Why Private Assets Are Forcing a Rethink of Data Operating Models

As continued fee compression in public markets continues to squeeze margins, investment management firms have over several years increased allocations to private assets—including real estate, infrastructure, private equity, and private debt—in pursuit of the "illiquidity premium."

However, this new balance in investment strategy requires a fundamental data transformation and a rethink of how data is captured, managed and used across the organisation.

Unlike public markets, where standardised identifiers and data feeds underpin most processes, private assets are notorious for "messy" data—manually maintained, unstructured and fragmented, and often buried in PDFs and spreadsheets. As firms scale their exposure, these limitations quickly become operational constraints.

Why Total Portfolio View (TPV) is the focal point for transformation

Data from our most recent Transformation Survey highlights a clear industry goal: Total Portfolio View (TPV). Firms are working to bring together public and private holdings into a unified, cross-asset view that supports investment, risk and reporting decisions.

• 78% of firms managing private assets are planning improvements to their TPV by 2027.
• Key transformation activity, across all AUM sizes, is focused on system deployment, replacement of legacy systems, and enhancement of data integration.
• Indication that barriers to progress are primarily related to cost of solutions. 

How TPV demand is shaping the vendor landscape—and why the challenge remains internal 

In response, we have seen the vendor landscape evolve to support this demand, with a number of providers—including BNY, BlackRock, Clearwater, Northern Trust, SimCorp, and State Street—introducing or enhancing “whole portfolio” or TPV-aligned solutions. This reflects both the scale of demand and the strategic importance of this capability.

However, while these platforms are helping to shape the target state, they do not remove the underlying complexity. Buy-side firms are still required to:

• Rationalise fragmented data sources
• Define consistent data models across asset classes
• Align operating models to support ongoing governance

This is reflected in how transformation programmes are progressing in reality.

What is interesting is that this tension exists alongside a clear articulation of the end goal. Firms know they are working towards TPV, yet many are still unable to translate that ambition into a defined delivery pathway.

In other words, technology may provide part of the answer, but TPV is equally a business and data problem rather than a purely platform-led solution. Across the buy-side, the challenge rarely lies in the availability of solutions, but in defining the data model and operating framework that can consistently source, standardise and govern data across asset classes.

What this means for data transformation

The increasing allocation to private assets is reshaping how data operating models need to function beyond the TPV. Rather than a linear flow from source to report, firms are having to manage multiple parallel data lifecycles, each with different levels of structure, frequency and ownership across asset classes.

Firms need capabilities that can handle the scale, complexity and regulatory scrutiny associated with private markets.

• Granular portfolio visibility
  Unlike public assets, private asset valuation depends on underlying operational data—for instance the occupancy rate of a logistics hub or the EBITDA of a portfolio company—which must be captured, standardised and governed consistently across the portfolio.
• Regulatory transparency
  Requirements such as SFDR in the EEA are increasing both the volume and precision of data firms must produce, often requiring a single dataset to support multiple regulatory outputs across jurisdictions.
• Timeliness of data
  Investor expectations are shifting away from quarterly reporting cycles towards more frequent, decision-ready insights, placing pressure on firms to move beyond manual processing and toward more scalable data pipelines.

Where we've delivered this

Programme execution related to private market data transformation typically hinges on how firms translate strategy into a working data operating model. This is where delivery experience becomes critical.

Read our client story: Asset Owner Redesigns Data Operating Model to Support Private and Public Markets

In this case, Citisoft partnered with a £40bn AUM asset owner to address a fragmented data landscape—driven by multiple systems, manual workarounds, and spreadsheet reliance—by redesigning the data operating model to create a more integrated, governed foundation supporting both private and public markets.  

Strategies for success

Transformation is most effective when it is anchored in business outcomes rather than technical delivery. The following approaches consistently underpin successful programmes:

lead with the business problem

Start with a clearly defined outcome—such as reducing onboarding time or improving portfolio visibility—rather than initiating a technology-led programme. For instance, you will gain more traction by expressing that “we need to reduce the time it takes to onboard a new private debt fund from four weeks to two days," rather than pitching for a data warehouse programme.

Use regulatory pressure to accelerate change 

Regulatory requirements create a non-negotiable need for improved data quality and transparency. In EMEA, regulations such as SDFR and DORA are key drivers for mandating change.

design for distributed data ownership

Centralised models often struggle to accommodate the realities of private market data. In regions like APAC, where data residency laws vary by country, a centralised "mothership" database often fails. Instead, consider using a data mesh approach where local teams own their data but follow global governance standards.

industrialise data governance 

Manual governance does not scale alongside private market growth. Don’t go and hire 50 more data stewards—instead, think about how tooling can introduce scale into the operation whether that is scanning incoming valuation reports to automatically flagging anomalies or missing fields.

You cannot manage what you cannot measure. As private assets continue to drive growth across the industry, data operations must move from a cost centre to a core part of how firms scale.

Firms that make this shift will be better placed to support more complex portfolios, meet rising expectations, and deliver insight that informs decisions rather than simply reporting on them.