Data Big and Small
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Data Big and Small
February 16–17, 2017
San Francisco

SRI International
February 15, 2017

Library Selection
Data for the People: How to Make Our Post-Privacy Economy Work for You (Basic Books, Jan. 2017)
By Andreas Weigend



conference overview
Three years ago, Jeff Jonas told a TTI/Vanguard audience that spatial and temporal information was “superfood” for big data. Since then, we’ve seen that borne out; mobility and IoT have made big data bigger and better than before. But as they get bigger, ensuring they’re better becomes ever more challenging.

But big data isn’t the only data of interest. A story is told about Ingvar Kamprad, the founder of IKEA. A business consultant went to his office for an appointment with him and was told he was probably down at the store checkout. Sure enough, he was manning one of the cash registers. The consultant asked why, and Kamprad replied, “This is the cheapest and the most efficient research. I can ask everyone why they choose it and why they didn’t choose it.” IKEA collects plenty of data, but Kamprad needed to complement it with small data.

Big data is in some ways a misnomer; the most useful big-data analyses often involve finding tiny subsets within big databases. And much of it comes from only a handful of measurements, often coming from small devices. Sparse data, probabilistic and fuzzy data, emergent data, and smart data all come together under the ambiguous rubric of small data.

list of speakers

Gam Dias, First Retail
Creating Possibilities, With Data Big and Small

Lindsey Dillon, Assistant Professor, Department of Sociology, UC Santa Cruz, and Steering Committee Chair, Environmental Data and Governance Initiative

Matt Price, University of Toronto

Emil Eifrem, Neo Technologies
Graph Databases

Ben Horowitz, Co-founder and Partner, Andreessen Horowitz 
The Hard Thing About Hard Data

Claudia Perlich, Dstillery
Data-Driven Marketing

Elan Kriegel, BlueLabs 
From Big Data to the Individual

Jans Aasman, Franz 
Semantic Graph Databases and Analytics

Gourab De, DataRobot
Automated Machine Learning

Eric Haseltine, TTI/Vanguard Advisory Board, & Chris Gilbert, M.D. 
Democratizing Data Discovery

Jeff Jonas, Data Scientist
Context Computing

Jana Eggers, Nara Logics
Synaptic Intelligence for Better Decisions

Peter Brodsky, HyperScience
Artificial Intelligence for the Enterprise

Andreas Weigend, Social Data Lab
Data for the People

Mike O'Neill, TruTags
Tagging Medications for Uniqueness  

Brian David Johnson, Arizona State University 
Sentient Tools 

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