Private assets are firmly established as a major focus of financial institutions’ investment strategies, but many still struggle to apply the same technological rigour to their alternative portfolios as they do their allocations to public assets. This is through no fault of their own, and is almost entirely the result of the shortage of quality data, the most recent A-Team Group webinar discussed.
The processes used by private asset allocators and portfolio companies to extract data are almost overwhelmingly performed through manual means, meaning that institutional limited partners who are used to the granular, synchronous information fed from exchanges have difficulty integrating that information into their own sophisticated systems.
At the Data Management Insight webinar, entitled Taming the Private Markets Data Beast: Valuation, Governance, and the AI Frontier, experts from the provision and consumption sides of the data ecosystem examined how modernisation of data governance, valuation workflows and reporting frameworks can help firms keep pace with institutional demand.
On the webinar, Åse Bergstedt, a Senior Sustainability and ESG Adviser, was joined by Christina Hois, Managing Director, Data and Analytics Governance at BMO Bank of Montreal and Calum Conejo-Watt, Head of Data Governance and Quality at Lombard Odier Asset Management. Data Management Insight Editor Mark McCord moderated the discussion.
Traditional Challenges for Modern Use Cases
The panel noted that difficulties that have nagged data managers for many years are more acute within private markets. The necessity to establish authoritative data sources to anchor alternative investment workflows is made problematic by valuation marks that arrive irregularly via valuation memos, quarterly statements and PDF reports.
In this scenario, attempting to enforce governance at every front-end ingestion point quickly becomes unmanageable, with one panellist noting that attempting to govern hundreds of consumption points is inefficient, making it essential to ring-fence critical data elements within centralised golden sources.
As automated risk engines and machine learning models digest alternative data, poor quality information and input errors propagate rapidly throughout downstream systems. The traditional industry adage of “garbage in, garbage out” has evolved in the era of advanced analytics into “garbage in, garbage compounded”, one speaker said. To counter this, institutions must establish an evolving operating model where critical data elements are rigorously mapped, validated and tied directly to quantifiable business returns and regulatory mandates.
Lineage Criticality
Satisfying regulatory scrutiny and internal audit requirements demands full end-to-end data lineage, tracing valuations back to their original assumptions and source documents. Historically, lineage was documented manually in static spreadsheets – a practice that is increasingly rejected by modern regulators. One panellist highlighted that while manual lineage was common during early regulatory implementations, modern environments require automated tools that maintain continuous visibility across changing technology stacks.
To achieve scalable lineage and real-time transparency, institutions are shifting towards automated code parsing and semantic contextualisation. By using intelligent assistants to inspect underlying Java, Python and SQL code, platforms can automatically generate natural language definitions and map dependencies across complex pipelines.
Furthermore, enriching raw data with a comprehensive semantic layer ensures that a single data point carries multi-dimensional meaning across risk, compliance, trading and finance functions. One participant stressed that future competitive advantage will not stem from accumulating larger volumes of data, but rather from superior contextualisation and business-aligned ontology.
AI Enters the Fray
The integration of Generative AI and Large Language Models offers transformative potential for ingesting unstructured alternative data, such as private placement memorandums and quarterly financial reports. However, deploying AI across private market valuation pipelines requires a nuanced balance between deterministic control and algorithmic flexibility. Unconstrained language models introduce unacceptable risks of hallucination and unexplainable outputs, whereas overly rigid rules eliminate the adaptive capabilities that make AI valuable.
Panelists cautioned that over-reliance on third-party commercial language models creates strategic vulnerability. Because foundational AI models are universally available to market competitors, an institution’s unique competitive edge lies entirely in combining AI extraction tools with proprietary domain knowledge and internal ontology. Furthermore, institutions must maintain clear accountability structures; as one speaker remarked, firms cannot relinquish governance by attributing valuation anomalies to algorithmic decision-making.
Sustainability Hurdles
Incorporating ESG factors into private market valuations represents another layer of operational complexity. ESG metrics have transitioned from non-financial disclosures into core valuation components, directly influencing reputational risk and long-term exit pricing. A senior sustainability advisor pointed out that regulatory compliance is no longer about presenting a single perfect data point, but rather about demonstrating transparency, materiality, and auditability throughout the decision-making process.
As institutional limited partners demand higher frequency reporting and deeper transparency, asset managers must move away from static quarterly PDF reporting toward interactive, event-driven data architectures. Implementing unified platforms – often described as a “single pane of glass” – enables real-time data profiling, proactive anomaly detection, and streamlined self-service access for investment professionals.
- The webinar, which was recorded for download-on-demand, is available for viewing here.
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