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LastDatabase uses structured data-processing and quality-control procedures to improve the consistency, usability and freshness of business contact information.
Business data changes continuously. People change jobs, companies change domains, email accounts become inactive and telephone numbers can be reassigned. For that reason, data quality should be treated as a measurable process rather than an unsupported guarantee.
Quality processing can differ by dataset and record type. Depending on the information available, processing may include:
These procedures are intended to improve data quality. They should not be interpreted as a guarantee that every individual field is current or accurate at every moment.
Where an email field is present, quality processing may consider signals such as:
An email address that passes a validation check can still become inactive later. Deliverability can also be affected by recipient systems, reputation, filtering, sending practices and changes that occur after validation.
Telephone data can require different validation procedures from email information. Depending on the dataset, processing may include:
Telephone numbers can be disconnected, transferred or reassigned. Therefore, historical validation does not guarantee that a number remains associated with the same person or organization indefinitely.
Business information obtained from different sources can use different spellings, abbreviations and formatting conventions. Normalization helps make the database searchable and comparable.
Examples may include:
Duplicate records can distort record counts and reduce the practical value of a dataset.
Depending on the dataset, duplicate detection may consider individual identifiers or combinations of attributes such as email, telephone, company and other record fields.
Exact and near-duplicate detection are not necessarily identical processes, and deduplication results can vary according to the available fields.
B2B contact information naturally changes over time. Common causes include:
Maintenance can therefore involve new information, corrections, normalization improvements, updated validation signals and suppression requests.
LastDatabase does not believe a universal accuracy percentage should be published without a documented test supporting that number.
A meaningful benchmark should disclose information such as:
When LastDatabase publishes measured research, the methodology and limitations should accompany the reported results so readers can evaluate the evidence.
Data quality also depends on correcting inaccurate information when it is identified.
Individuals and organizations can contact LastDatabase to request review, correction, removal or suppression of relevant information.
For additional information about how data is processed and organized, see:
Data quality is not a single permanent score. It can vary by field, source history, country, record type, and the time since a record was last processed.
Package pages should be read together with their coverage details and available fields. Buyers should use a test segment, maintain their own suppression lists, and validate information for their intended use.
We do not present an unmeasured marketing estimate as a verified LastDatabase accuracy rate. When we publish a benchmark, it should identify its sample, testing period, method, and limitations.
