Our methodology describes the processes used to transform business information into structured datasets that can be searched, filtered and evaluated by customers.
This document focuses on process transparency. It does not claim that every record is error-free or that every dataset has been processed using exactly the same source or verification method.
Business information may enter the processing pipeline from different source categories. Incoming records can differ substantially in structure, completeness, formatting and freshness.
Before records are made useful for search and segmentation, fields may need to be mapped into a consistent internal structure.
Records may be checked for structural problems before or during processing.
Standardization helps make records searchable and comparable across different datasets.
Duplicate and near-duplicate records can reduce the usefulness of a business database.
Depending on the dataset, duplicate detection may use one or more identifiers such as email address, telephone number, company information or combinations of record attributes.
Contact-validation methods depend on the type of information available and the dataset being processed.
Email-related checks may include syntax, domain and other deliverability-oriented signals where available. Telephone data may undergo formatting, country-code and consistency checks.
A validation signal should not be interpreted as a permanent guarantee. Contact information can change after a validation event.
Records may be organized using attributes such as:
Classification improves filtering but can contain ambiguity because businesses and job roles do not always fit a single category.
Business data naturally decays over time. Maintenance can include new validation signals, corrections, normalization changes, suppression requests, customer feedback and updated source information.
A meaningful accuracy claim requires a defined test rather than a marketing estimate.
When publishing a LastDatabase benchmark, we intend for the associated report to identify, where applicable:
Until a specific metric has been measured using a documented test, it should not be presented as a verified LastDatabase accuracy rate.
No business-information system can eliminate data decay completely. Common causes include: