Next-gen banking success starts with the right data architecture

February 28, 2025by Aziz Shaikh and Henning Soller, with Aysen Cerik, Fares Darwazeh, and Margarita Mlodziejewska

On average, a bank spends about 6 to 12 percent of its annual technology budget on data.¹ Banks aim such investments at harnessing a portion of the $2.6 trillion to $4.4 trillion in potential global industrial value from deploying gen AI to gain insights and realize efficiencies in these banks’ complex systems worldwide. However, data implementation plans often lack well-defined business cases and thus fail to deliver on their full potential value.

But according to McKinsey analysis, with the right data architecture archetype, banks could cut their implementation time in half and lower costs by 20 percent. Realizing maximum value, efficiency, and savings is even more vital when new systems must be scaled across multiple countries and be compliant with a range of regulations. Without an optimal architecture, this process can be a major cost driver. At the same time, banks need to remain a step ahead of new threats to data privacy and cybersecurity as well as changing regulations, including the General Data Protection Regulation (GDPR), the Basel Committee on Banking Supervision (BCBS) 239, and the Digital Operational Resilience Act (DORA).

Each data architecture archetype is more or less suited to a bank’s unique array of business and analytics needs. Assessing those needs and determining the appropriate architecture involves complex considerations. A detailed decision-making road map can provide the guidance and insights essential to informing the process and building a solid foundation for success.

Realizing digital transformation’s full value: Common barriers and enablers

Most banks made significant investments in their data transformation journeys in the past five to ten years. While some were able to finalize their transformations successfully, most banks either could not finalize them or could not realize their expected impact. In a 2022 McKinsey Global Survey on digital strategy and investments, for instance, less than one-third of the expected value of digital transformations and initiatives was captured, and only 16 percent of surveyed executives said their transformations successfully improved performance and led to sustained long-term gains.

In our experience, incomplete digital transformations in banking typically result in one of three scenarios:

• Legacy IT stack and data architecture. Data architecture has not been transformed, and a spaghetti architecture remains in place, along with legacy platforms and tools.
• Fragmented data warehouses and data lakes. Data transformation was not finalized, and banks must manage old and new platforms simultaneously.
• Core data transformation without a new stack or proper tool use. Data transformation was finalized, but the impact was then curtailed by either using new platforms and tools inefficiently or not using them at all.

Each of these scenarios limits the potential gains from the transformation and creates inefficiencies such as delayed decision-making, along with security and compliance risks and elevated costs from maintaining multiple complex environments.

Five keys to unlocking digital transformation value

Among banks that do realize the targeted value from their transformations, five common best practices stand out as significant enablers. Adopting these practices can lead to 20 percent cost reductions for platform builds, 30 percent faster time to market, and 30 percent lower change costs, according to McKinsey analysis.

The five overarching best practices are the following:

• Building true data platforms. Using new architectural approaches can enable data and process synergies that span countries and business lines.
• Opting for open-source and cloud-provisioned platforms. Opting for these platforms rather than vendor platforms can improve cost efficiency by reducing licensing fees and infrastructure costs, enhance the scalability of resources, and avoid vendor lock-in.
• Enabling optimal automation. Automating as many processes as possible can enhance quality checking and speed deployment, allowing for faster delivery.
• Enhancing existing platforms. Strategic upgrades and adaptations of existing platforms can enable new capabilities, including gen AI applications, without the time or expense involved in building new platforms from scratch.
• Enabling lab environments. Using such environments can allow data scientists to innovate and experiment with data while leaving a bank’s data intact.

Designing the right data architecture: Principal considerations and strategies

There are five data architecture archetypes: data warehouse, data lake, data lakehouse, data mesh, and data fabric. Using data warehouse and data lake archetypes alone is no longer a common practice. Other archetypes are typically used alone or in combination—for example, a data mesh and a data fabric operating on an underlying database solution—and are, ideally, selected to align with a bank’s vision and strategies for its data and business (Exhibit 1).

