What is good data architecture, and why is it so important for GPs, particularly when investing in complex sectors like technology?
Trinanjan Sengupta: Data architecture is foundational to how private markets work. For instance, if you think about how private markets data behaves, the underpinning processes and frequencies are very different to how information behaves and moves through the public markets.
An LP may have investments into hundreds of private funds, across private equity, private credit, infrastructure, real estate and venture capital, and they will want to see their overall exposure. They want to see the funds, sectors and the underlying companies that contribute to the fund performance.
The GP lens is different: they want to be able to explain their portfolio consistently – particularly where that consists of complex tech businesses – as well as seeing the valuations, managing the cashflow and looking at the investor documents. For them, data infrastructure becomes the operating backbone.
You cannot solve these two problems without a consistent data architecture. Siloed and often unstructured data sets create friction and a lack of interoperability. A solid data architecture across investment vehicles becomes the cornerstone, whether you’re a GP, an LP, or an administrator.
AI augments and enriches the capabilities that a strong data architecture lays the foundations for
What role can robust data architecture play in enabling effective AI and automation rollouts?
Adam Oumina: AI and automation are basically only as good as the data that sits behind them. If the data is fragmented, inconsistent and poorly governed, then your outcome is inevitably unreliable, no matter how advanced the technology is.
Automation addresses the widespread need to reduce manual work and can also improve the quality and speed of information capture
TS: That’s right.AI augments and enriches the capabilities that a strong data architecture lays the foundations for. It’s not a replacement, and it’s best to avoid thinking of it as the solution to all the problems we see in private markets data.
That said, AI is going to fast-track the solution of certain problems, whether that’s data ingestion and being able to extract meaningful insights and data intelligence from unstructured documents, looking at various kinds of alternative data sets as part of your macroeconomic information, or using AI to speed up the detection of anomalies in your data sets.
Then, you can use AI for its natural language generation and processing capabilities to be able to converse with your data and ask questions on your data for more insights. That is where AI augments what you have, on top of the right data foundation.
What should be the main objective for private equity investors when it comes to improving their data position and gaining greater visibility across portfolio companies?
TS: LPs and GPs will have different perspectives, but the key things they would be looking at would be transparency, performance, risk, liquidity, valuation and reporting. An LP might have the lens of, ‘What do I own and what is my exposure to various countries, sectors and currencies?’ And they would want to compare the performance contributed by the underlying companies, and underlying assets or explain their exposures.
For multi-asset managers, the goal is often to integrate these private market insights with their public market exposures, or to more accurately forecast capital calls, distributions and commitments. From a GP perspective, the questions are different. Rather than comparing performance across managers, you’ll want to understand valuations and potential deviations from your plans, manage cashflows and financing processes and answer the increasingly granular requests coming in from investors.
AO: The common objective is around building a single transparent view of the portfolio. Investors need to understand not only how companies are performing today but also what kind of risks are emerging and what value can be created tomorrow. They want better visibility to identify patterns across portfolios and allocate resources in a more effective way.
Success is not measured by the amount of data collected but by the quality of the insights that can be generated. That’s what allows investors to make decisions with greater confidence.
How can digitalization change the way GPs and LPs interact with asset-level data?
AO: Private markets have traditionally relied on spreadsheets, reports and email-based exchanges of information between GPs and LPs. Data is often collected at the GP level, stored in different systems and then distributed to LPs, creating inefficiencies, delays and the risk of inconsistencies over time. Digitalization addresses this by creating a single source of truth shared across stakeholders, with higher-quality data and much greater availability in near real-time.
A good example is CapLink Private, powered by AssetMetrix, which provides GPs and LPs with secure access to portfolio data, performance metrics, reporting and documents within a connected digital environment. Rather than waiting for information to be distributed, investors can access, explore and analyze data directly through interactive tools and dashboards.
As AssetMetrix CEO Dimitris Matalliotakis notes, the industry is moving away from “email and PDF cycles” towards “continuous visibility”. This shift enables more transparent and efficient workflows, consistent performance monitoring and easier access to supporting documentation.
Importantly, digital platforms allow investors to move seamlessly from a fund-level overview to the underlying portfolio companies, providing deeper insight into portfolio composition and investment exposures. Bringing data, analytics and documents together in one place strengthens transparency, improves decision-making and delivers a significantly better user experience for both GPs and LPs.
TS: The actual problems you have with private markets data are not so different to the public markets. You need to bring in data from multiple sources, ingest it in a seamless manner, try and harmonize it into a common model, have a well-governed data platform that allows you to run analytics that enrich your understanding of a particular sector or universe of businesses, and then distribute it in the form of dashboards or reports.
However, private markets differ in shape, sources, nature and frequency of data. A unified architecture avoids separate systems. For example, at BNP Paribas, our Data PRISM360 service, developed with NeoXam, brings together public and private investment data into a harmonized model for a total portfolio view.
Tim Versteeg, Chief Sales Officer and Managing Director APAC, aptly says: “A total portfolio view needs to bring public and private assets together while preserving their distinct inherent characteristics”
How are GPs using automation and analytics to improve their decision-making?
AO: Automation addresses the widespread need to reduce manual work and can also improve the quality and speed of information capture. This is where technologies such as optical character recognition, machine learning, robotics and now AI can help, by automatically collecting and processing information from various kinds of reports and statements. This obviously helps with the reduction of operational effort and improves consistency, allowing teams to focus on higher-value activities.
Effective analytics really helps GPs move from looking at the past to planning for the future. Instead of relying only on historical reports, managers can use forecasting, benchmarking, risk analysis and scenario modelling to start to understand what might happen next.
TS: GPs use analytics, AI and automation across those building blocks: processing unstructured data, identifying performance drivers and testing liquidity scenarios when distributions change or capital calls accelerate.
Christophe Picardel: In many ways, we have followed the same path as most GPs on this journey. We first selected partners such as AssetMetrix and NeoXam to complement our offering and integrated them into our ecosystem.
Now, we are all moving to the next stage, which is redesigning our overall operating models around AI to become more efficient, more scalable and faster.
Private markets exits were not under the same LP pressure they are today. Now though, analytics has become an essential tool, helping GPs benchmark assets against relevant peers and assess risk. Plus, it’s no longer just the investment team using analytics, but also the CFO office using it for cashflow forecasting and addressing liquidity challenges. Investors want more information, and GPs need to be able to deliver that on demand across their overall value chain.
Analytics has become an essential tool, helping GPs benchmark assets against relevant peers and assess risk
What is the end goal when it comes to digital capabilities, and what are the risk factors that GPs need to pay attention to?
AO: The end goal is scalability with control. Growing portfolios mean more data, investor types, reporting and regulatory requirements. Digital capabilities should support efficient growth, transparency, governance and the investor experience. Operating models must connect data, workflows and digital interfaces through seamless information flows, supported by strong governance, auditability and high-quality data. Innovation remains a key enabler, provided it meets regulatory requirements and client-specific AI governance standards.
TS: Data provenance and lineage and appropriate access entitlements are essential, as is the implementation of a responsible AI framework to ensure ethical and transparent use of the technology. In Asia-Pacific, solutions must also respect data sovereignty and residency requirements.
CP: GPs must retain control of their data and processes, keep humans in the loop and assess AI’s long-term business value, beyond short-term cost efficiencies.
More information is available from Private Capital and Investment analytics and data services.
This article was first published by Private Equity Group in its “Investing in Tech” report in November 2026.