Effective data management is challenging for businesses in almost every vertical.
But few have it as rough as those in the financial services industry, where challenges like legacy technology and strict regulations contribute to issues such as data silos, data modernization difficulties and a high cost when operating data pipelines.
Fortunately, a different approach to data management – Data Mesh – offers a solution. By enabling financial services businesses to migrate to a new type of data architecture, data meshes can help resolve some of the core data integration and access issues that have plagued the industry for decades.
What is a data mesh?
A data mesh is not a technology or architecture, but an organizational and operational paradigm designed to scale data in complex enterprises. It promotes domain-oriented data ownership, where teams manage their data as a product, using a self-service infrastructure and following federated governance principles.
In a data mesh, any team or department within an organization becomes accountable for the quality, discoverability, and accessibility of the data products they own. The concept emerged around five years ago as a response to the bottlenecks and limitations created by centralized data engineering teams acting as data gatekeepers.
Historically, most businesses employed a centralized data team to manage and serve organizational data. This led to delays, silos, and scalability challenges. Data mesh addresses this by distributing ownership and aligning data responsibilities with business domains.
Data management challenges in the financial services industry
Before explaining how Data Mesh can benefit the financial services industry, let’s discuss the deep-rooted, long-standing data management challenges that the industry has traditionally faced:
● Siloed data: It’s common for different departments, such as credit, underwriting, customer support and compliance, to store and manage data on different systems. This makes it challenging for one department to access information that is “owned” by another.
● Legacy technology: Some financial services businesses still depend on mainframes, on-premises infrastructure and other legacy technologies to store and transform their
data. This complicates data access while also contributing to high costs when moving data between systems.
● Slow data access: Banks, insurance companies and other financial services businesses often need to make fast, data-informed decisions. But their ability to do so is limited when they can’t quickly find and access the information they need due to problems like data silos, slow data integration processes and overburdened data engineering teams who can’t respond to requests rapidly.
● Strict regulations: Financial services organizations face especially stringent data security and privacy requirements. Meeting those high standards is difficult (not to mention costly) when data is siloed across disparate systems and when data pipelines include legacy platforms that lack modern security controls.
In short, the financial services industry has traditionally faced especially acute challenges when it comes to managing and integrating data. Those challenges have only intensified as the amount of data flowing within organizations has increased, and as the data has become ever more critical for making business decisions.
The benefits of Data Meshe in financial services
By adopting an organizational approach based on the data mesh concept, financial services companies can address several persistent data challenges, including delays in data access, lack of alignment between data and business needs, inconsistent governance, and the high cost of managing data at scale.
Improving the alignment between data and business needs
In a data mesh model, data ownership and stewardship are assigned to the business domains that generate and use the data. This means that teams such as credit risk, compliance, underwriting, or investment analysis can take responsibility for designing and maintaining the data products that meet their specific needs.
For example, an underwriting team might focus on maintaining rich, high-quality customer risk profiles, while a trading desk may prioritize fast access to real-time market data streams. This model reduces the dependency on centralized data teams and allows business units to respond more quickly to changes in priorities or market conditions.
Reducing delays in accessing and using data
Data mesh encourages clear definitions of data products and ownership, which helps reduce the bottlenecks often caused by fragmented data ownership or overloaded central teams.
When combined with modern data technologies—such as cloud-native platforms, data virtualization layers, and orchestration tools—data mesh can help organizations connect data across legacy mainframes, on-premises databases, and cloud systems.
For example, a bank might have customer data stored across a mix of older core banking systems and newer digital channels. With a data mesh approach, a relationship manager does not need to understand where the data resides. Instead, they define what customer insights they need, and the data platform makes that data product available with appropriate governance and controls.
Supporting consistent data governance
Data mesh introduces a federated governance framework, providing clear guidance and standards for security, quality, and data access across decentralized data products.
While this does not remove the need for strong compliance and regulatory oversight, it allows financial organizations to systematically enforce controls in a scalable way. Data privacy requirements such as GDPR or local financial regulations can be embedded into data product definitions, ensuring that data consumers across departments only access what they are permitted to.
For instance, a data product used by the marketing team may automatically exclude personally identifiable information (PII) based on policies defined centrally but enforced at the domain level.
Helping manage data operations at scale without excessive cost
Traditional data management models in financial services often rely on large, centralized teams to serve data requests across the organization, which leads to high operational costs and slow delivery times.
By decentralizing data responsibilities and promoting clearer ownership, data mesh can reduce the amount of manual work and repetitive integration efforts. For example, instead of the data engineering team repeatedly creating custom extracts for different reporting or regulatory teams, each domain can self-manage their approved data products, reducing both effort and delay.
This structure allows organizations to scale data operations as the business grows, without proportionally increasing the size or cost of their data engineering and analytics teams.
The caveats of data meshes for financial services
While data mesh offers a compelling model to address persistent data challenges, implementing it in the financial services industry presents distinct complexities.
The most significant barriers are cultural and operational. Financial institutions are traditionally structured around centralized controls, strict processes, and a risk-averse mindset. Transitioning to a model where business domains are responsible for their own data products requires fundamental changes to organizational processes, roles, and decision-making structures. Success depends heavily on strong internal education, clear governance principles, and sustained organizational support to ensure that teams are empowered while maintaining alignment with enterprise standards.
A second key challenge is integration with legacy systems. Many banks, insurers, and asset managers operate critical processes on mainframes or older on-premises platforms. Connecting these systems to domain-owned data products within a data mesh requires both specialized technical knowledge and careful planning to avoid disruption.
Finally, regulatory compliance remains complex. Although the federated governance model of data mesh can help embed security and privacy standards within each domain, the responsibility for defining and enforcing these controls stays with the organization. Achieving compliance requires a deep understanding of both regulatory requirements and the capabilities of the data mesh framework.
About the Author
David Eller is Group Data Product Manager at Indicium. Indicium is a highly specialized and certified pure-play data and AI services firm, setting the standard for execution in the modern data stack. Our collective intelligence, driven by structured training programs with 380+ advanced courses, ensures engagements are led by top experts with the highest certifications across dbt, Databricks, Snowflake, AWS, Google Cloud, and Astronomer. With proven expertise from 600+ engagements over seven years, we execute four times faster using best-practice accelerators and AI-driven automation.
