The Power of Domain-Enabled Architecture vs. Generic Cloud Infrastructure for the Buy Side
Buy-side firms are accelerating AI adoption. But they are doing it on a data foundation that was never intended for this purpose. It’s not surprising then that an October 2025 survey about AI use in finance conducted by EY and the Institute of International Finance found that data quality is the most common obstacle to production-ready AI.
What has become clear is that the constraint lies in the architecture supporting the AI model. Specifically, whether that architecture resolves the meaning of financial concepts in a single governed repository or resolves them repeatedly—and differently—at each point of use.
Until very recently, this difference didn’t matter. Human operators took up the slack. In other words, an analyst who knew the data intimately could catch a figure that “looked wrong” before it ever reached a client or a regulator. No autonomous agent has that kind of intuition, and the underlying data architecture must account for this.
As firms continue to move from assistive tools to AI agents that read and act on data directly, using a traditional data architecture becomes a significant reporting risk. In this article, we’ll explore what kind of architecture works best in a data warehouse for agentic AI and why.
What Separates A Data Warehouse From A Collection of Tables
The critical difference in buy-side workflows is that financial metrics are often calculated, not just stored. Exposure, AUM, NAV, and IRR each rely on a specific definition of the term and a method of calculation applied to the underlying data. Exposure, for example, depends on how currency, netting, and look-through are defined. IRR depends on the cash flow convention.
The big question is where these definitions live.
In a buy-side data warehouse architecture, each definition is declared once, in a fully governed semantic layer, and passed along to every downstream consumer. In a generic system, definitions are declared at the edge, wherever a given report is being constructed, and then reconciled later.
To understand why this is, we need to acknowledge that a “data warehouse” can refer to three very different types of data architecture.
| Architecture | Purpose | Where Definitions Are Resolved |
|---|---|---|
| Table assembly | Relational tables built one by one over many years, connected manually for context | Ad hoc for each report |
| Generic cloud platform | Elastic, modern system that has no built-in definitions of trades, fund structures, capital calls, or any other buy-side concept | Firm must create definitions manually |
| Domain-modeled data warehouse | Engineered around buy-side taxonomies and calculations from the outset | Declared once in a governed semantic layer |
All three of these are, technically speaking, data warehouses. But only the domain-modeled data warehouse resolves buy-side definitions consistently.
Why A Table Assembly Fails As An Analytical System
The failure of table assemblies is structural. Transactional databases are designed for fast, narrow writes: one trade or price at a time. Analytical work requires the opposite, aggregating millions of rows of data to establish total issuer exposure across every fund on a given date. Forcing a transactional database to serve analytical queries involves deep, multi-table joins it was never designed to support, so query performance degrades as data volume grows.
This weakness compounds with a missing foreign key, an inconsistent time zone convention, or a deleted security identifier. Each of these issues is invisible in isolation, and each produces orphaned records, broken joints, and figures that are wrong in ways no one detects until two reports disagree. This effect compounds in ways no single quarterly report can captures, and most firms only see it once two systems disagree on the same number.
Why A Generic Cloud Platform Is Not Sufficient On Its Own
Moving to a generic cloud platform solves a number of urgent problems and is the correct first step. Decoupling storage from compute lets a quant team, for example, scale up to back test decades of history and scale down later without contending for shared hardware. Native data sharing removes the pipelines historically required to consume third-party datasets. These are durable advantages and the reason why the shift to cloud infrastructure has held strong.
What a generic platform can’t deliver is domain-specific meaning. It does not know what AUM is, how fund ownership structure should be modeled, or how a capital call flows into fund-level performance. Building that context into a generic platform is a massive undertaking, one that commonly requires dozens of engineers and sustained investment to complete.
If this customization is left undone, gaps are filled ad hoc. Teams encode different definitions into their queries and report conflicting figures for the same underlying metric, each of which is internally correct. In other words, a buy-side firm can run a modern, elastic, well-administered data infrastructure and still not have a data warehouse in the one way that really matters.
