As allocators explore adopting a total portfolio investment approach, service providers are competing to show institutional investors and asset managers that their products can offer the necessary speed and precision needed for the real-time decision making to implement and operate using a total portfolio view.
“People are really attracted to the idea of a total portfolio approach, so more and more, we’re having conversations about it and how to bridge the gap from the theory to the implementation,” says Christopher Carrano, vice president at portfolio analytics provider Venn.
Adoption of the TPA investment concept has begun in the U.S. with large public pension funds, such as the $600 billion California Public Employees’ Retirement System under CIO Stephen Gilmore, who arrived in 2024 after holding senior investment positions at both Australia’s Future Fund and the New Zealand Superannuation Fund. Those global investors and others, such as Canada’s CPP Investments and Singapore’s GIC, have long moved to the total portfolio approach from a strategic asset allocation.
The change is big. Proponents describe TPA as a more dynamic, flexible type of management and decisionmaking that considers a broader range of factors, a move away from the more static, top-down investment approach that has dominated institutional portfolios for decades.
“TPA involves active, short- to medium-term shifts based on market conditions, which require constant monitoring, skill, and often, higher costs,” WTW stated in its 2025 report. “For many investors, especially those seeking to navigate uncertainty and capture short- to medium-term opportunities, TPA can be complementary and in some cases, a more effective core strategy than traditional SAA.”
The 21st century’s expansion of computing power and fintech innovations are both needed to make the real-time portfolio decisions involved in TPA.
“TPA is not a new concept and has been adopted by large sovereign wealth funds for years,” says Raphaelle Granger, global head of asset owner product strategy at Northern Trust Asset Servicing. “Historically, TPA has been supported by in-house teams. Widespread adoption of total portfolio approach will hasten a fundamental shift already underway in how the data [are] managed and leveraged across organizations. Historically, asset owners have managed liquid and illiquid investments separately, using separate tools and processes. A true total portfolio view bridges these gaps by offering a holistic view of the entire portfolio.”
Jonathan Fuss, head of CIO total portfolio view for private markets and buy-side product reporting at Bloomberg L.P., outlined four key components he says investors need to achieve a total portfolio view:
- The ability to see all types of assets in one place, supported by consistent identifiers and data structures;
- A clear view of what the asset owner holds across debt and equity, across public and private markets, and whether those exposures sit in internally or externally managed portfolios;
- The ability to normalize different reporting timelines, cadences and return methodologies; and
- A risk framework that can support analysis across asset classes in a consistent, meaningful way.
“As [TPA] becomes more mainstream, service providers have an opportunity to elevate their data and analytics solutions, leveraging artificial intelligence and other advanced technology to replace spreadsheets that were typically used to get to a total portfolio view from multiple disparate sources,” Granger says. “The challenge is especially acute in the private market space where no data standard exists.”
Minding the Data Gap
The opacity and diversity of private market data are emerging as blind spots, making it more difficult for asset owners to fully implement TPA.
“For example, data [are] often spread across manager portals, emails, PDFs, spreadsheets and internal systems,” Fuss says. “At the same time, the market still lacks adoption of common identifiers, taxonomies and classification standards for private assets.”
As the name suggests, total portfolio approach—which views an investment portfolio as a whole, rather than as a series of individual asset class buckets—must contend with the time delay of private markets data (performance is often provided to investors on a one-quarter lag) and the unstructured nature of private markets data. The providers of market data and analytics tools are looking to provide standardization through their platforms.
“The honest reality is that TPA has exposed a gap the industry has been papering over for years,” says Mike Muniz, chief strategy officer at Canoe Intelligence, an alternative investment data processing company. “The analytics infrastructure for public markets is mature; the data infrastructure for private markets is not. Closing that gap is where the real work happens.”
Asset owners pursuing a total portfolio approach need their private market data to behave more like public market data, Muniz says. All the investment data need to be structured, timely and ready to flow into the systems where portfolio decisions get made alongside all investments.
At the moment, most private market data sit outside core portfolio management systems.
“Traditionally, investors have had to manually pull together private markets data from general partners, custodians and fund administrators, often leaving them with a portfolio view that lags public market data by two to four weeks,” says Subbiah Subramanian, head of data and analytics at Clearwater Analytics.
