For asset owners navigating today’s investing landscape, every step is fraught with risk and uncertainty – from navigating macroeconomic and geopolitical tensions to fears of stock market concentration risk, asset bubbles and private market illiquidity. All of which make comprehensive, accurate, timely data increasingly vital.
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As the story goes, Nathan Rothschild’s couriers sped him early news of Napoleon’s defeat at the Battle of Waterloo in 1815. He traded this intelligence on to profit from the subsequent stock market rise. This story, maybe apocryphal, highlights an eternal truth: the value of timely, accurate information.
Insights in investment data have always been crucial to asset owners. Yet three key trends are sharpening the focus on effective data management and the quality of outputs.
Three trends driving effective data management
Asset diversification complicating data management
Institutional investors have been at the forefront of the reallocation from traditional listed assets into unlisted private markets, in the push for stronger returns and diversification. A lack of liquidity and historically low distribution rates that threaten to blunt performance and pin some limited partners up against their allocation limits may have dampened some of the lustre. But the shift continues.
“Demand has surged across Asia Pacific,” says Elaine Tan, Head of Asset Owners & Asset Managers Client Lines for Asia Pacific at BNP Paribas’ Securities Services business.
She points to Australia’s superannuation pension plans, which have seen increasing investments in areas such as infrastructure and other unlisted assets. Singapore sovereign wealth fund GIC’s long-term strategy targets a 13-17% allocation to private equity and 9-13% in real estate[1], with the fund topping European startup dealmaking in 2024.[2] Interest in private equity and hedge funds among South Korean institutions is also growing, with the government actively promoting private infrastructure investment, adds Tan.
In continental Europe, various pension funds that once had extremely limited exposure to illiquid assets are now eyeing allocations of up to 20% to boost returns to match their liabilities.
“The investment rationale is clear. Managing this asset diversification operationally is challenging though” cautions Rémi Toucheboeuf, Head of Investment Analytics at BNP Paribas’ Securities Services business.
“Accurate, timely, granular data is vital to ensure investors have transparent look-throughs into their portfolio positions and can easily aggregate data for multi-asset portfolios,” he says. “They want visibility on their whole portfolio of assets and need to run deep dive analytics to understand their specific exposures and risks, while identifying investment opportunities or strategic re-allocation requirements.”
Yet while masses of public information exist around listed instruments, unlisted assets often lack readily available datapoints. Valuing illiquid ‘Level 3’ assets[3], where there is no accurate market price, depends on estimates derived from complex mathematical models and subjective assumptions. A lack of universal standards and patchy environmental, social and governance (ESG) metrics complicate the asset owner efforts to have full transparency over the sustainability of their investments, and the management of their commitments.
Data fragmentation is a further issue, especially as asset owners diversify portfolios and work with more specialist asset managers. “Data obtained from multiple sources is often inconsistent and/or incomplete. This creates operationally complex demands for data acquisition, integration, normalisation, standardisation and consolidation to create a full and unified view of owners’ holdings, performance and risk exposures,” notes Toucheboeuf.
Data focus in evolving regulation
The more stringent global regulatory landscape has amplified scrutiny for meticulous data management and reporting. Asset owners must have a complete picture of their investments and the ability to report on them.
“For instance, the Australian Prudential Regulation Authority is currently extending its Superannuation Data Transformation project. The goal is to collect accurate and comparable data from the industry to aid regulatory oversight, increase transparency and improve member outcomes [4]“, Tan observes.
That means not just providing an overview, but having a look-through capability so investors can understand what they are invested in.
In the UK, the Prudential Regulation Authority has proposed new liquidity reporting requirements for large insurance firms with significant exposures to derivatives or securities involved in lending or repurchase agreements. One of the objectives is to avoid a repeat of previous derivative-driven liquidity strains, such as the ‘dash for cash’ at the beginning of the Covid-19 pandemic and the liability-driven investment crisis in September 2022.[5] The proposals require insurers to source details of all the assets in their portfolio so they can provide more timely, consistent and accurate information on their liquidity positions.
A data management framework that combines robust data governance with accurate reporting is essential to ensure asset owners can stay compliant with these evolving regulatory standards.
