Data Warehouses: From Sci-Fi Dream to Wealth-Management Reality

As with most new ideas and technology trends, the truth is often stranger and much more complicated. So, let’s crack open the book and break down exactly how data warehouses work and the implications they have for the wealth management industry.

Centralization: Easier said than done

The vision of a single, unified data source is alluring, but centralizing advisor data is far from easy and has eluded the profession for decades. Tech advances have mostly led to more new tools and increasing integration issues. The complexity lies in normalization, which is the process of transforming data from diverse vendors into a consistent format.

Several important issues have to be dealt with in this step. As vendors push to differentiate themselves, they create a wide range of idiosyncrasies. While this does allow them some unique functionality, on the underside it means that each custodian, portfolio management system, and CRM has its own data structure and quirks.

This complexity is further exacerbated by the varying quality of data, including issues with accuracy, completeness, and timeliness, which can significantly impact the usefulness of the centralized data repository. Managing the disparate formats not only requires carefully applied expertise to do it correctly, but ongoing maintenance afterwards.

On top of that, historical baggage from legacy systems is packed with inconsistent data that needs to be cleaned and standardized, and all financial data comes with intricate compliance requirements that require the data warehouse to have robust security and audit trails to ensure adherence.

Data lakes versus data warehouses

The terms “data lake” and “data warehouse” are often used interchangeably, but they refer to fundamentally different systems. While they both store large quantities of data, the way they each hold and organize that data is unique.

Think of a data warehouse like a well-lit library: structured and organized, with data pre-processed and ready for analysis. They’re ideal for historical analysis, reporting, and compliance. Machine learning comes into play with a data warehouse, specifically because it has structured data. Softlab360, the software engineering and development company run by Henry Zelikovsky, has been using machine learning, applying its technology to derive meaningful insights, and building data warehouses for over 10 years.

On the other hand, a data lake is more like a vast wilderness – variable and unstructured. Vast amounts of data are stored in a raw format without immediate schema restrictions, making them a great tool for exploring emerging trends and insights but requiring additional processing before analysis. Data lakes are ideal for firms exploring big data analytics, AI, and machine learning to glean insights and drive innovation.

For wealth management firms, data warehouses are the natural choice as the structured nature aligns well with regulatory requirements and reporting needs. But incorporating elements of a data lake can provide additional future-proofing qualities while unlocking hidden gems within the data.

The promises of well-used data

Despite these complications, the strategic importance of data management in the advisory profession cannot be overstated. In fact, the challenges of normalization and integration go a long way to proving why these solutions are necessary for firms to run smoothly and efficiently.

These platforms not only simplify data access and analysis but enable firms to develop customized apps and tools that provide tailored services and insights to their clients. This level of customization can have a strong effect on client satisfaction and loyalty, offering a competitive edge in a crowded market.

For advisors, the advent of data warehousing in wealth management signals a shift towards greater data sovereignty.

For too long, firms have been beholden to their software providers for access to their own data, especially when transitioning between systems. A centralized data warehouse eliminates this dependency, empowering firms with direct control over their data and facilitating smoother transitions and integrations between different software solutions.

By democratizing access to data, the hope is that wealth management firms can foster innovation, developing new services and solutions that meet the evolving needs of their clients. This environment encourages a culture of innovation, reminiscent of the early days of fintech when many groundbreaking solutions were developed by tech-savvy advisors who understood the unique needs of the sector.

Now that’s exciting!

From dream to reality: A measured approach

While data warehouses hold immense potential, let's be realistic: They’re not a silver bullet. Implementing and maintaining a robust data warehouse requires significant investment, expertise, and a data-driven culture within the firm.

For wealth management firms looking to navigate this evolving landscape, it’s best to start small and focus on essential data points from core systems. Don’t try to boil the ocean (or lake)! Prioritize data quality by investing in data cleansing and standardization before putting it into your systems. If you put garbage in, you’ll get garbage out.

Go slowly and evaluate your needs and resources carefully before selecting a partner for your data warehouse platform.

No matter how good your tools and data are, they’re only worth as much as they’re utilized. Spend the time educating and empowering your team to understand the systems and leverage them to make better decisions across the company.

Meet the main characters

Despite the complexity of building or working with a data warehouse, they’re a hot category in the fintech landscape – even Amazon jumped in with its Redshift platform to compete with Snowflake. As the two biggest players, many firms are looking between Redshift and Snowflake as their platform choice, and each comes with its own strengths and weaknesses.

Amazon Redshift is renowned for its integration with other AWS services, making it an attractive option for firms already vested in the AWS ecosystem. It offers a cost-effective solution with the capability to scale computing and storage resources independently.

Snowflake stands out for its architecture that separates computing and storage, allowing users to scale them independently without impacting performance. This feature enables more efficient resource management and cost optimization. Snowflake also offers advanced data sharing capabilities, making it easier for firms to collaborate and share data securely with partners and clients.

In the wealth management realm, companies such as Envestnet and Orion are leading the way as data giants. Envestnet offers a ready-to-use data storage platform that comes prepackaged with data models. Orion has partnered with Amazon to move all its data into Redshift to enable clients to easily access anything they need. These platforms are not just theoretical constructs but are backed by significant industry players, signaling a serious shift towards data ownership and interoperability within the wealth management sector.

Writing the future together

While data warehouses offer one path toward fintech integration, let’s not forget about wider collaborative initiatives that are working to make our profession more connected. Open APIs and data portability initiatives like FDC3 can facilitate secure data exchange between systems, reducing reliance on centralized warehouses.

The future may not be one giant data repository for all, but rather a network of interconnected systems seamlessly sharing data for the benefit of advisors and clients. In fact, this is our preferred outcome. This collaborative approach fosters innovation, competition, and ultimately, a more dynamic and client-centric wealth management landscape.

Through data warehouses or otherwise, wealthtech is due for a revolution that takes data from a source of frustration to one of opportunity and empowerment. But the journey requires a clear-eyed understanding of the challenges and a measured approach that prioritizes data quality, collaboration, and a culture that embraces the power of information.

Will data warehouses lead us out of a data dystopia? Not by themselves, but they’re a pretty good start.

Henry Zelikovsky co-founded Softlab360 in 2000 as its chief technology officer. The company merged with Starpoint Solutions in 2002, where it operated as a software engineering division until 2021, at which time it became an independent entity, Softlab360 (www.Softlab360.com). Zelikovsky has an extensive background in software engineering and development, being a senior consultant at Credit Suisse in the ‘90s, and an analyst and software developer at BNY/Mellon and Merrill Lynch before founding his first company, SmartLink. He leads Softab360 in the direction of enterprise custom software solutions, and integrations with cloud-platform products running in any operating environment. He has a B.S. in computer science from Brooklyn College and an M.S. in computer science and software engineering from The New York University – Polytechnic School of Engineering.

Craig Iskowitz is a business and technology strategy consultant who is a recognized expert on fee-based advisory platforms and wealth management technology. He is the CEO and founder of Ezra Group (www.EzraGroup.com), a strategy consulting firm providing technology and operational advice to RIAs, broker-dealers, asset managers, and private equity firms. He is also the publisher of the WealthTech Today blog and podcast and is a sought-after speaker for conferences and industry events. Iskowitz has a B.S. in computer science from Rutgers University.

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