What is Entity Resolution?

Entity resolution follows a five-step pipeline. First, records from multiple sources are ingested and standardized into a common format — normalizing company names, address abbreviations, phone formats, and date fields so comparison is meaningful.

Second, a blocking or indexing step groups records into candidate sets. Comparing every record against every other is computationally infeasible at enterprise scale — a dataset of one million records yields 500 billion pairs. Blocking reduces this to a tractable search space by pre-grouping records that share at least one common attribute (a domain prefix, a zip code, a phonetic name cluster).

Third, within each candidate set, records are compared attribute-by-attribute (company name, domain, address, phone, EIN) and scored for similarity. Fourth, each pair is classified as a match, non-match, or possible match based on thresholds calibrated to the precision-recall tradeoff appropriate for the use case. Fifth, matched records are clustered into a unified entity profile — a single golden record that consolidates all attributes from the source records.

Advanced systems add a relationship-detection layer that discovers both disclosed connections (explicitly shared attributes like the same registered address) and inferred connections (behavioral co-occurrence in data streams). This powers use cases like fraud ring detection and account hierarchy mapping in B2B.

Entity resolution is the umbrella discipline. Within it, deduplication refers to finding and merging duplicate records that exist within a single dataset — for example, cleaning duplicate contacts inside your CRM. Record linkage refers to matching records across two or more distinct datasets — for example, matching a third-party intent provider's account list against your CRM accounts.

Identity resolution is a narrower concept: it is entity resolution applied specifically to individuals (persons), often in a marketing or compliance context. In B2B, the most critical entity is typically the account or company, not an individual, because revenue flows from organizations — though resolving contacts to accounts is equally important for routing and attribution.

In practice, most enterprise entity resolution problems involve both: first deduplicate within each source, then link across sources, then canonicalize into a master record. Teams that treat these as separate, optional clean-up projects rather than a continuous pipeline tend to accumulate data debt faster than they can pay it down.

Gartner has estimated that poor data quality costs the average organization $12.9 million per year — based on a survey of 154 large enterprises already investing in data quality tooling, which means the number likely understates the cost for organizations with no such investment. For GTM teams specifically, the damage is concrete: duplicate account records mean intent signals fire to the wrong territory owner; unresolved contacts mean outreach goes to the wrong person at the right account; and mismatched records cause attribution models to double-count or miss pipeline entirely.

A BARC survey of 421 organizations found that data quality as the number-one obstacle to AI adoption jumped from 19% in 2024 to 44% in 2025 — meaning as teams deploy AI scoring, sequencing, and research agents, bad entity data has become the single biggest bottleneck to extracting value from those investments.

Sales reps also feel the drag directly. Research from multiple GTM analyst sources suggests reps waste more than 27% of potential selling time following bad or duplicate data — more than a full day per week spent on dead-end records that proper entity resolution would have flagged, routed correctly, or suppressed.

The highest-leverage use case is account deduplication and hierarchy resolution: building a clean, canonical list of target companies from CRM, MAP, and third-party data sources so that intent signals, territory assignments, and outreach are all anchored to the same record. When a company operating as 'Acme Inc.' in Salesforce and 'Acme Corporation' in Bombora are resolved to one entity, signal-to-action latency drops significantly.

A second major use case is contact-to-account matching — ensuring that individual contacts (resolved across LinkedIn, email databases, and CRM) are correctly associated with their employer account record. This underpins accurate buying committee mapping, personalized outreach, and post-conversion attribution.

Franchise and subsidiary data is a third area: LeadGenius notes that entity resolution can reveal that 750 franchise locations are controlled by 30 major ownership groups, enabling strategic resource allocation that flat record searches miss entirely. Financial crime and KYC is a fourth major application, where resolving disclosed and inferred relationships between entities (people, businesses, addresses) is the core mechanism behind AML and fraud ring detection.

Entity resolution and data enrichment are sequential, not competing, processes. Entity resolution comes first: it establishes which records represent the same account or contact and merges them into a canonical profile. Only after resolution does enrichment add value — because enriching a duplicate record just creates another copy of stale data, and enriching the wrong entity wastes budget on a phantom account.

The relationship to CRM hygiene (a broader category covering deduplication, decay monitoring, and field standardization) is similarly upstream-downstream. Entity resolution is the matching engine inside a CRM hygiene workflow. The '1:10:100 rule' of data quality captures the cost dynamic well: fixing entity data at ingestion costs $1, correcting it later in the pipeline costs $10, and ignoring it costs $100 in flawed decisions downstream.

For teams using data waterfall enrichment — routing records sequentially through multiple providers — entity resolution is also the step that prevents the same record from being re-enriched by each provider as if it were a new, clean lead. Without it, waterfall enrichment amplifies duplication rather than resolving it.

Signal-based selling depends entirely on resolving which account a signal belongs to before routing it. When Komo monitors buying signals — job change alerts, intent surges, website visits, funding announcements — it must resolve the signaling entity (a domain, a LinkedIn company page, an IP address) back to the correct CRM account before any research or outreach can begin. Without entity resolution, a signal fires to the wrong rep, gets suppressed as a duplicate, or surfaces the wrong contact.

Komo's workflow applies entity resolution at the point of signal ingestion: normalizing company names and domains across sources, matching signals to the canonical account record, and surfacing the relevant contacts within the buying committee. This means a rep's morning briefing contains de-duplicated, correctly attributed signals — not a noisy mix of duplicate firings for the same event.

Because Komo keeps a human on every send that matters, clean entity resolution also prevents embarrassing outreach errors — like sending a 'congrats on the funding' email to the same person twice, or reaching out to an account already in active negotiation with your AE.