From Big Data to Big Analytics: Automated Analytic Platforms for Data Exploration

Jonathan Kroening
Rich Timpone
Yongwei Yang
BigSurv 18 (Big Data Meet Survey Science) conference, Barcelona, Spain(2018)
Google Scholar


As Big Data has altered the face of research, the same factors of Volume, Velocity and Variety used to define it, are changing the opportunities of analytic data exploration as well; thus, the introduction of the term Big Analytics. Improvement in algorithms and computing power provide the foundation to produce automated platforms that can identify patterns in analytic model results beyond simply looking at the patterns in the data itself. Introducing the class of Automated Analysis Insight Exploration Platforms allows conducting tens and hundreds of thousands of statistical models to explore them to identify systematic changes in dynamic environments that would often be missed otherwise. These techniques are designed to extract more value out of both traditional survey as well as Big Data, and is relevant for academic, industry, governmental and NGO exploration of new insights of changing patterns of attitudes and behaviors. This paper discusses the architecture of our Ipsos Research Insight Scout (IRIS) and then provides examples of it in action to identify insights for scientific and practical discovery in public opinion and business data. From the Ipsos Global Advisor Study we show examples from the U.S. withdrawal from the Paris Agreement and the 2016 presidential election. We then show with an example how a research project at Google is leveraging these platforms to inform business decision-making.