A CIO’s blueprint for modernizing core data infrastructure in asset management
MAY. 6, 2025
7 Min Read
An actionable roadmap for wealth management CIOs to integrate data silos, adopt cloud platforms, institute governance, and build an AI-ready foundation with measurable business value.
Modernizing data infrastructure is critical for wealth and asset management CIOs to deliver real-time insights, superior client service, and measurable returns.
Yet many firms remain bogged down by siloed legacy systems that prevent a unified view across the business. With limited budgets (often under 20% of IT spend for new tech), every modernization dollar must count. 94% of firms now rank data modernization as a top priority. A structured roadmap ties technology upgrades to clear business outcomes: integrating data across silos for a single source of truth, migrating to cloud platforms for agility, enabling real-time analytics for faster decisions, and embedding governance from the outset to reduce risk. The right phased approach lets CIOs score quick wins that demonstrate ROI, secure buy-in, and lay the groundwork for AI and advanced analytics.
Key Takeaways
- 1. Start with a clear assessment of data silos and pain points. Knowing where legacy gaps exist helps target modernization efforts for maximum impact.
- 2. Use a phased strategy to achieve quick wins. Implement changes in stages (like integrating key systems first) to show immediate value and build support for further projects.
- 3. Leverage cloud and real-time data integration. Migrating to a cloud data platform and enabling real-time data flows greatly improve scalability, speed, and insights for decision-making.
- 4. Embed data governance from the beginning. Strong data quality controls and compliance measures ensure the new data platform is trusted internally and meets regulatory standards.
- 5. Measure and communicate ROI at each step. Track metrics such as time saved, cost reductions, and revenue gains after each phase to demonstrate business value and maintain executive buy-in.
A candid assessment of the data landscape is the first step
An honest review of the current data environment often reveals scattered, siloed systems and hidden inefficiencies. Wealth and asset management firms typically have applications for portfolios, customer records, trading, and more that don’t communicate with each other. This fragmentation means no single source of truth for client or investment data. Leaders widely acknowledge this hurdle: 54% of financial executives say data silos are a major barrier to innovation. Advisors piece together information from multiple platforms, working with stale or inconsistent data and missing opportunities for timely insights.
Legacy on-premises systems further compound the problem, since aging servers cannot scale or deliver information on demand. This lack of agility hurts responsiveness when clients and regulators expect instant answers.
A phased modernization strategy delivers quick wins and builds an AI-ready foundation
Rather than attempting an overnight overhaul, successful CIOs take a phased approach. Breaking the effort into manageable projects that each deliver tangible improvements lets IT leaders show progress quickly. These wins build confidence and deliver immediate value.
Equally important, phasing the work lays the groundwork for long-term capabilities. Early integration and cloud migration efforts deliver quick wins (advisors who adopted integrated tools saw a 74% jump in AUM and 77% higher client retention), while later phases ensure the data platform can support advanced analytics. A phased strategy fixes pressing issues now while building a scalable foundation for future initiatives. When it’s time to deploy machine learning or other AI, you have high-quality data and infrastructure in place, reaffirming that there’s no AI without good data.
Robust data governance ensures quality, compliance, and trust
Establishing strong data governance from the start addresses three critical areas: data quality, regulatory compliance, and internal trust.
Ensure data quality from day one
Modernization must resolve data quality problems at the source. As systems are integrated and migrated, enforcing standards and cleansing data prevents bad information from polluting new systems. Poor data quality already costs financial firms around $15 million per year, and 66% of banks struggle with data integrity issues. Embedding validation and cleanup in each phase helps CIOs ensure analytics and reports are built on accurate, complete information. This commitment to quality reduces errors and sets the stage for reliable AI outcomes later.
Meet compliance requirements with confidence
Every modernization step should include controls to protect privacy and meet regulatory requirements. Enforcing data access rules, audit trails, and retention policies in the new platform design helps firms avoid regulatory violations and fines. Strong governance gives regulators and internal auditors confidence.
Build internal trust in data
The greatest payoff of governance is the trust it creates inside the organization. When employees know that data definitions are consistent and dashboards draw from a single source of truth, they are more likely to trust and use the insights. This trust accelerates decision-making, since teams spend less time questioning data and more time acting on it. Clear data ownership and transparent data catalogs further foster confidence—people understand where information comes from and who is responsible for its accuracy. Over time, a culture of trust means new analytics tools or AI recommendations will be embraced rather than met with skepticism.
Step-by-step roadmap to data modernization in wealth management
Modernization only delivers lasting value when it’s tied to specific business needs and executed with precision. For CIOs in wealth and asset management, success depends on translating strategic priorities, like real-time decision support, operational efficiency, and regulatory readiness, into concrete, measurable outcomes. Rather than chasing a future-state ideal, leaders must build a platform that evolves in sync with the firm’s goals, capabilities, and risk appetite. What matters most isn’t speed or scale alone, it’s sequencing each action so it drives results that justify the next investment.
Step 1: Assess current data and define goals
The first step is to take an honest look at your current data estate. This involves auditing all data sources across the organization, from portfolio systems to CRM platforms and operational databases. Assess where data resides, the quality of that data, and the specific silos that hinder its use across departments. While this phase may seem tedious, it is invaluable for creating a clear starting point.
Once you understand where data lives and its condition, set concrete goals that align with business priorities. For instance, one goal might be improving the accuracy of client insights, while another might focus on reducing reporting cycles. Establish key performance indicators (KPIs) to track progress. Clear goals help define the metrics of success that will guide you through each phase and demonstrate the business value of the initiative.
