In the ever-evolving landscape of wealth management, data reigns supreme. It serves as the bedrock for informed decision-making, risk management, and delivering personalized services. However, amidst the rush to leverage data effectively, many wealth management firms face a common obstacle: data fragmentation.
Imagine valuable information scattered across disparate applications, in discrete silos, some duplicated, and difficult to fully harness. This fragmentation not only hampers a comprehensive understanding of business performance, client needs and market trends but impedes decision-making while also raising security concerns.
Enter the solution: a data integration layer that combines the strengths of a data warehouse with a data lake. This dynamic duo is poised to become table stakes for firms aiming to stay competitive in the near future.
An Integrated Data Hub: The Data Lakehouse
Think of this new data integration layer as the heart of a company’s data strategy—an amalgamation of a structured data warehouse and a flexible data lake.
Data Warehouse: This organized repository stores structured data, providing easy access to transaction histories, client profiles, and portfolio data. It's the gateway to historical analysis and performance assessment.
Data Lake: A versatile pool capable of handling structured and unstructured data alike. It accommodates big data, including market feeds and social media sentiment analysis, empowering advanced analytics and machine learning for predictive analysis and risk management.
IBM recently christened this merged entity to be referred to as a data lakehouse:
“A data lakehouse is a data platform, which merges the best aspects of data warehouses and data lakes into one data management solution. Data warehouses tend to be more performant than data lakes, but they can be more expensive and limited in their ability to scale. A data lakehouse attempts to solve for this by leveraging cloud object storage to store a broader range of data types—that is, structured data, unstructured data and semi-structured data. By bringing these benefits under one data architecture, data teams can accelerate their data processing as they no longer need to straddle two disparate data systems to complete and scale more advanced analytics, such as machine learning.”
A data lakehouse incorporates similar data structures as data warehouses but pairs it with the low storage costs and flexibility of data lakes. This enables organizations to store and access big data quickly and more efficiently while also allowing them to mitigate potential data quality issues.
Data lakehouses can support diverse data datasets since they include both structured and unstructured data, which can meet the needs of business intelligence and data science workstreams. It typically supports programming languages like Python, R, and high performance SQL.
Data lakehouses also support the key properties that define a transaction to ensure data integrity including consistency and persistence even in the event of a system failure.
The Competitive Edge
Why does an integration layer based on a data lakehouse matter? Three reasons:
- Eliminates Data Silos: Say goodbye to fragmented data. A unified view across operations will help streamlines processes and enhance decision-making.
- Provides real-time Insights: With agile access to data and analysis, you can make informed decisions swiftly—a critical advantage in the fast-paced financial world.
- Enhances Security and Compliance: Centralized control improves data integrity and compliance with regulatory standards, mitigating risks and bolstering trust.
The Bottom Line
In a data-driven era, ownership and mastery of data are paramount. Establishing a robust data integration layer isn't just a strategic move—it's a necessity for survival and success in wealth management. By embracing this paradigm shift, firms can offer superior client experiences, derive deeper insights, and adapt nimbly to market dynamics. The time to act is now—the future of wealth management belongs to those who master their data.
Read more about this topic in this article penned by Softlab360 CEO Henry Zelikovsky published in the Wealth Management 2024 Market Outlook.
