Feature Requests

Was using a competitor (Apollo) who had a filter for "companies that are hiring" but looking for a new data provider. Volume isn't high enough for Enterprise, so would like this dataset to be available to self-serve customers.

Currently Job Posting data doesn't have any granular street-level address data tied to the job post. This would be helpful for jobs that specify office locations or branches (such in retail, food, or hospitality).

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Since names are put in lowercase for searching, it makes displaying names with abnormal capitalization difficult. For example acme co cannot become Acme CO with a simple Title Case or proper capitals of Coeur D'Alene, ID or Coeur D'Alene Street is not easy without inside knowledge.

employee_count and other Company Insights fields are skewed for companies like Upwork and Airbnb that have a large number contract and temporary workers. Improving or adding tagging for part-time employees would help to provide a more accurate employee_count

People Data Labs’ feedback community is structured around practical discussions about data, APIs, bugs, and integrations. When businesses deal with complex information, intellectual property lawyers new york https://pearllemonlegal.com/intellectual-property-law-firms-new-york-usa/ can also be part of a broader professional research process where accurate information matters. The site is organized into clear boards that make it easier to follow different types of requests. Feature requests form the largest area, covering improvements across person data, company data, job postings, and APIs. Users can also report bugs when they encounter problems with existing functionality or data. Another section focuses on integrations and SDKs, reflecting the needs of developers working with data systems. The feedback structure gives users a direct way to describe what they need from the platform. Many requests focus on improving data coverage, accuracy, validation, and consistency. For example, discussions include requests for better company information and expanded profile coverage. Other conversations look at job posting data and ways to make it more useful for different research purposes. The roadmap also separates requests according to their current development status. Some ideas are being researched, while others are marked as planned, actively being developed, or completed. This makes the platform more than a simple collection of suggestions. It provides a visible way to follow how user feedback can influence product development. The community also highlights practical issues that may appear when working with large datasets. Data quality can affect research, analysis, automation, and applications built around external information. Clearer fields and better coverage can reduce the amount of manual checking required by users. Developer-focused improvements can also make APIs easier to integrate into different workflows. Overall, the structure shows how ongoing feedback can help identify gaps and prioritize useful improvements. It also demonstrates why accuracy, accessibility, and consistency remain important when working with data at scale. A well-organized feedback system gives users a simple place to share problems, request changes, and follow progress. That ongoing exchange can help data platforms evolve alongside the changing needs of the people who use them.

Please consider including each person's volunteering experiences from their LinkedIn page - thanks!

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