Step 2: Consolidate and integrate critical data
After identifying data silos, the next logical step is to consolidate and integrate critical data across platforms. This phase is essential for breaking down silos and creating a unified view of client and investment information. A fragmented data estate can lead to inconsistent or duplicate data, which ultimately undermines decision-making.
To address this, establish a single source of truth by integrating key data systems. This might involve connecting portfolio management platforms, CRM systems, and other key applications through APIs or data lakes. At this stage, data cleansing becomes a priority, ensuring that the integrated data is accurate, up-to-date, and consistent. Consolidating data not only makes information more accessible but also streamlines workflows, reduces redundancy, and enhances the reliability of insights.
Step 3: Migrate to a scalable cloud platform
Once your data is consolidated and integrated, it’s time to move to the cloud. Legacy on-premises systems often limit scalability and flexibility, which is why many firms are turning to cloud platforms to enable rapid growth and advanced analytics. Cloud-native data lakes or warehouses provide the scalability necessary to accommodate the growing volume of data and evolving business needs.
Migration to the cloud also offers a host of operational benefits, including cost savings, faster data processing, and enhanced performance. Cloud infrastructure also supports the implementation of advanced technologies like machine learning and AI. Moving to the cloud sets the stage for future innovation by providing a flexible, high-performance foundation that can scale as your firm’s data and technology needs evolve. This phase is critical for future-proofing your data strategy.
Step 4: Implement data governance and security
With the migration to the cloud underway, it’s essential to implement robust data governance and security measures. This is not a step to be overlooked, as data security is a priority for both compliance and business continuity. Assign data ownership to specific teams or individuals and establish clear data standards to ensure consistency and quality across the organization.
Simultaneously, enforce security protocols such as access controls, encryption, and audit logs to safeguard sensitive information. This layer of governance ensures that only authorized individuals have access to critical data, reducing the risk of breaches. Embedding data governance from the outset also supports compliance with regulatory requirements, such as GDPR or SEC regulations, and establishes a framework for continued oversight as the platform evolves.
Step 5: Enable real-time insights and AI innovation
With a unified, governed, and scalable data platform in place, the next step is to enable real-time insights. Real-time data streaming allows you to update dashboards and analytics tools instantly, providing the business with the most current information available. This is particularly beneficial for portfolio managers and advisors who need to make quick decisions based on the latest data.
The integration of real-time insights also lays the foundation for AI and machine learning applications. With trusted, high-quality data now readily available, you can begin to experiment with AI-driven analytics to unlock new revenue opportunities or improve client service. For example, machine learning algorithms might be used to predict market trends or optimize portfolio allocations, giving your firm a competitive edge. This step marks the transition from foundational modernization to advanced innovation, where data becomes a strategic asset driving business growth.
Progressing through these phases with clarity and purpose
Each advancement should reinforce the next, not only in technical terms but in strategic credibility. Stakeholders won’t support modernization for its own sake; they expect proof that technology delivers business value. Whether that’s faster advisor response times, reduced operational overhead, or readiness for AI-driven insights, outcomes must be communicated in language the board and front office understand. What earns buy-in isn’t a vision of what data could do—it’s evidence of what it already is doing to move the business forward.
With the right roadmap in place, CIOs can manage risk, maximize ROI, and position their firms for future AI and analytics advancements.
Measure ROI at each phase to demonstrate value and maintain momentum
For each phase of the roadmap, IT leaders should define clear metrics up front and then measure the results rigorously. This approach allows the team to objectively confirm the impact after implementation. Quantifying outcomes, say a process cut from ten hours to one, or a significant reduction in costs, turns anecdotal wins into hard evidence.
Tracking ROI at every stage also helps sustain executive support. When stakeholders see tangible results, they are more likely to back further investments. Regularly communicating these outcomes in business terms keeps leadership supportive.
How Lumenalta guides CIOs from vision to execution
Our team helps CIOs sustain momentum from strategy to execution by bringing deep expertise in cloud, data architecture, and AI, plus an agile approach focused on quick results. We work side by side with in-house teams to implement each phase efficiently while upholding governance and security requirements. We also ensure every improvement is tied to a clear business outcome—whether cutting costs, accelerating time-to-market, or opening new revenue streams—so technology investments translate into real ROI.
This partnership model reduces risk. Delivering solutions in quick, iterative increments means stakeholders see progress early and gain confidence. And because robust governance is baked into each step, compliance and data integrity are assured without slowing innovation. With the right partner at the helm, CIOs can modernize core data infrastructure, knowing that every phase will yield tangible business results and position the firm for continuous innovation.
Table of contents
- A candid assessment of the data estate is the first step
- A phased modernization strategy delivers quick wins and builds an AI-ready foundation
- Robust data governance ensures quality, compliance, and trust
- Step-by-step roadmap to data modernization in wealth management
- Measure ROI at each phase to demonstrate value and maintain momentum
- How Lumenalta guides CIOs from vision to execution
- Common questions about data modernization in wealth & asset management
How do I start modernizing data infrastructure in wealth management with a limited budget?
What are the key steps to integrate siloed data systems in my asset management firm?
How does data governance improve a wealth management firm’s data strategy?
How can I measure the ROI of data modernization projects?
Why use a phased approach for data modernization in wealth management?
Follow a clear roadmap and unlock real-time insights, scalable growth, and measurable ROI
