Financial institutions invest billions in data governance, infrastructure modernization, and analytics. Yet many find themselves trapped in a vicious cycle – a data doom loop – where complexity increases, inefficiencies multiply, and trust in data erodes.
The symptoms are everywhere:
- Regulatory reporting struggles: Despite massive compliance investments, financial firms still grapple with inaccurate reports and audit headaches.
- Data overload, but limited usability: Trading desks, risk systems, and compliance teams generate endless data, yet accessing reliable, timely insights remains a challenge.
- Failed centralization efforts: Institutions chase the next "silver bullet" architecture – Hadoop, data lakes, now lakehouses – only to find they don’t solve real-world challenges.
This isn’t just a technical problem – it’s an organizational and strategic failure in how financial institutions approach data management.
Why traditional approaches keep failing
Data governance became a compliance checkbox
Banks and investment firms have built extensive Chief Data Officer (CDO) functions, implemented stringent governance policies, and invested heavily in regulatory compliance. Yet, they continue to struggle with:
- Fragmented data ownership
- Poor data quality
- Slow and inefficient regulatory reporting
Governance should enable data-driven decision-making, not act as a bureaucratic burden.
Overpromise of lakehouse architectures
Modern data lakehouses improve upon Hadoop’s limitations, offering better compute-storage separation and performance. However, core flaws remain as they:
- Are optimized for batch analytics, not real-time trading or regulatory reporting
- Attempt to centralize all enterprise data, ignoring the reality that financial institutions require specialized systems (e.g., time series databases for trading, graph databases for risk relationships)
Blind pursuit of data mesh
The concept of data mesh – treating data as a product owned by business domains – had promise but adoption stalled because it was treated as a big bang transformation rather than an incremental shift.
It created unrealistic expectations on business teams to manage data without clear implementation models. Vendors misappropriated the term, further muddying its original intent.
According to Gartner, “... by 2025, 80% of data mesh early adopters will fail to meet their planned SLAs around data engineering productivity, augmentation and federated governance.”
The lesson? Technology alone won’t fix financial data challenges. Institutions need a strategic shift in how they govern, access, and use data.
Breaking free: A new model for financial data management
Financial firms that successfully escape the data doom loop are shifting toward a pragmatic, business-driven approach focused on three key elements:
- Data products: Business-defined, well-maintained datasets designed for specific use cases (e.g., risk reporting, regulatory compliance)
- Data contracts: Standardized agreements that define data relationships, quality expectations, and governance requirements.
- A unified access layer: A flexible, governance-embedded interface that enables compliant, efficient access to data without unnecessary bottlenecks.
The discerning reader will recognize overlaps with data mesh principles. While data mesh as a whole didn’t fully take off, its foundational ideas still hold significant potential for reshaping data management practices.
The key to success lies in adopting incremental approaches rather than dramatic, all-at-once shifts. This method embraces complexity rather than attempting to eliminate it, ensuring that financial institutions can effectively manage their vast and diverse data ecosystems.
What’s next?
In Part 2 of this series, we’ll explore how financial institutions can implement data products and data contracts to bring structure, governance, and usability to their data. Stay tuned.