Data challenges for a successful business to overcome
Strategies based on legacy data management platforms struggle to handle the volumes, velocity and variety of data coming from a modern multi-asset portfolio. Decision-making is often based on historical data, presented in static formats, making it hard to identify market trends and insights that will abet faster, better decisions. Time-consuming and error-prone processes compromise operational efficiency.
Data consumers, such as chief investment, chief information and chief operating officers, now require more information and greater visibility to be able to perform their duties.
“Deep-dive analytics to understand the exposure to sudden market events or the risks associated with specific assets is becoming essential,” notes Toucheboeuf. “To make informed investment decisions and improve oversight and reporting, accurate and normalised data is vital. These normalised investment datasets are ultimately essential for our clients when they need to connect to their eco-system of partners and providers. ”
“Powerful data analytics and visualisation capabilities provide deep insights into asset performance, market trends and risk, allowing users to quickly assess their investment exposures and adjust their portfolio strategy accordingly,” adds Tan.
Adopting data best practices
Closing the gap between what asset owners have and what they need to reap the advantages of a modern data management infrastructure requires massive investment and expertise.
“The goal is a holistic, standardised, cross-asset class, multi-dataset view of asset owners’ portfolios,” explains Tan. “That entails full end-to-end capture, normalisation and transformation of data from multiple third-parties and internal data sources. Data dictionary and model, business rules and controls, properly applied, will normalise and standardise the data and ensure its quality. The resulting books of record can then be used to support performance and risk analytics, decision-making across the investment value chain, operations, as well as client, regulatory and management reporting. And it must be wrapped in full data security, resiliency and privacy.”
Creating a fit-for-purpose infrastructure takes a meld of sophisticated technology, data feeds and business acumen. This must be allied with teams of data scientists and analysts who understand the data complexity associated with different asset classes, and how technology can be best employed to handle them.
Some asset owners are willing to invest and obtain that crossover of skills. For others keen to avoid the cost and focus on their core business, outsourcing to a proven managed service expert offers an alternative option.
The data management tailored solution
Partnering with a third-party specialist allows asset owners to take advantage of an industrial-scale data management solution without the associated investment and maintenance costs. An end-to-end data layer can also enable asset owners to break the data siloes that build up across different parts of the organisation, helping them obtain a unified view across all their asset class investment domains.
To ensure asset owners have the capabilities they need, BNP Paribas has launched a new post-trade data management service in collaboration with financial data technology solutions leader NeoXam.
“Operated by BNP Paribas’ expert teams and using NeoXam’s advanced Investment Data Solution technology, the service provides clients with a holistic, standardised view of their portfolios across different asset classes and multiple datasets to help them make more accurate and informed investment decisions,” Toucheboeuf explains.
“A one-size-fits-all approach – the sort of standardised rigidity that outsourcing models are sometimes accused of – won’t work though with data management,” says Toucheboeuf. “Understanding an institution’s specific data input and output needs, their business constraints, and proposing an industrialised and cost-efficient solution are essential. As is a self-service facility, so that our institutional clients can choose a particular data source, implement specific business rules or extract information, and do it on the fly.”
For more information on our data management services , please read our press release: BNP Paribas’ Securities Services business launches new data management services in partnership with NeoXam – Securities Services
[1] Our Policy Portfolio, GIC, https://www.gic.com.sg/how-we-invest/our-policy-portfolio/
[2] Singapore’s GIC invested more than any other sovereign wealth fund into European startups in 2024, Sifted, 7 January 2025, https://sifted.eu/articles/singapore-gic-sovereign-wealth
[3] Level 3 assets are infrequently traded and so cannot be valued using readily available market information. Instead, Level 3 asset valuations integrate managers’ marks and are based on external valuation models and assumptions
[4] Need for real-time processing and automation. Consultation on data collections to strengthen transparency in latest phase of Superannuation Data Transformation, APRA, 6 December 2024, https://www.apra.gov.au/phase-2-depth
[5] CP19/24 – Closing liquidity reporting gaps and streamlining Standard Formula reporting, Bank of England, 11 December 2024, https://www.bankofengland.co.uk/prudential-regulation/publication/2024/december/closing-liquidity-reporting-gaps-consultation-paper