When designing a data architecture, the overarching considerations are core system complexity, cost, flexibility, and risk. More specifically, considerations and evaluation criteria include the following:

• Integration complexity. Assess how complicated and difficult it will be to integrate all components and layers—such as an enterprise data warehouse and a data lake—into a single data architecture.
• Cost and resource allocation. Consider financial implications along with any licensing, infrastructure, maintenance, and resources required.
• Scalability and flexibility. Determine the architecture’s ability to accommodate increasing data volumes, evolving needs, and new technology integrations effectively.
• Business continuity and risk. Ascertain the potential impact on business continuity and risks, and establish mitigation strategies.
• Business enablement. Estimate the impact on the business and future possibilities, factoring in critical use cases and optimal steering.
• Regulatory remediation. Determine the best data strategy to comply with regulatory requirements, and deploy the appropriate remediation.
• Overall strategy alignment. Establish requirements based on the bank’s overall business strategy, including, for example, M&A readiness and group consolidation.

For multinational banks, the process of decision-making is complicated by the need to incorporate the guidelines of central and local entities. Several multinational banks have initiated programs to establish groupwide platforms. They have set up overarching platforms that span multiple entities, including countries and business lines, along with platforms that allow for easy integration of further entities into a standardized data model used throughout the group. Additionally, they have established standardized tools and methods on top of the platform to enable overarching steering for the group.

Matching data archetypes with aspirations: Selection criteria and decision road maps

Factoring in a bank’s existing capabilities and planned use cases for its data provides critical insights to inform decisions on archetypes. Each archetype is either well-suited or ill-suited to a given set of conditions, circumstances, needs, and goals. Navigating these various and often overlapping scenarios can seem daunting. However, the process can be made more straightforward by breaking down a bank’s existing circumstances and desired outcomes into ten decision-criteria elements and answering “yes” or “no” to each corresponding question, such as the following:

• Geographic footprint. Does the architecture need to support data access and management globally, across regions, and with minimal latency?
• Scalability and flexibility. Must the architecture enable seamless scaling of data volumes and adaptation to evolving needs?
• Data governance. Does the data architecture need to be capable of supporting comprehensive data governance controls—including data stewardship, policy enforcement, and audit trails—across diverse data environments?
• Data security and compliance. Does the data solution need to provide robust support for and enforcement of security and compliance measures such as encryption, access management, and regulatory adherence across multiple jurisdictions and data types?
• Data variety. Does the solution need to support the management of a wide range of data types (structured, unstructured, and semistructured)?
• Business domain specificity. Do business processes require specialized data solutions that cater to specific industry or domain needs, with tailored data models and analytics?
• Data interoperability. Does data from multiple sources and formats need to be integrated and processed efficiently in real time?
• Stream processing. Does the data architecture need to be capable of handling high-throughput, low-latency stream processing efficiently for real-time analytics and decision-making?
• Metadata management. Is an advanced metadata management system that supports detailed data lineage, impact analysis, and comprehensive data cataloging required?
• Cost-effectiveness. Does the solution offer the best cost-effectiveness when balancing storage capabilities and future scalability requirements?

Once the required criteria are identified via “yes” answers to the questions, each archetype can be evaluated according to its ability to meet specific requirements (Exhibit 2).

After the archetype is selected and a robust data architecture is designed, fully implemented, and scaled, rigorous follow-up to assess the need for improvements must be done regularly to ensure the setup remains sustainable and continues to deliver value well into the future.

Getting started: Essentials for leaders

Leaders seeking to ensure their banks’ digital transformations are optimized to yield maximum value can begin by conducting high-level diagnostics of business needs and data maturity assessments to understand their requirements for data architecture and data governance as well as identify any gaps that can be shored up by adopting best practices. An architecture blueprint can then be designed alongside a road map for model design and required governance. Finally, implementation can be rolled out use case by use case in an iterative manner.

Aziz Shaikh is a partner in McKinsey’s New York office, Henning Soller is a partner in the Frankfurt office, Aysen Cerik is an associate partner in the Istanbul office, Fares Darwazeh is a consultant in the Riyadh office, and Margarita Młodziejewska is a consultant in the Zurich office.

The authors wish to thank Asin Tavakoli and Mitch Gibbs for their contributions to this blog post.

¹ Analysis is based on McKinsey’s Tech: Performance benchmarking product across seven industries: advanced industries, banking, consumer, global energy and materials, insurance, pharmaceuticals and medical products, and telecommunications, media, and technology.