The data advantage: How leading funds accelerate insight, governance and performance

Disconnected systems create challenges that extend far beyond reporting inefficiencies. When portfolio companies operate on different enterprise resource planning (ERP) systems, customer relationship management (CRM) platforms or operational tools, consolidating information becomes a time-consuming and error-prone exercise. Teams often spend a lot of time and resources gathering and validating data instead of analyzing it and acting on insights. This fragmentation can lead to:

• Limited visibility into portfolio-wide performance
• Delayed responses to operational or financial issues
• Inconsistent KPIs and reporting standards
• Increased compliance and governance risks
• Reduced confidence in forecasting and planning
• Higher administrative costs driven by manual processes

Without a unified data strategy, funds may struggle to identify emerging trends, benchmark portfolio performance or uncover opportunities for operational improvement. In many cases, leadership teams are forced to make critical decisions using incomplete or outdated information. Funds that invest in clean, connected data infrastructures gain more than operational efficiency – they gain a competitive edge. By integrating data across portfolio companies and centralizing reporting environments, RIAs can establish a single source of truth that improves transparency and decision-making and enhances overall governance. A connected data ecosystem enables funds to:

• Access real-time performance insights across the portfolio
• Standardize KPIs and reporting frameworks
• Improve forecasting accuracy and scenario planning
• Strengthen compliance and audit readiness
• Reduce manual workloads and operational inefficiencies
• Enable faster identification of risks and opportunities

Perhaps most importantly, unified data provides a clearer line of sight into value creation initiatives. When leadership teams can quickly assess operational performance and profitability trends, they are better equipped to implement targeted strategies that improve margins and increase cash flow.

Modern data governance extends far beyond traditional information technology (IT) management, especially now that artificial intelligence (AI) is taking over. Governance is not simply about controlling access to databases or maintaining infrastructure – it is an enterprise-wide discipline that establishes how data is defined, managed, secured and used across the organization. Outlined below is a four-phase framework for building a scalable governance program that is designed to evolve alongside your fund and adapt to changes in technology.

• Assess: This phase focuses on understanding your firm’s current state, and evaluating existing systems, workflows and reporting structures to identify operational pain points and governance gaps. This stage will often reveal issues such as duplicate records, inconsistent reporting definitions, fragmented ownership and manual reconciliation processes that create inefficiencies across finance, operations and investor reporting functions. This is also the time to assess data maturity, compliance risks and any possible technology limitations. The assessment phase establishes a baseline from which firms can prioritize improvements and measure future progress.
• Envision: Once the current state is understood, leadership teams need to step in and define the firm’s governance vision and strategic objectives. This phase focuses on aligning governance efforts with broader business priorities such as operational scalability, investor transparency, AI readiness or portfolio performance optimization. In this phase, firms identify the outcomes they want governance to support, whether that involves accelerating reporting cycles, improving forecasting capabilities or enhancing regulatory compliance. The envision phase also helps firms establish alignment and secure buy-in across departments, which is a critical factor in long-term governance success.
• Plan: In the planning phase, organizations formalize governance structures, define responsibilities and establish operational frameworks. This includes identifying data owners, appointing data stewards and creating governance committees that are responsible for oversight and accountability. Policies surrounding data access, retention, quality standards and escalation procedures are documented and standardized. During this phase, firms also begin prioritizing technology investments and integration initiatives necessary to support overall objectives.
• Build: The final phase involves implementing governance infrastructure and operationalizing governance practices across the firm. This may include building centralized data repositories, deploying data quality monitoring tools, creating metadata inventories and establishing automated workflows and controls.

It is important to keep in mind that governance is not a one-time implementation project. Governance frameworks must remain flexible and adaptive as firms adopt new technologies and expand operations while responding to changing regulatory requirements.

