Steven Hart

Information Architect

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From document library to intelligence platform

A knowledge graph design for a private equity intelligence platform, modelling the relationships between investors, fund managers, funds, deals, people, events, and editorial content; then showing what those relationships make possible in the product.

Developed while contracted to PEI Group as Senior Information Architect. While not taken into production, the work was reviewed and supported by senior design and data leadership as a potential future direction.

Snapshot of a knowledge graph

The problem

An existing platform built on a document repository:

  • Content was structured for publishing, not analysis

  • Relationships between entities not made explicit in the data model

  • Users had to manually piece together insights

As a result:

  • Key investor questions were slow or impossible to answer

  • High-value signals remained buried in unconnected content

What my work changed

Instead of organising content around pages, I restructured it around entities and relationships:

  • Firms (Fund managers; Investors)

  • Funds and strategies

  • Sectors and regions

  • Events and signals

  • Articles

This created a system where meaning is defined by connections, not just content.

Core insight

The breakthrough was recognising that PEI’s data is inherently relational.

By modelling three primary dimensions:

  • Strategy

  • Sector

  • Region

…it became possible to unify disparate content into a coherent structure that supports querying, comparison, and pattern detection.

The solution

I designed a knowledge graph that:

  • Defines entities and their relationships

  • Applies controlled vocabularies for consistency

  • Supports structured querying across the dataset

This enables:

  • Dynamic exploration of investment activity

  • Identification of behavioural patterns

  • Aggregation of signals across multiple sources

What the graph unlocks for customers

Instead of having to do a lot of reading and bouncing between pages to find answers, users can now ask:

  • Which LPs are increasing exposure to a specific sector?

  • How is a GP’s strategy shifting over time?

  • Where are emerging clusters of investment activity?

Previously, there were either manual, time-intensive tasks, or simply not feasible.

An example: Detecting strategy drift

Every fund has a declared focus: the strategy, sector, and region it says it will invest in.

Every fund also has an actual behaviour: the strategies, sectors, and regions where its portfolio companies actually operate.

These two things are often different, and the difference reveals whether a fund is drifting from its stated mandate, entering new territory quietly, or shifting focus in response to market conditions.

In the old model, detecting strategy drift took up analyst time and effort, forcing them to:

  • Read multiple articles

  • Manually track firms, sectors, and activity

  • Build a mental model over time

But the graph enables instant comparison of declared or intended focus, and actual investment behaviours. A single graph query returns:

  • Relevant entities

  • Associated strategies

  • Recent signals and actual behaviours

Patterns across the dataset emerge immediately.

What this means for the product

The graph is not just a backend model, its design and structure actively drives key product features:

  • Dashboards showing GP activity and positioning

  • Article enrichment with network intelligence modules providing rich context and multiple windows onto the data ecosystem

  • Signal detection across entities and markets

This connects information architecture directly to user-facing value.

Challenges

Designing the model exposed (and solved) some non-trivial problems:

  • Entity resolution
    Identifying when different references describe the same real-world entity

  • Data consistency
    Applying controlled vocabularies across varied and messy source content

  • Scalability
    Ensuring the model can evolve as new data and use cases emerge

  • Synthetic vs real data
    Early validation required assumptions that would need testing in production

Outcome

This work created a foundation for:

  • Faster, more reliable insight generation

  • New product capabilities based on querying and aggregation

  • A shift in user behaviours: from searching for content, to discovering intelligence.

My role

  • Defined the information architecture

  • Designed the graph schema (entity and relationship model)

  • Established controlled vocabularies

  • Shaped how the model translates into product features

Personal reflection

When data-rich products fail, it is often not because they lack data, but because they lack user-centred structure.

This project demonstrates how information architecture can:

  • Unlock latent value in existing content

  • Enable entirely new classes of interaction and insight

  • Turn information into a strategic asset


Steven Hart

Information Architect

CONTACT ME