Data Complexity as a Defining Challenge for OCIOs

Unique challenges of OCIO data and technology

The OCIO model introduces structural data challenges that differ meaningfully from traditional asset owners, particularly due to the need to aggregate and standardize data across multiple custodians, managers, and external providers. Unlike more centralized models, OCIOs must reconcile inconsistent data formats, reporting timelines and valuation approaches across client portfolios.

A key source of complexity lies in the distinction between accounting, custody, and investment data. Across custodians, OCIOs must track differing accounting versus custody platforms, with data ingestion occurring across multiple sources and reflecting a range of accounting and custody nuances. As a result, even foundational datasets require significant normalization before they can be used for analysis.

These challenges are both persistent and interconnected. According to the survey, 61% cite integration of data across sources as a top issue, while 57% point to data accuracy, 43% to consistency, and 41% to timeliness. Together, these issues limit the ability to generate a unified, reliable view of portfolios and create downstream friction in analytics, reporting, and client delivery.

The expansion into private markets further compounds these challenges, introducing irregular data flows and a lack of standardization across managers. In response, OCIOs are increasing their investment in data infrastructure. For example, 43% are planning to spend more on data warehouse development, compared with 35% of the average asset owner respondent, and 65% are increasing spend on portfolio analytics tools, versus 51% more broadly.

However, continued fragmentation suggests that technology investment alone has not resolved the underlying issue. Data integration remains the core constraint shaping how effectively firms can scale.

Front office data challenges

The impact of data limitations is increasingly visible in the front office, where investment teams rely on timely, high-quality information to make portfolio decisions and respond to changing market conditions. The link between data quality and investment agility is evident in survey findings: 72% cite the ability to remain nimble as investment teams evolve as a key operational challenge, a full 16 percentage points higher than the average respondent.

In practice, fragmented and incomplete datasets create barriers to effective portfolio management, particularly when trying to generate insights across multiple clients and strategies. These constraints limit the speed at which portfolios can be adjusted in response to market developments, the ability to identify risks and opportunities across aggregated exposures, and the consistency of performance and risk analytics across portfolios.

As a result, investment teams often rely on workarounds, including manual data aggregation and supplementary tools. While these approaches can provide short-term solutions, they introduce inefficiencies and additional operational risk over time since they are prone to error.

Data outsourcing model

To manage growing complexity and operational demands, OCIOs are increasingly adopting a hybrid approach to data and analytics capabilities, combining internal infrastructure with external provider support. According to the survey, current operating models reflect this balance, with 33% outsourcing performance and analytics reporting.

External providers play a significant role in delivering core data-driven services to OCIOs. When asked what services they receive from third-party providers, OCIOs’ top selections covered data-centric needs:

·       Performance measurement (54% of OCIO respondents)

·       Data feeds into other systems (39% of OCIO respondents)

·       Performance attribution (37% of OCIO respondents)

·       Risk measurement (35% of OCIO respondents)

This reliance on service providers reflects both the complexity of data management and the need for specialized capabilities that may be difficult to build internally.

At the same time, demand for enhanced support remains, particularly in areas such as enhanced risk management analytics (46%) and data support and reporting solutions (35%). These findings suggest that while outsourcing has helped alleviate some operational burden, it has not fully addressed underlying integration and usability challenges, especially when it comes to reconciling data from various external sources.

As a result, OCIOs continue to balance the benefits of scale and efficiency with the need for control, flexibility, and differentiation in how data is managed and used.

AI use by OCIOs

Against this backdrop of data complexity, AI adoption is accelerating rapidly across OCIO firms, driven by the potential to improve efficiency and enhance insight generation. Survey data indicates that adoption is already widespread: 94% of OCIOs report deploying AI in some form, with 74% using general-purpose tools such as Microsoft Copilot or ChatGPT.

OCIOs’ AI use cases with the greatest potential are primarily focused on areas where data processing and operational efficiency gains can be realized quickly. These include investment due diligence (63%), research (63%), document management and alternatives processes (59%) and operational due diligence (54%).

These applications reflect a pragmatic approach, with firms prioritizing AI in areas that support existing workflows rather than fully transforming investment decision-making. At the same time, AI is seen as both an opportunity and a challenge, with 67% of OCIOs citing “harnessing the power of AI” as a key operational issue, broadly in line with the average asset owner respondent.

Ultimately, the effectiveness of AI initiatives is dependent on the strength of the underlying data, reinforcing the need to address core data challenges before more advanced capabilities can be fully realized.

The next data phase for OCIOs

Data and technology challenges exist for all asset owners but are particularly structural within the OCIO model, reflecting the complexity of aggregating, standardizing and analyzing information across diverse client portfolios. While outsourcing and increased technology investment have improved capabilities, fragmentation and data quality issues remain key constraints on scalability and efficiency.

AI adoption is advancing quickly, offering the potential to enhance both operational processes and investment insight. However, its impact is closely tied to the maturity of underlying data infrastructure.

Firms that prioritize data integration, governance and architecture will be better positioned to scale their operating models, support front-office decision-making and deliver more consistent outcomes in an increasingly complex environment.