types. The data access layer offers a single entry point to access data across domains – whether compliance, risk management, or trading – without disrupting existing systems or workflows.
3. Scalability and agility
   As financial data grows in volume and complexity, the need for a scalable and agile data infrastructure becomes paramount. The data access layer supports this by offering modular scalability, ensuring your data strategy evolves with your business’s needs. It enables quick adjustments and updates without requiring major overhauls of your systems.
4. Improved data quality and trust
   By operationalizing data products and data contracts, the data access layer ensures that data consumed across the organization is consistent, validated, and high-quality. This improves data trust and transparency, ensuring decisions are based on reliable, accurate information.

To achieve these benefits, a data access layer needs to support several key capabilities:

1. Universal source support
   Financial institutions manage a diverse set of data sources, including core transaction systems, real-time market feeds, and legacy systems. The data access layer must seamlessly connect to a wide range of data sources – both structured and unstructured – enabling real-time and batch data access across systems.
2. Flexible query methods
   Different users have different needs when it comes to accessing data. The data access layer should support a variety of query methods – such as REST APIs, GraphQL, SQL, and even streaming data access – to ensure that stakeholders, whether they’re data scientists, developers, or analysts, can access data in the format they need.
3. Data composition and aggregation
   Financial data is often spread across multiple systems and domains. The data access layer must enable data composition –the ability to flexibly combine and aggregate data from multiple sources – while respecting the defined relationships and validation rules in the data contracts.
4. Automated validation and monitoring
   Data quality is crucial. The data access layer should provide built-in validation and monitoring to ensure consistently enforced data quality rules. This can include active validation (where consumers define rules) and passive validation (automated anomaly detection).
5. Security and compliance
   Financial data is sensitive. The data access layer must consistently enforce security protocols, including access control, data encryption, and auditing. Additionally, it should support data sovereignty policies – ensuring data is only accessible in specific jurisdictions and according to all necessary regulations.
6. Observability and monitoring
   The data access layer must provide insights into data usage patterns, user context, and dependencies. This observability enables better decision-making, allowing teams to easily optimize data usage, troubleshoot issues, and ensure compliance.

The data access layer doesn’t replace existing systems – instead, it complements and enhances them. Whether you have a lakehouse, data warehouse, or operational data stores, the data access layer sits on top, providing a unified interface that makes data more usable and accessible.

Here’s how it works:

• In lakehouse architectures, the data access layer adds business context to raw data, standardizes access patterns, and ensures consistent governance across data lakes and operational systems.
• It ensures smooth integration between real-time and batch processing, enabling teams to work with a unified data view without worrying about the underlying technical complexity.

Implementing a data access layer: A step-by-step approach

1. Define the data access strategy
   Start by understanding the data needs of your organization. Define what data needs to be accessed, by whom, and for what purposes. This will guide your implementation and ensure you build the correct access layer.
2. Implement data contracts and products
   Before deploying the data access layer, ensure that data products and data contracts are well defined. These products and contracts drive the access layer’s functionality, ensuring all rules and expectations are embedded from the start.
3. Choose the right tools
   Choose the right tools to support the data access layer depending on your institution’s architecture and needs. Look for solutions that offer flexible query interfaces, robust security features, and native integration with your existing data systems.
4. Start small, scale gradually
   Implement the data access layer incrementally. Start with a few data domains or use cases, validate the functionality, and expand as your organization gains confidence and usage grows.
5. Monitor and optimize
   Continually monitor the performance and usage of the data access layer. Gather user feedback, track data usage patterns, and optimize access controls, security protocols, and query methods accordingly.

With a data access layer in place, financial institutions are now equipped with a unified, secure, and flexible way to consume data across teams and systems. But the journey doesn’t end here.

By combining data products, data contracts, and a data access layer, organizations move away from the data doom loop, making smarter, data-driven decisions while ensuring compliance, efficiency, and agility.

This approach also sets the stage for future innovations, like AI-driven analytics and regulatory automation, allowing your institution to adapt quickly to new challenges and opportunities.

In the final part of this series, we’ll wrap up by looking at how the combination of these strategies drives long-term success in financial services data management. Stay tuned!

Is your institution ready to implement a Universal Data Access Layer? Contact us to learn more about how this critical component can help you unlock the full potential of your data and break free from the data doom loop.

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