Find below pioneering examples of big and deep data integration, or at least cases where one data set is generated in a complementary fashion to the other.
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How LinkedIn is coming to measure and understand when developing its services, with the help of its UX team.
Key quotation: "It is common for behavioral data, A/B testing, analytics and predictive models to serve as the exclusive inputs for design...[but we] start to realize that we can’t rely on big data or metrics wholesale to define and understand people. We are serving people, not numbers, and people are complicated. As much data as we have about people, people defy being defined by them."
How a supermarket chain discovered why shoppers’ big weekend trips to the market—one of the key parts of its business—seemed to be disappearing. Plus what it did about it.
Key quotation: "Combining the two approaches can solve many of the problems that each category of data faces on its own. Thick data’s strength comes from its ability to establish hypotheses about why people behave as they do. It cannot help answer questions of “how much,” only “why.” Big Data has the advantage of being largely unassailable because it is generated by the entire customer population rather than a smaller sample size. But it can only quantify human behavior, it cannot explain its motivations. That is to say, it cannot arrive at a “why.”
How Twitter wanted to help music organisations and brands understand the phenomenon of music fandom, to relate better to its users.
Key quotation: "We know what people are Tweeting about; their topics of conversation, the hashtags they use, what videos they are watching. What we did not know was WHY. Why do users use Twitter to follow their interest in music? What makes them flock to the platform and interact with artists and other fans? We wanted to tell a human story rather than leading with big data."
Key quotation: "Through the application of analytics to big data, as well as ethnography (the design and implementation of qualitative field studies to observe cultural patterns) Xerox is answering important questions about congestion."
A hybrid 'ethno-mining' process by Intel Lab researchers: one way of understanding what behavioral tracking technologies might contribute to research leading to actionable insights.
Numbers have qualities too: Experiences with ethno-mining in a study of time use and technology in middle class America.
Key quotations: "Tracking data...can...be used as a component for building relationships with participants, ultimately enabling new narratives from them." "What ethno-mining does particularly well is it extends the social, spatial and temporal scope of research." "The behaviors which manifest themselves through track-able objects tell a partial story—a very small slice of much larger lives." "we are able to use the data combined with our cultural analysis to highlight the heterogeneity of participants."
How Nokia could have benefited from some deep, aka thick, data alongside its big data in China.
Key quotation: "For businesses to form a complete picture, they need both big and thick data because each of them produce different types of insights at varying scales and depths."
The use of network data from longitudinal sensor measures of presence and flows of visitors at the Louvre to provoke qualitative knowledge.
Key quotation: “Quantitative data analysis and visualization techniques will answer some questions but prompt many more…The qualitative view from the staff at the Louvre reinforced the quantitative measures and consolidated our overall knowledge of hyper-congestion at the museum. In other words, the articulation between qualitative insights and sensor measures enabled us to refine our understanding of the phenomenon."