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November 11, 2015

Carrie Grimes, Google | Stanford Women In Data Science (WiDS) Conference 2015

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Google it: The evolution of search | #WiDSConference
by Marlene Den Bleyker | Nov 13, 2015…

Google, the trailblazer of all search engines, has made a powerful impact over the years in the way users and businesses interact online.

Carrie Grimes, distinguished engineer at Google, has been with the company since its early days, and she caught up with Jeff Frick, host of theCUBE, from the SiliconANGLE Media team, at the Women in Data Science Conference held at Stanford University to explain Google’s processes and the evolution of search.

The new questions

In the past, to extract meaningful data meant using algorithms; however, Grimes stated, “Now we actually have to ask questions, such as how do you make a business decision based on this data.” She believes that blending the computational ability of computer science with the confidence gained from statistics is critical for companies such as Netflix, Amazon or Google to make business-based decisions.

Grimes describes how traditionally the goal is to look at customer and user data and derive value, but now it is also essential to focus on the backend, where data science is really important to help make business decisions. According to Grimes, some of the considerations are: “How do I use data science to pick the optimal point? How much computational power do I put into indexing a tweet versus the value of going out and getting a whole new set of content that is more static?”

Grimes pointed out that you can’t force the algorithms Google uses to index, to crawl and to find the right meaning of content and keywords. She referenced the amount of content people are creating and having to decide whether to invest in compute power or invest in a feature, a cool new rendering or the ability to understand structured data. ‚ÄúThat’s the kind of tradeoff we have internally,‚Äù she said.

New data issues to tackle

Grimes has worked for Google since 2003 and has seen the steady need to progress and adapt. She commented on how industry outsiders don’t understand the pressure from users. As search becomes more intelligent, customer expectations become higher.

When she began her career at Google, she recalled that a great deal of effort went into content that was static, managing scale and moving data around. Now, personalized recommendations and understanding the nuances about knowing who is searching and what the searches mean are a much larger focus for data scientists today than it was 10 years ago.

In This Video
Engineering Fellow, Google