08/21/14 | Uncategorized

Without These People, Your Startup’s Big Data is Completely Useless

You’re supposed to be mining big data to find insights. But how? Thanks to these amazing people, you can learn how to tackle data science for your business.

Betsy Mikel (Women 2.0, Editor)

Each new time you visit a new website, log onto any account or buy something online, you create a whole bunch of this. Companies are dying to get more of it from their customers. But then once they get it, they don’t know what to do with it. This is a highly misunderstood term and only a few rare people know how to work with it.

We’re talking about big data, of course.

Big data has become so big, it’s spread beyond the tech world. When 163-year-old publication New York Times hired a chief data scientist earlier this year, it became clear that even non-technical organizations were hopping on the big data train. To successfully predict what their customers want or how they might behave, companies that know how to mine big data — also know as companies who hire good data scientists — have the advantage.

To do their jobs effectively, data scientists must do a whole lotta dirty data work. The New York Times calls it “data janitor work.” In a recent article, NYT reported that data scientists spend from 50 percent to 80 percent of their time laboriously collecting and prepping data before it can be extracted into digestible insights.

“Data wrangling is a huge — and surprisingly so — part of the job,” Monica Rogati, VP for data science at Jawbone, said in the piece. (Psst… she’s also one of the keynote speakers at our conference! Catch her presentation How to Build Data Products for Data Natives on October 1.)

We know how huge big data is for entrepreneurs, so we’ve put together a kick-ass panel of data scientists to speak on our upcoming panel, How to Take Data Science in the Real World in Your Business. Catch these data scientists on Day 2 at our “How To” conference on October 1.

Sarah Allen

CTO, Mightyverse |Founder, Blazing Cloud | Co-Founder, Railsbridge: Data Science Panel Moderator

 Sarah AllenSarah Allen is a serial innovator with a history of developing leading-edge products, such as After Effects, Shockwave, Flash video, and OpenLaszlo. She has a habit of recognizing great and timely ideas, finding talented teams, and creating compelling software. Last year, Sarah was a Presidential Innovation Fellow at the Smithsonian.

She is currently CTO of Mightyverse, and continues her civic innovation as part of 18F, a new digital services team for the US Government. You can follow her on Twitter at @ultrasaurus.

Anita Lillie

Senior Data Scientist, Practice Fusion: Data Science Panelist

Anita LillieAnita Lillie is a data interface designer, developer, and visualization specialist. For over 12 years, Anita has worked to create visualizations that make data clear and actionable in a wide range of fields, including genetics (Stanford), music (MIT), mobile (Nokia Research), social networks (LinkedIn), and healthcare (Practice Fusion), and most recently as a consultant to some awesome data startups. Anita has a B.S. in Mathematical and Computational Science from Stanford and a Masters from the MIT Media Lab.

Anita currently works on the data science team at Practice Fusion, a health technology startup in San Francisco. When she isn’t working, Anita is usually on a bike trail near Skyline, climbing rocks, or eating desse

Elena Grewal

Senior Data Scientist, AirBnB: Data Science Panelist

Elena GrewalElena Grewal is a Senior Data Scientist at Airbnb. She is leads a team of data scientists responsible for the user online experience and offline experience. The team partners with the product team to understand and optimize all parts of the product, using experimentation in a wide variety of contexts. Examples include testing the impact of different search ranking strategies on the likelihood of finding a desired listing, identity verification on incident rates, and professional photography services on booking rates. Because Airbnb is a complex ecosystem involving both online and offline interactions, traditional A/B testing must be adapted and refined. Prior to working at Airbnb, Elena completed a doctorate in education at Stanford where she built predictive models of friendships in schools and modeled the impact of peers on educational outcomes.

Clare Corthell, Data Scientist & Designer, Mattermark: Data Science Panelist

Clare CorthellClare is a Data Scientist & Designer at Mattermark, a data-driven deal intelligence platform, where she builds technologies that quantify the growth of private companies. She is the originator of The Open Source Data Science Masters, a curriculum for learning Data Science. A Stanford-trained product designer and engineer, she’s founded and worked with early-stage companies in the US, Europe, and East Africa. She’s up early pondering discovery algorithms, information design, computational linguistics, and education systems. Follow her at @clarecorthell.

Grab your conference ticket to catch the panel on October 1



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