Product Launches
To meet the challenges of integrating private markets data, several service providers have launched products offering greater transparency into alternative asset class data.
In February, Venn launched Venn Daily Private Asset Returns, which provides asset owners hundreds of daily unsmoothed return streams for private market strategies and vintages. “An allocator can use them on a stand-alone basis, so if they want to look at the risk-return factor analysis of a 2016 buyout return stream, they can do that, or they can think of them as a best effort to represent their funds as part of a total portfolio approach,” Carrano says.
The tool aims to estimate marked-to-market exposure for several alternative asset classes by observing private asset cash flows and modeling the behavior of those cash flows with public market performance to show how these assets are performing on a day-to-day basis.
“Our clients, they’re really looking for easy-to-use, intuitive solutions that don’t require them to overhaul their investment allocation framework,” Carrano says. “They’re rather just looking for a way for things like hedge funds and private assets to become an extension of what they were already doing in order to implement a total portfolio approach.”
Clearwater Analytics aims to bring illiquid and public assets into the same data model through its investment intelligence suite, anchored by a tool called Total Portfolio Oversight.
The model does not treat “private markets as a separate side system,” Subramanian says. “That includes normalizing private assets alongside public securities, ingesting both structured and unstructured data, and applying the same quality and governance standards across the full portfolio.”
A total portfolio approach has been central to service provider Addepar since it was founded, according to Janeen France, the firm’s chief client officer: Approximately 40% of the $9 trillion in assets on Addepar’s platform are invested in private assets.
“We unify portfolio, market and client data into a single, permission-aware foundation so investment professionals can see and analyze public and private investments together across asset classes, ownership structures and geographies, and apply insight directly within their daily workflows,” France says.
France adds that the operational burden of disparate data has long been a challenge for private-market investors.
“Too many firms are still asking highly paid investment talent to manually stitch together delayed data,” France says.
Technology solutions company Confluence Technologies is building a tool to assist investors in evaluating the effects of swapping fixed-income managers, something intended to improve transparency.
“You can argue that a service provider—a classical fintech service provider, portfolio analysis provider—isn’t going to be easily fitting a single kind of portfolio analysis framework into every total portfolio analysis approach,” says Chris Smith-Hill, Confluence’s vice president and head of product. “What we’re trying to do is to enable transparency on equity funds as we do today, but we’re also expanding into fixed income.”
The Potential of ‘Shared Intelligence’
Smith-Hill notes that TPA clients will typically have separately managed accounts with their investment managers where data on investments in those accounts will be fed in from their custodian. Through their custodians, they can evaluate a fixed-income mandate’s active risk and liquidity components against the rest of their portfolio. But if they want to evaluate a new manager and swap that manager out, it might be more difficult to map out and evaluate the active risk and liquidity impact.
“So we’re building a solution that does the heavy lifting of that data ingestion for external managers that would help a total-portfolio-approach investor more easily get the transparency and measure the active risk and liquidity impacts of a new external fixed income fund,” Smith-Hill says.
Bloomberg and Canoe Intelligence recently announced a partnership to integrate Canoe’s private fund data into Bloomberg’s portfolio and risk analytics tool, PORT Enterprise, to support a total portfolio view.
“This reduces manual reporting and data entry while making private fund cash flows and position data more accessible for analysis alongside public market exposures,” Fuss says.
The tool aims to automate the flow of private fund data into Bloomberg’s platform, with the goal of making it easier to compare and monitor data across a portfolio. Clients, for example, could incorporate private funds into the same performance and risk workflows they use with public assets.
“What we’re seeing change is the expectation clients hold service providers to. For a long time, firms accepted that private market data would be slow, incomplete and manually intensive. That tolerance is running out,” Muniz says. “Our response has been to build deeper and automate further. We now process over 1 million documents per month across 44,000-plus funds, extracting structured data down to the underlying asset level. That breadth creates what we call ‘shared intelligence’—when patterns are resolved for one client’s fund, every client with that fund benefits. The system gets smarter at scale in a way no single institution can replicate on its own.”
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