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This policy explains how LastDatabase approaches evidence, research, data-quality claims, technical documentation, JIT verification content, programmatic database pages, corrections and AI-assisted publishing.
Last updated: 19 September 2026
Material factual and quantitative claims should be supported by evidence appropriate to the claim. Marketing language, inventory measurements, verification results and research findings should not be presented as though they are the same type of evidence.
This policy applies to informational content published by LastDatabase where editorial or factual standards are relevant. This can include research reports, benchmarks, methodology documents, data-quality pages, educational articles, comparisons, technical documentation and public explanations of LastDatabase processes.
The policy also guides the factual portions of database, industry, country, category, job-title, technology and other programmatic pages when those pages contain statements that extend beyond the underlying database description.
Transactional interfaces, customer account screens and raw database records are operational content rather than editorial publications, although accuracy and privacy controls can still apply to those systems.
Quantitative claims should be supported by identifiable evidence. The type of evidence should match the type of claim being made.
LastDatabase should not present an estimated marketing figure as though it were the result of a documented statistical test, verification run or research study.
Depending on the subject, appropriate evidence can include:
Original research should provide enough context for readers to understand what was measured and what the result does and does not establish.
Where relevant, a research publication should identify:
A result from one dataset, sample, country, period or record type should not automatically be represented as a result for all LastDatabase data.
Research pages should distinguish the evidence produced by the study from broader product descriptions or commercial claims.
Data quality is not treated as a single universal percentage. Different fields and record types can require different measurements.
Statements about completeness, uniqueness, normalization, freshness, verification or other quality characteristics should identify the relevant measurement when the statement is quantitative.
LastDatabase should not publish a universal accuracy percentage unless the percentage is supported by a documented methodology that justifies applying the result to the stated population.
Contact and business information can change over time. A historical measurement therefore should not be presented as a permanent guarantee about every record.
See the Data Quality and Methodology pages for additional context.
Content describing Just-In-Time verification should distinguish JIT activity from historical collection, database maintenance and other quality processes.
JIT descriptions should identify that applicable checks occur close to delivery or export for supported records and workflows. The exact checks available can depend on record type, product and technical conditions.
Where applicable, JIT-related content may discuss normalization, suppression checks, domain or MX checks, SMTP-related signals, classification or other supported risk signals.
JIT verification should not be described as a guarantee that an email will be delivered, opened or answered, or that a contact detail can never change after verification.
The authoritative public explanation is available on How JIT Works.
Operational inventory counts and published research results answer different questions.
A live or recent database count describes records available or measured within the applicable system at a particular point in time. A research result describes the population, sample, methodology and period defined by that research.
LastDatabase should not silently substitute a current inventory count for the population used in an older study, or present an older research measurement as though it automatically describes the current inventory.
Where a count is time-sensitive, the relevant measurement date should be provided when practical.
When informational content depends on external facts, laws, technical standards, platform behavior or third-party research, primary or authoritative sources should be preferred where reasonably available.
External citations should support the claim for which they are used. A source should not be cited in a way that implies it supports a broader conclusion than the source actually provides.
Data provenance statements should distinguish documented source information from assumptions or general descriptions.
More information about LastDatabase source categories is available on the Data Sources page.
LastDatabase publishes pages covering combinations of data types, countries, industries, categories, job titles, technologies and other targeting concepts.
Programmatic publishing should not be used to invent facts merely because a page template requires content. Claims about a specific dataset should be supported by the underlying dataset, documented platform logic or another appropriate source.
A page title or search-targeting concept does not by itself prove that every displayed record has every characteristic implied by that phrase.
Template content should avoid presenting illustrative examples as real measured statistics unless those measurements have actually been performed.
Material limitations that affect interpretation should be disclosed where relevant.
Technical documentation should describe supported platform behavior as accurately as practical at the time it is published or materially updated.
Instructions for API authentication, data search, customer workflows and CRM integrations should be based on the actual supported implementation rather than hypothetical functionality.
When third-party platforms change their APIs, requirements or policies, documentation can become outdated. Material changes should be reviewed and updated when identified.
Current LastDatabase technical guidance is available through the Documentation Center.
Important research and educational content should identify an author, reviewer or responsible editorial source where appropriate.
Specialist review should be claimed only when a real review has taken place.
LastDatabase should not fabricate author biographies, qualifications, professional experience or reviewer identities.
When a publication represents organizational research rather than an individual's independent work, it can identify LastDatabase as the responsible publishing organization.
Product and provider comparisons should distinguish verifiable factual information from interpretation or LastDatabase's own assessment.
Competitor prices, features, coverage, policies or capabilities should not be invented.
Time-sensitive comparison information should identify an appropriate verification or update date where practical.
When a third party is the authoritative source for its own current product behavior, LastDatabase should prefer that party's official documentation over unsupported secondary descriptions.
LastDatabase sells data products and related services. Publications discussing LastDatabase products therefore have a direct commercial connection to the company.
LastDatabase's own product descriptions, research and comparisons should not be presented as independent third-party evaluations.
Commercial interest does not remove the requirement to support material factual claims with appropriate evidence.
Content about privacy laws, compliance requirements and responsible data use should distinguish general informational guidance from legal advice.
Legal and regulatory requirements can vary by jurisdiction, data type, communication channel and use case. Customers remain responsible for determining the requirements applicable to their own activities.
Related information is available through the Privacy Center, Compliance page and Responsible Data Use guidance.
Automated and AI-assisted tools may be used for tasks such as drafting, organization, analysis, formatting, classification or research assistance.
A material factual claim should not be published solely because an automated system generated it. Important claims should be checked against appropriate evidence before publication.
AI-generated citations, statistics, people, companies, qualifications, test results or technical capabilities should not be treated as factual merely because they appear plausible.
Human review should not be claimed unless a person actually performed the stated review.
Important informational pages should show a publication date or meaningful update date where appropriate.
A material change to methodology, evidence, conclusions, technical instructions or substantive factual content can justify a new update date.
Cosmetic changes such as spacing, typography or layout alone should not be presented as a substantive research update.
Historical measurements should retain enough date context to prevent an older result from being mistaken for a current measurement.
Material factual errors identified in published informational content should be reviewed and corrected where appropriate.
A correction can include updating an inaccurate statement, replacing an outdated source, clarifying a limitation or correcting a technical instruction.
Corrections should not be used to conceal material limitations in research or make historical evidence appear stronger than it was.
To report a potential factual error or request an editorial correction, contact protection@lastdatabase.com .
If you identify a potentially inaccurate factual statement, outdated source or material issue in a LastDatabase research or informational publication, send the page URL and a description of the issue.
Email: protection@lastdatabase.com
