Wealth management firms face a critical data challenge: valuable information trapped in disconnected systems. The Data Lakehouse—a hybrid architecture combining data lakes and data warehouses—offers a powerful solution specifically designed for financial services.

The Wealth Management Data Problem

Wealth managers struggle with three key data challenges:

1. Disconnected Systems

• Portfolio systems (Addepar, Orion, Black Diamond)

• CRM platforms (Salesforce, WealthBox, Redtail)

• General ledger systems (Sage, QuickBooks, FundCount)

• Document management tools

These disconnected systems create incomplete client views and require manual reconciliation.

2. Diverse Data Types

• Structured: Account balances, transactions, holdings

• Unstructured: PDF statements, client emails, research

• Semi-structured: API feeds, custodian data

Traditional systems handle either structured OR unstructured data—rarely both effectively.

3. Growing Complexity

A $5B AUM firm typically manages:

• 500,000+ annual transactions

• 20,000+ PDF statements

• Multiple data formats and sources

This complexity outpaces traditional data solutions.

What Makes a Data Lakehouse Different?

The Data Lakehouse combines the best of previous architectures:

• Structure and governance of data warehouses

• Flexibility and scale of data lakes

• Support for all data types in one platform

• ACID transactions for data integrity

• Direct analytics on raw data

Why Wealth Managers Need a Data Lakehouse

1. Unified Client View

Consolidate all client data—from transactions to unstructured communications—creating a comprehensive picture for advisors.

2. Regulatory Compliance

Maintain data lineage, history, and integrity while providing audit trails for reporting.

3. Advanced Analytics

Apply AI and machine learning directly to raw data without complex transformations.

4. Cost Efficiency

Eliminate redundant systems and reduce manual data reconciliation.

Case Study: Data Transformation in Action

An $8B AUM RIA transformed their operations with a Data Lakehouse approach:

Before:

• 7 disconnected systems

• 30+ hours weekly on manual reconciliation

• 48-hour delay in performance reporting

After:

• Single unified data platform

• 4 hours weekly for reconciliation oversight

• Near real-time performance updates

• 65% reduction in technology costs

• $450,000 annual savings in staff time

Enhancing the Data Lakehouse with Agentic AI

Collation.ai's specialized AI agents maximize the value of a Data Lakehouse:

• Worker Agent: Automates data collection from systems, APIs, and files

• PDF Reader Agent: Converts unstructured documents into analyzable data

• Analytics Calculator: Performs complex financial calculations consistently

• Auditor Agent: Monitors and fixes data quality issues automatically

• Chatbot Agent: Provides natural language access for non-technical users

Implementation Roadmap

1. Data Assessment: Inventory sources and identify pain points

2. Pilot Project: Start with high-impact use cases

3. Expand Incrementally: Add data sources systematically

4. Deploy AI Agents: Automate processes and enhance analytics

The Competitive Edge

Firms adopting the Data Lakehouse approach gain significant advantages:

• More personalized client service through comprehensive data insights

• Faster investment decisions with real-time analytics

• Lower operational costs through automation

• Enhanced scalability to support growth

• Improved compliance through better data governance