About Factual
Factual is an open data platform for application developers that leverages large-scale data aggregation and community exchange. Our focus is on making data more accessible (i.e. cheaper, higher quality, less encumbered) for machines and developers, to drive and accelerate innovation in an unprecedented way. We take on the dirty work of data management – more... and data curation, letting developers focus on higher value and more productive tasks. We provide clean, structured data with complete source transparency to developers on liberal terms.
Factual was founded in 2007 by Gil Elbaz, co-founder of Applied Semantics (which launched ASI's AdSense product). Applied Semantics was acquired by Google in 2003, and Gil continued with Google until he left in 2007 to found Factual. Gil has long believed that making data accessible will enable more innovative tools and applications. To that end, he set out to develop an open data platform in an effort to maximize data accuracy, transparency, and accessibility. He has attracted a great team to help build out his vision.
What is Factual data?
Factual offers thousands of datasets across a variety of topics (with a deep focus in Local data) aggregated from multiple sources, made easily accessible for developers to build web and mobile apps. For example, you will find datasets for millions of U.S. and International local businesses and points of interest, as well as datasets on entertainment, education, government and health. Unlike other data providers, Factual believes data aggregation and curation should be a community effort which drastically brings down the cost of data ownership and management.
Here are a few reasons a developer might use Factual:
Factual is a valuable source of open, living data that can easily be incorporated into web and mobile applications. Factual's APIs and tools make it easy to access our repository of hundreds of thousands of datasets. Your data is part of an ecosystem and "living", which means it is constantly improving through the sum of human and algorithmic activity: crowdsourcing, machine learning, spam detection, validation, and real-time cleaning and structuring. Factual brings together data from multiple sources, so you always know where the data came from and can assess its quality and reliability. If you create an application using our data and allow it to be editable, you are eligible for discounts against fees due for access.
What can you do with the data? And in what ways can I access it?
You can use the data to build web and mobile applications, made easy via our APIs and downloads.
How much does it cost to use Factual data in my application?
Our APIs are free to everyone up to a certain volume. Our downloads fees vary based on a variety of factors. Please see our pricing page for more details.
Factual's datasets are open for edits and we put incentives in place for users of the data to provide quality contributions in exchange for discounts (or free!) access. This feeds into an open data ecosystem - similar to contributing back to the code base of an open source project.
How can I be sure the data is accurate?
While Factual has unique tools to improve the quality of the data over time, we cannot guarantee any data table will be 100% comprehensive and accurate. We hope that with the help of the community and our powerful inferencing and curation tools, our data will become better and more reliable over time.
If I see a website cited as a source for particular fact, does that mean you crawled that company's website to get the data?
No. There are many ways for Factual to know that a specific fact appears on a company's website: users often cite websites as sources of facts when they are making inputs, edits and corrections; 3rd party web caches and search engines independently document the content of web pages; and less frequently, the conclusion that a fact appears on a specific site may be verified by our own crawl. All of our methodologies abide by robots.txt and other standard practices that dictate good behavior on the web.
How does Factual find factual data?
Factual aggregates data from many sources including partners, user community, and the web, and applies a sophisticated machine-learning technology stack to: 1. Learn facts from millions of structured and unstructured sources 2. Clean, standardize, and canonicalize the data 3. Merge, de-dupe, and map entities across multiple sources.
We encourage our partners to provide edits and contributions back to the data ecosystem to reduce the overall transaction costs via exchange. A majority of our partners embrace our open model by providing edits and contributions, enriching the data for everyone.
What are Factual Tables (on Factual.com)?
On the surface, Factual Tables are very much like relational database tables: they're organized into rows and columns, and you can apply basic operations, such as filters and joins, to them. But Factual Tables differ from database tables in one very important way: Each cell in a Factual Table can incorporate multiple inputs entered by users or extracted from the web. These inputs are used to establish a consensus value for the cell. For example, a quick web search for "Napoleon height" returns two inconsistent answers: 5'2" and 5'6½". A Factual Table of the heights of historical figures would collect inputs on Napoleon's height from various sources, and the value displayed would be determined by a consensus algorithm. (By the way, the most correct answer is 5'6½". 5'2" is from the French system, in which the unit of measurement is longer than an Imperial inch.)
Furthermore, Factual Tables support operations at the underlying 'input' level. For example, an input filter can filter out a set of sources or other users who have been deemed unreliable, and the entire table can be re-rendered, ignoring those unwanted inputs entirely. It's a bit like a source code control system which enables viewing of a document at a historical point in time, except in addition to the time dimension, Factual can filter on user, source and other pieces of metadata.
What features do Factual Tables on Factual.com have?
Here are some of the things you can do with Factual Tables:
Search for data within the table.
Sort and filter the data. Download the table as a CSV formatted file. There are a handful of datasets that are not downloadable -- please contact us if you want to access them in that way.
How do you handle data that changes over time?
Data with a time dimension can be dealt with in a couple of ways. An appropriate field may be defined as a component of a schema, e.g. a historical table of average temperature per city per day. Or, it can be left out, as in a table which simply stores current temperature for each city. In this latter case, it would be be appropriate to limit historical inputs to recent history, or to change the aggregation function to "Wiki" which means only the last "edit" is displayed. – less – More from ZoomInfo »
