3 Ways to Store and Use Client Data

The main advantage of data lakes is their flexibility and scalability. They can accommodate any data type and grow cost-effectively. Wealth management firms can maintain comprehensive client profiles and market intelligence. Data lakes excel at storing alternative data sources like social media sentiment, investment metrics, and real-time market feeds that can inform investment decisions. They're particularly valuable for machine learning projects that require access to large amounts of historical data.

However, data lakes come with challenges. Without proper governance, they can become "data swamps" where information is difficult to find and use. Without proper controls, they can become difficult to navigate, potentially impacting advisor productivity and client service quality. They also require skilled data scientists who can work with raw, unstructured data.

Data Warehouses: The Structured Investment Analysis Platform

A data warehouse is like a well-organized library where data is cleaned, validated, transformed and structured before storage. It's designed for specific reporting needs like portfolio analysis, risk management, and regulatory filings.

Consider how UBS utilizes its data warehouse to analyze client portfolios across global markets. Their structured approach allows wealth advisors to generate standardized performance reports, conduct risk assessments, and ensure regulatory compliance efficiently.

The primary benefit of data warehouses is their ability to deliver fast, reliable analytics for business intelligence. They excel at answering predefined business questions and supporting regular reporting needs. The primary benefit for wealth management firms is the ability to deliver consistent, accurate reporting for both clients and regulators. Data warehouses excel at supporting regular investment analysis, compliance reporting, and client portfolio reviews. Their structured nature makes them particularly suitable for relationship managers who need quick access to verified client information.

The main drawbacks are their rigid structure and higher storage costs. Adding new data sources or modifying existing reporting structures can be time-consuming, potentially impacting your firm's ability to adapt to new market opportunities or regulatory requirements. They're also less suitable for exploratory analysis and machine learning applications that require access to raw data.

Data Lakehouses: The Modern Wealth Management Platform

A data lakehouse is a modern hybrid architecture that combines the flexibility of data lakes with the management features of data warehouses. It's like having a smart storage system that can handle both raw and refined data while maintaining performance and governance. Wealth management firms can handle both detailed client records and standardized reporting while maintaining security and compliance.

Databricks, a leading technology company, uses this architecture to serve diverse needs across their organization. Data scientists can access raw data for machine learning while business analysts can query structured datasets for reporting, all within the same platform.

Addepar’s Addepar Data Exchange (ADX) and BlackRock's Aladdin platform exemplify this approach, enabling firms to manage both structured portfolio data and unstructured market intelligence while maintaining strict security and governance standards.

The key advantage of data lakehouses is their versatility. They support both traditional business intelligence and modern machine learning workloads. They also provide better data governance and quality controls compared to pure data lakes, while maintaining more flexibility than traditional data warehouses. Data lakehouses provide robust security and compliance controls while maintaining the flexibility to incorporate new data sources and analytical approaches.

The main challenge with data lakehouses is their relative novelty. This technology is still evolving, and implementing a data lakehouse requires significant expertise. While the potential benefits are significant, implementing a data lakehouse requires careful planning and consideration of your firm's specific needs. They also typically require investment in modern data platforms and tools.

Making the Right Choice for Your Firm

Your choice should align with your firm's strategic priorities.

If your focus is on building comprehensive client profiles and incorporating alternative data sources for investment decisions, a data lake might be optimal. If your priority is standardized reporting and regulatory compliance, a data warehouse could be more appropriate. If you're looking to combine traditional wealth management services with advanced analytics while maintaining strict governance, a data lakehouse might be the ideal solution.

Remember that these architectures aren't mutually exclusive. Wirehouses maintain multiple data platforms to serve different needs. The key is understanding your specific requirements—whether that's improving client service, enhancing investment decisions, or streamlining compliance—and choosing the architecture that best supports your firm's growth objectives while maintaining the highest standards of security and compliance.

If you’re a modest sized firm who wants to maximize your data, then a data lakehouse may be the best solution for you that combines flexibility with the ability to apply machine learning in the future.

John O’Connell is founder and CEO of The Oasis Group, a leading consultancy for the wealth management industry that specializes in helping wealth management and technology firms solve their most complex challenges.