Factual Places Now Available in SIC & NAICS

Factual Places are classified into 440 consumer-facing categories. However, as many of our data partners still employ Standard Industrial Classification (SIC) codes, and their more recent replacement, North American Industry Classification System (NAICS) codes, we have provided a mapping between Factual categories and these Government standards.

The files are tab-delineated text files, and can be opened in Excel. We’ve made them freely available for downloading here:

We hope that these provide a handy utility for those still keying their data on these reference systems.

-Tyler Bell
Categorically Factual

Tyler Bell on Solving Large-Scale Data Problems at Strata

Tyler Bell, Director of Product here at Factual, will be speaking at the upcoming Strata Conference in Santa Clara.  His session, titled “Bigger Than Any One — Solving Large-Scale Data problems with people and Machines”, will be on Wednesday 2/27/13 at 4:50 pm in Ballroom F.

The informatic challenges of 2013 and beyond are bigger than any one company. This presentation provides an overview of a number of recent, successful crowd-sourced and community-driven applications that combine ‘Big Data’ approaches with Community involvement. The speaker dives into the numbers and specific details of Factual’s approach to large-scale, multi-authored data collection and aggregation, and how the company’s data ethos and business positioning dictates both the shape of its technology and its vision of large-scale, collective data ecosystems.  http://strataconf.com/strata2013/public/schedule/detail/27302

Tyler has a post on Strata’s blog illustrating some of what he will address with an interesting example involving Russain railroads.

MIT Technology Review Names Factual One of 50 Disruptive Companies in 2013

I’m excited to announce that Factual has been selected as one of MIT Technology Review’s 2013 50 Disruptive Companies in the field of Internet and Digital Media.  We were nominated by MIT Technology Review’s editors, who look for companies that have demonstrated original and valuable technology over the past year, are bringing that technology to market at significant scale, and are clearly influencing their competitors.

“Factual is making many smart applications possible,” said Jason Pontin, publisher and editor-in-chief of MIT Technology Review.  “By collecting and analyzing large sets of data, it creates stores of knowledge that can inform many kinds of software.”

This is an amazing honor and a testament to the hard work our team has put forth in building one of the deepest, most reliable data stacks in the world.  What started off as a crazy idea has now been shown to provide real, measurable value to lots of customers.  We feel fortunate to have the opportunity to help the world see data in a profoundly different way.

- Gil Elbaz, CEO & Founder

Announcing Two New Attributes for Global Products: Manufacturer and Average Price

Today we’re excited to announce the latest update to our Global Products dataset.  This release includes the addition of two new attributes that many of our partners have been asking for: manufacturer and average price.


The connections between brands and manufacturers may sometimes seem like a complicated web.  By tying together the many brands and products that fall under a single manufacturer or parent company, we’ve made it easier to generate insight about these connections, and about the consumer products marketplace as a whole.

For instance, getting a list of the brands owned by Unilever is now as simple as making a facet call to our API:

preview the data and view the faceted list of brands on the right

Getting a list of products the fall under Unilever brands is just as simple:

preview the data

We’ve started by providing manufacturer data for more than 20 of the top CPG manufacturers and we’ll be rolling out data for additional manufacturers in the near future.

Average Price

When shopping for products online or in a store, it can be difficult to know if you’re getting a good deal.  We’ve eliminated some of the guesswork by providing the average price for the most commonly searched for products.

In addition to getting the average price for a particular product, you can also filter API results by price.  For instance, to find shampoo with an average price under $5, you can use the following query:

preview the data

If you’re the kind of person who likes to spend a little more on their hair care, you can easily get a list of the most premium hair care brands by combining a facet call with an average price and category filter:

preview the data and view the faceted list of brands on the right


We hope you find the addition of these attributes useful and we can’t wait to see what you build with them.  You can explore all our product data, including our new attributes, by visiting our shiny new Data Preview.  Visit our Global Products page to learn more, sign up for an API key, and find links to documentation.

As always, retailers and manufacturers interested in including their product data and links in our Global Products dataset can visit our Merchant Partners page for more information.

John Delacruz
Product Manager, Factual Global Products

Increasing Transparency with Data Preview 2.0

Here at Factual, we have always tried to make our data as accessible as possible. We believe that a key part of accessibility is transparency. After all, data is not really accessible if prospective consumers can’t easily evaluate it. To this end, our data has always been publicly available to explore on our site through our data preview tool. Today we announce Data Preview 2.0 – a set of usability improvements and new features that make it significantly easier for anyone to explore our data and understand what exactly we offer prior to consuming it. Here is a quick tour:

Data Preview has 5 main areas:

  1. Full-Text Search – runs a full text-search across every attribute in the data
  2. Filters – allow you to filter the data down to the exact data you wish to see
  3. Searchable Map – allows you to both visualize geodata and search data via the map
  4. Data Table – shows you the actual data, row by row, that meets your filter criteria
  5. Facets – displays row counts faceted by major attribute values and provides a shortcut to filter by specific facet values


Let’s say I was interested in building an app focused on coffee shops, so I needed Places data on coffee shops. I ran a full-text search for “coffee” in the Global Places data preview. I don’t have a great sense for how many coffee shops there are globally, but it’s good to know my app will be useful around the world.  I live in West LA, so I filtered the data down to the cities of Los Angeles, Santa Monica, and Culver City in California – an area I’m much more familiar with.


I used the map to zero in on Century City and so I could QA the data around my office and look at specific shops that I know exist.  Since my app is much more about smaller coffee shops than big chains, I also filtered out all Starbucks locations.

Now I can see how every coffee shop around my office is represented in Factual data, and I can develop a level of trust prior to using it.

I hope these improvements to Data Preview make it easier for you to examine our data and quickly decide if Factual data meets your needs. So please, dig into our data! Our Global Places data covers 64 million local businesses and other points of interest in 50 countries, with deep attributes on Restaurants, Hotels, and Healthcare Providers.  Our Global Products data covers over 650,000 consumer packaged goods with food nutrition info and ingredients data.  If you like what you see, sign up for an API key or request a download.



-Vikas Gupta, Marketing / Operations