Gresham Enterprise Data Management (EDM) is the perfect match for resolving the Entity matching challenge. The growing complexity and variety of data sets such as private markets and ESG data can benefit by combining AI-powered entity resolution with governance, enrichment, and human oversight. Transforming entity data from an operational burden into a strategic asset. Gayatri Pengilly provides practical insight from her experience in the use of EDM to overcome these challenges.
For most data teams, entity matching is one of those persistent challenges that never seems to disappear.
I've spent years leading data operations teams, and no matter what type of dataset we encountered, private markets, ESG, credit risk, reference data, or security master data, the same issue kept resurfacing: identifying when multiple records actually refer to the same entity.
It sounds simple. In reality, it can be one of the biggest operational bottlenecks. The good news is that with a structured approach to Enterprise Data Management (EDM) firms can easily address this head on.
I've seen highly skilled analysts spend hours manually comparing records, reviewing exceptions, and reconciling conflicting data from multiple vendors. The work gets done, but strategic initiatives often get delayed. Data quality improvements, new analytics capabilities, and governance projects are too often pushed down the priority list because teams are stuck fixing entity data issues.
If that sounds familiar, you're not alone.
The challenge isn't usually a result of a skill shortage, The problem is that entity matching becomes increasingly complex as organizations scale.
A single company can appear in multiple datasets under slightly different names. Legal entities change names, subsidiaries are mistaken for parent organizations, identifiers are missing, country codes differ, and simple data-entry errors introduce additional uncertainty.
What looks like a minor discrepancy quickly becomes a major operational burden when you're managing hundreds of thousands, or even millions of records.
The result is a continuous cycle of manual investigation, reconciliation, and exception handling for already busy teams to absorb.
Much of the knowledge required to resolve these issues often sits with a handful of experienced team members rather than being embedded within governed data management processes.
From a data perspective, that's a significant risk but one that can be resolved with the capabilities of Gresham EDM.
Private markets highlight this problem more than almost any other asset class.
In public markets, firms can rely more on established identifiers such as LEIs, ISINs, or CUSIPs to narrow the scope of entity resolution. In private markets, those identifiers are often unavailable.
Many records arrive with little more than a company name and country.
At the same time, institutional allocations to private assets continue to grow, creating greater demand for high-quality entity data management. Teams frequently rely on internal spreadsheets, manually maintained entity lists, and institutional knowledge to fill the gaps.
The same challenge can appear in ESG data management and credit risk data management. Organizations often receive data from multiple providers, each using different naming conventions and reference structures.
Without a robust entity management framework, firms can struggle to:
Over time if not managed strategically with a robust EDM solution, these inefficiencies can lead to duplicated effort, reduced confidence in data quality, and increased operational costs.
There are plenty of entity matching and entity resolution tools available today.
A lot can identify whether two records are likely to represent the same organization.
While useful, it's only part of the challenge.
Gresham EDM goes beyond simply matching records. It creates trusted, governed, and enriched entity data that can be used across the organisation.
Many matching solutions stop at the point of identification.
Gresham EDM goes further to resolve Entity matching. Gresham EDM addresses:
Without a structured approach to Enterprise Data Management and Entity Matching, firms can find themselves performing significant manual work after the matching process has finished.
The gap between "these records match" and "this is a trusted enterprise-wide golden record" is closed with Gresham EDM.
Gresham doesn’t treat entity management strategies as a standalone exercise.
Instead, Gresham EDM combines three critical capabilities:
AI-powered matching engines evaluate multiple attributes simultaneously, including:
This enables matching even when traditional identifiers are unavailable.
High-confidence matches can be processed automatically, while uncertain cases are routed for review.
Once an entity has been matched, the next step is enrichment.
A matched record becomes significantly more valuable when it is linked to:
This transforms a simple match into a comprehensive golden record that can support downstream reporting, compliance, analytics, and investment processes.
Gresham EDM delivers Governance from the beginning.
Entity matching is not a one-time exercise. New entities arrive daily, vendor records change, and organisational structures evolve.
Gresham EDM secures:
Without governance, entity quality will likely deteriorate over time.
Artificial intelligence has transformed entity matching capabilities, but human expertise remains essential.
The reality is that no algorithm yet perfectly resolves every edge case.
Complex ownership structures, ambiguous naming conventions, mergers, acquisitions, and incomplete records still require human judgement.
That's why the most effective Enterprise Data Management models combine AI-driven automation with expert review.
The technology handles the vast majority of matching and enrichment activities, while data specialists focus on the exceptions that genuinely require investigation.
This "human-in-the-loop" approach delivers the best of both worlds:
Most importantly, it allows internal teams to focus on higher-value activities instead of spending their time manually reconciling records.
Entity matching has traditionally been viewed as a necessary operational burden.
Organizations that approach it as a core Enterprise Data Management capability gain a significant advantage.
When entity data is trusted, enriched, and governed, teams can make faster decisions, improve reporting accuracy, strengthen regulatory compliance, and unlock greater value from their data investments.
The goal isn't simply to match records.
The goal is to create a trusted entity foundation that supports every downstream business process.
Once that foundation is in place, entity matching stops being a constant headache and starts becoming a strategic asset.
If you are interested to learn more about how Gresham is solving the Entity Matching challenge with intelligent Enterprise Data Management, click here to connect with one of our experts Contact Us | Gresham