What A Domain-Modeled Data Warehouse Does That Generic Platforms Can’t
A domain-modeled data warehouse handles two key concepts very differently than a generic platform: time and meaning.
Time
Buy-side data is often revised retroactively due to delayed corporate actions, fund administrator restatements, late trade corrections, and many other exceptions. A generic platform typically overwrites the prior value at the time of correction, destroying the original record. A domain-modeled data warehouse tracks two independent timelines: the date when a fact became true (effective date) and the date when the system recorded it (knowledge date).
These two dates are often different, which is why bi-temporal data management is necessary to preserve both timelines and overwrite neither.
For example, consider a closing price booked Friday afternoon but corrected on Monday morning. A bi-temporal model retains both values as distinct records that can answer two separate, equally valid questions:
- What was the portfolio worth as of Friday afternoon?
- What is the portfolio worth now that the correction has been applied?
This capability is vital for passing audit tests, which require firms to reconstruct what they knew at any given point in time, without reconstruction after the fact.
Meaning
A domain-modeled data warehouse like the IVP Data Warehouse Solution carries a financial ontology in its design: governed definitions of instruments, entities, transactions, and positions, and all the ways to derive metrics from them. This semantic layer is what enables a single security to include every international identifier within one governed record, so downstream reporting works from a single resolved identity instead of constantly reconciling mismatches.
Domain-specific definitions also make calculations portable. A performance engine computing returns across multi-level hierarchies (firm, fund, deal, sector) using governed methods for TWRR, dynamic IRR, MOIC, and modified Dietz will produces identical figures no matter where they are consumed. The same applies to compliance. Overcollateralization and interest coverage tests for CLOs or UCITS and ‘40 Act rules run inside the governed semantic layer rather than a separate extract pulled after the fact.
Sophisticated Firms Prefer A Converged Model
Technically mature buy-side firms are moving toward a converged model with a domain-specific semantic layer deployed on elastic cloud infrastructure like Snowflake.
With this approach, firms benefit from the elasticity of modern compute and the domain awareness that compute does not provide. More than one platform now occupies this category credibly. It has become a recognized architecture rather than any single vendor’s position.
Why AI Raises The Stakes
The gap left by unresolved definitions was closed manually for years. While analysts who worked from instinct, experience, and intuition were a real source of control, these particular abilities can’t be automated.
An AI agent has no such instincts and needs none to sound authoritative. It reads whatever definition its query path reaches, applies it, and returns a result with the same degree of confidence whether the definition was correct or not.
This is not a defect in the AI agent, it’s a defect in any data foundation that uses three distinct definitions of the same concept across three teams. With no central governed source to check against, an AI agent will produce errors at machine speed and scale, with a level of fluency that makes these errors much harder to catch than the manual versions ever were.
In short, AI readiness is determined by the data foundation, not the model deployed on top of it.
How To Assess Your Data Warehouse
To assess AI readiness, firms first need to determine which kind of data warehouse you have, and whether reported figures are decided once and governed accordingly or decided repeatedly at every point of use.
A firm running on tables and spreadsheets generally recognizes it is not ready for AI agents. But a firm running on modern cloud infrastructure often assumes that data governance is built into the platform, without confirming whether it is domain-modeled. That assumption must be tested before an agent is pointed at the data, because the agent will not test it, and it will not hesitate to answer.
The IVP Data Warehouse Solution, for example, is specifically designed for the converged model. It is a domain-modeled data warehouse with bi-temporal data management, a governed financial ontology, and a native performance and compliance engine, all of which is deployed on modern cloud infrastructure.
Getting the data warehouse right also determines whether the tools built on top of it, including dashboards, reports, and investor-facing analytics, can actually run in real-time rather than against a stale extract.
When you know what kind of data architecture your firm is actually running, you can start determining what steps remain before AI agents can be deployed successfully.
