Are You Ready to Implement Your Next Gen Data Platform?

In an industry defined by milliseconds and margins, having a modern, unified data platform is no longer a differentiator, but a baseline requirement. Investment and financial firms are doubling down on data transformation to remain competitive, agile, and insight-driven. While intent is clear, implementation remains the hardest part. According to McKinsey, 70% of large-scale data transformations fail, often due to lack of clarity around the end-state vision, poor cross-functional alignment, and insufficient change management[1]. Organizations must evaluate whether they have the right foundation in place to support a successful implementation. 

How to ensure your transformation is a success 

Modernizing your data platform isn’t about simply swapping systems; it’s about creating an environment in which a data platform can scale and perform effectively. Modernization demands a smarter approach to how data is acquired, integrated, and shared. It involves thoughtfully aligning how data flows across your investment lifecycle, ensuring every team, from trading to reporting, works from a single source of truth. Success begins with of a firm’s target state for reporting, modeling, and integration capabilities. This includes mapping the current architecture against the future vision, identifying gaps, and ensuring each phase of the roadmap has defined outcomes. What’s equally important is having clearly defined roles and ownership models during implementation, supported by effective change management.  

Checklist: Preparing for your Data Platform Modernization 

As a vendor, one of the most common questions we hear from clients is: “How should we prepare for implementation?”. The answer starts with a clear understanding of your firm’s priorities and available resources. Defining your needs upfront rather than adapting them to a platform’s features ensures a comprehensive, objective-driven approach. This clarity accelerates execution, reduces friction during onboarding, and enables faster stakeholder alignment across business and technology teams.  

The following checklist can be used to effectively measure the implementation readiness and set your data transformation journey up for success:  

1. Strategic Alignment: Leading firms are increasingly adopting telemetry-based metrics, like “time to value” and “path to insight”, to measure user engagement with their data platforms. By breaking the implementation into structured phases, organizations can deliver value incrementally, encourage adoption, and maintain alignment across teams.

• Clearly define business objectives tied to platform implementation and align them across all stakeholder groups, including technical and non-technical teams. 
• Translate business goals by benchmarking existing platform engagement metrics, such as login frequency, session duration, and user decision-making confidence. 
• Develop a phased roadmap with milestone tracking across teams to enable targeted value delivery, early wins, and continuous feedback loops to support adoption. 
• Identify and evaluate data products with the greatest enterprise-wide value to serve as foundational drivers of transformation. Their early adoption can accelerate change, foster alignment, and shape a scalable enablement strategy across functions. 

2. Data Landscape Assessment: Industry leaders are adopting value-based models, such as data product P&L attribution, which helps them measure how much each dataset costs and how much value it brings to the business. 

• Identify all critical data domains and develop a prioritized inventory of data products with emphasis on those linked to revenue-generating use cases, such as personalized client insights, to enable clear value attribution downstream. 
• Develop a current-state infrastructure map that details all data sources, flows, and consumption points across the enterprise to establish a baseline for modernization. 
• Conduct a comprehensive gap analysis to compare existing capabilities with future-state requirements, explicitly highlighting known risk areas, historical events, control gaps, and any prior disruptions or failures that may impact transformation readiness. 

3. Data Quality and Governance: With the rise of AI and advanced analytics, and rising regulatory control, regulators and stakeholders no longer accept “black-box” systems where it’s unclear how decisions are made. Every step in the data pipeline must be transparent, traceable, and easy to audit. 

• Assess your current data quality by analyzing key metrics such as exception volumes, resolution times, and issue origination by source. While deploying advanced data platforms often uncover additional gaps, establishing a clear baseline upfront supports targeted improvements and ongoing monitoring. 
• Benchmark existing data quality KPIs, including accuracy, completeness, and consistency. These metrics will serve as reference points to demonstrate progress as validation rules and automated alerting are integrated into your data pipelines. 
• Define golden sources across key datasets, resolving duplicates and conflicting records to eliminate ambiguity and ensure data reliability at the source. 
• Consolidate all governance artifacts, including data privacy frameworks, lineage documentation, and access control protocols. 
• Understand the domain ownership model across domains and functions. Agree on future state and get a clear picture of gaps in your current stewardship processes. 
• Identify which policies require replication in the new platform and which can be deprecated. Replicable policies offer immediate implementation value, while policy retirement may require deeper analysis, something your implementation partner can help guide. 

4. Third Party Integrations: As data integration becomes more complex, modern data platforms are stepping in to help firms monitor pipeline efficiency and reliability across the investment ecosystem. Yet for many investment firms, the challenge remains significant, ~44% report that integrating data across custodians, fund administrators, and internal systems is still their biggest hurdle².

• List all systems your platform needs to connect with such as CRMs, order and portfolio management systems (OMS/PMS), and fund admin feeds. Note how each one shares data via APIs, files, messaging, or data-sharing tools. 
• Classify all incoming datasets as structured, semi-structured, or unstructured to inform downstream ingestion, transformation, and normalization processes. 
• Establish clear lines of communication with all third-party vendors, system providers, and integration partners. Ingestion pathways from custodians, counterparties, and fund administrators should be prioritized early to ensure scalable, production-ready data operations from day one. 
• Develop a finalized list of data sources to be onboarded during the implementation phase. Scope clarity at the outset is essential, as modifying the list midstream can significantly disrupt timelines and resource planning. As your new platform introduces more efficient onboarding workflows and self-service capabilities, consider staging lower-priority or exploratory sources as part of a structured Day 2 plan. 

5. Organizational Readiness: Operational success hinges on strong cross-functional ownership and proactive change management throughout the implementation lifecycle. 

• Create a dedicated implementation team with clear representation from both business and technical functions. While resource allocation may be challenging, early alignment and sustained involvement are critical to program success. 
• Designate functional stakeholders for gathering key information, user acceptance testing, and day-to-day decision-making. These roles often differ from executive sponsors and should be aligned with operational users closest to the workflows being transformed. 
• Develop a comprehensive change management and communication plan, tailored by stakeholder group and implementation phase to ensure transparency, engagement, and minimal disruption during execution. 

What Transformation Success Looks  

A successfully modernized data platform transforms how your firm operates from the front office to compliance. Investment teams gain intra-day portfolio and P&L insights with minimal operational support. Client reporting becomes faster and more personalized, risk teams access real-time exposures, and IT reduces infrastructure costs while scaling efficiently. 

Arcesium's cloud-native platform, Aquata®, is purpose-built for the investment industry, helping ­financial institutions make critical decisions from a synchronized source of data. It offers low-code data integration, advanced analytics, and exceptional visualization tools to help investment managers and financial firms unlock the value of their data. With robust connectivity to disparate financial data sources, a powerful data quality framework, and rich metadata models, Aquata builds trust in data, enhances governance, and clears the path for deeper insights, streamlined deal structuring, and improved client engagement.  

Our clients don’t navigate this journey alone. Arcesium’s implementation team brings deep domain expertise, proven methodologies, and a collaborative approach to every engagement, ensuring transformation goals are achieved with speed, precision, and lasting impact.  

This isn’t just transformation, it’s how tomorrow’s leaders compete. 

Sources: 

[1] Common pitfalls in transformations: A conversation with Jon Garcia (McKinsey & Co.) 

[2] New Benchmarking Survey on Data Modernization from BetaNXT Reveals Growing Investment and Competing Priorities at Data-Minded Financial Services Firms (PR Newswire)