FICTIONAL ACQUISITION INTEGRATION

WHEN THE FIELD YOU NEED
DOESN'T EXIST.

Explore how DataMerge uses deterministic evidence to reconstruct a missing relationship, unify two systems, and preserve every exception—without silently guessing.

STEP 01 · LEGACY DATA

The required relationship is missing at the schema level.

The acquired company describes the customer, service, contract, entity, and account—but its schema has no Billing Item ID.

Billing Item ID does not exist in the legacy schema.
LEGACY DATA · 6 REPRESENTATIVE RECORDSNO BILLING ITEM ID COLUMN
CustomerService DescriptionProduct CodeContract DateEntityLegacy Account
Northstar HoldingsManaged Network ServiceNET-4102026-03-15LEG-0154120
Northstar HoldingsHosted Voice PremiumVOI-2202026-03-15LEG-0154130
Northstar HoldingsFiber access – DallasFBR-1052026-04-01LEG-0154120
Harbor Health GroupNetwork monitoringNET-4152026-02-10LEG-0254120
Harbor Health GroupHosted voice seatsVOI-2252026-02-10LEG-0254130
Summit Retail Co.Managed connectivityNET-4102026-05-01LEG-0354120
Step 1 of 6

BEFORE → DATAMERGE → AFTER

From disconnected evidence to an auditable relationship.

BEFORE

Legacy record

Legacy CustomerLegacy ServiceNo Billing Item IDLegacy DateLegacy Account
DATAMERGERelationship reconstructionTarget alignmentApproved rulesValidation
AFTER

Target-ready record

Billing Item IDCurrent ServiceTarget AccountTarget ClassificationStatus + Lineage

DESIGNED FOR SCALE

Built for large, multi-million-row data populations.

The experience uses a compact fictional sample so the relationship is easy to inspect. Production engagements apply approved logic across complete populations in controlled environments.

PROVEN IN A REAL-WORLD ENGAGEMENT

THE ESI ACQUISITION STORY

At Estech Systems, Kym Feltus was brought into a complex reporting and acquisition project involving more than 10 million legacy and current-system records. A critical customer item identifier required by the current system did not exist in the legacy data.

She built the initial rules-driven engine in approximately 48 hours, reconstructed missing relationships from the information that did exist, normalized and unified the populations, and applied ASC 606 and company-specific processing logic. She continued adapting the engine as requirements changed.

ESI communicated that the solution enabled them to meet their third and final past-due reporting deadline.
Project scale, timing, and outcome are reported engagement claims and are not presented as independently audited statistics.

YOUR DATA · YOUR RULES · CONTROLLED EXECUTION

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