Traditional data governance focuses on ensuring that organizational data is accurate, secure, standardized and well-managed throughout its lifecycle. AI governance introduces additional layers of oversight related to how models are trained, deployed and monitored. With the rise in ‘shadow AI’ use, AI governance has taken on a whole new level of importance. Shadow AI refers to employees using publicly available AI tools – often through personal accounts – without organizational approval or oversight. In many cases, employees unknowingly upload confidential financial, investor or operational information into external AI platforms that may not meet organizational security or compliance standards. While these tools may create efficiency gains, they also introduce significant governance and compliance challenges. To address these concerns, formal AI governance programs must include:

• Approved AI tools and vendor review processes
• Acceptable use policies for employees
• AI literacy and education programs
• Human oversight requirements for AI-generated outputs
• Integration of AI governance into cybersecurity and privacy frameworks
• Continuous monitoring of AI usage and risks

Without strong data governance, AI governance and the above factors become nearly impossible to execute effectively. AI systems are only as reliable as the data that supports them. Poor-quality or biased data can produce flawed outputs that directly impact investment decisions and operational processes.

As regulatory scrutiny surrounding AI continues to evolve, particularly within the financial services industry, firms that unify governance efforts across both disciplines will be better equipped to scale AI adoption while maintaining trust and transparency.

Fund administration operations are particularly vulnerable to the effects of poor data quality and fragmented reporting structures. Many firms still rely heavily on manual spreadsheets and disconnected systems to support accounting, investor reporting and operational processes. These environments often lead to the creation of duplicate records, inconsistent data definitions and reconciliation challenges that slow reporting cycles and increase operational risk.

Reporting inconsistencies and inefficient workflows can impact both internal operations and investor relationships. Teams frequently spend more time gathering and validating information than actually analyzing performance and identifying opportunities for improvement. To modernize operations, adopting connected operating models built around trusted data foundations is key. Some recommendations include:

• Standardizing reporting definitions and accounting system rules
• Integrating finance, portfolio and operational systems
• Automating reconciliation processes
• Establishing centralized reporting environments
• Defining governance ownership across functions

When fund administration functions operate from a single source of truth, firms gain greater efficiency, transparency and confidence in reporting outputs.

Tax operations represent another area where governance maturity has significant downstream impact. Tax reporting processes often rely on information sourced from multiple systems across the firm. When data is inconsistent or difficult to access, firms may face delays in producing draft and final investor reporting packages. Common challenges can include:

• Incomplete or inaccurate reporting data
• Difficulty accessing historical information
• Manual reconciliation processes
• Delays in investor processes
• Limited visibility into tax implications tied to investment activity

Improved governance practices help streamline tax reporting by ensuring data is standardized, accessible and validated throughout the reporting lifecycle. Enhanced data management can also improve tax planning and research capabilities. Firms gain better visibility into transaction structures, investment activity and portfolio-level tax exposures, enabling more proactive planning and decision-making.

Many firms are already using AI to automate administrative processes, improve efficiency and reduce operational costs. According to Baker Tilly’s 2026 Mid-Market Report, which included a survey of 500 business leaders at organizations that range from $200M-$2B in revenue, the findings on AI adoption were eye-opening:

• 76% are using AI to improve efficiency and automate back-office work
• 60% are deploying AI specifically to reduce rising overhead costs
• Only 1% of the businesses polled aren’t making any AI investments

While interest in AI remains high, successful implementation often depends less on the technology itself and more on the organization’s governance maturity. AI initiatives frequently stall when firms attempt to operate with:

• Different KPI definitions: Funds and their portfolio companies at times use inconsistent metrics, making aggregation unreliable.
• Manual reporting packs: Quarterly packs rebuilt from scratch with no governed single source of truth
• Unclear ownership: No one is accountable for the full reconciliation, exception routing, and close calendar end-to-end.
• Late tax packages: Upstream fragmentation drives LP exceptions and delays that compound each reporting cycle.

Many firms today remain in the early stages of maturity because they rely heavily on manual processes, siloed reporting and fragmented data environments. Because of this, they are often forced to operate reactively and spend a significant amount of time correcting errors, consolidating information and responding to reporting requests. As governance maturity improves, organizations begin standardizing processes, automating workflows and integrating systems, and subsequently reporting becomes faster, proactive and more consistent.

Automation is able scale successfully only when the run model defines thresholds, ownership and accountability at every step in the process. Firms that establish strong governance foundations before scaling AI initiatives are more likely to achieve sustainable operational improvements and avoid costly implementation failures. Once the highest levels of maturity are met, firms are able to leverage predictive analytics, AI-assisted insights and real-time monitoring capabilities to support strategic decision-making across the entire enterprise.