As a well-known site with more than 300 million users, LinkedIn is a vital social media platform, sometimes thought of as “Facebook for businesses.” For most people, it is a great way to keep in touch with current and former colleagues, and its depth of information is invaluable for anyone who is looking to change jobs or research a company. The site is a treasure trove of information, and it thrives on the data that its users generate.

Yael Garten (pictured), director of data science at LinkedIn Corp., explained that LinkedIn has a strong “culture of data,” where data scientists can grow and thrive, knowing that data itself is valued by the company, as well as their contributions as scientists.

Garten recently joined Lisa Martin (@Luccazara), co-host of theCUBE, SiliconANGLE Media’s mobile live streaming studio, during Stanford Global Women in Data Science Conference, held in Stanford, CA, to discuss the culture of data at LinkedIn and how to keep data scientists successful and happy. (*Disclosure below.)

How LinkedIn uses its data to benefit users

“Much of LinkedIn is a data product,” said Garten. She explained that there are two types of data mainly used: decision science, where the data is for humans to use, and machine learning, where a machine is the ultimate consumer of the data output.

She gave an example of how her team works with data from its users. The data may tell her team that, in some countries, users are primarily using an Android app to access LinkedIn. So the team notes that, and they discuss if they should work to improve their Android app. Is it worth it to invest in this area? Is it of benefit to enough users in this particular country, and in other countries as well?

To help make data scientists productive, LinkedIn has a standard playbook on how to launch its features. It details how to launch, as well as how to measure the success of the launch.

“Having a process that the whole team abides by is very useful,” said Garten. She emphasized that to have a happy, data-driven company, everyone needs to participate in a community where data is discussed; one that is data informed, as well as data-driven.

Watch the complete video interview below, and be sure to check out more of SiliconANGLE and theCUBE’s coverage of the Stanford Global Women in Data Science (WiDS) Conference. (*Disclosure: TheCUBE is a media partner at the conference. Neither Stanford nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo by SiliconANGLE