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Intesa Sanpaolo Processes

This project is an exploratory business analytics application that allows the organization to explore and audit risk management data to understand and manage its complexity. The application affords users a visual understanding of how risk is distributed across organizational structures and how activities are distributed among different actors at the company. Additionally, users can identify activities and associated risks without appropriate controls and develop plans to redistribute workflows.

The power behind the design of this application is its minimal interface design that facilitates user interaction. A query status pane allows users to filter, zoom, and highlight specific attributes of the visualizations. Additionally, navigational controls create a visualization-first user experience by hiding all non-visualization components on the screen.

Information is Beautiful Awards 2023 Longlist in the Business Analytics category

View the project here.

CfD Team Members: Paolo Ciuccarelli, Todd Linkner, Estefania Ciliotta,

PhD Students: Matt Blanco, Joli Holmes, Tanisha Rajgor, Andrea Cosentini

Data Embodiment

What affordances, opportunities, and challenges emerge when interpreting data through the body using dance? The Data Dance team has been working with a contemporary dance company in a series of workshops which guide participants through activities aimed at creating space for embodied engagement with data. From these workshops, the team is designing a toolkit to guide other researchers, practitioners, and communities in using movement as inquiry, approaching data together, and fostering data curiosity. Data Dance is part of the larger Data Moves project at the Co-Laboratory for Data Impact that explores participatory embodied data practice more broadly to include dance, theater, and other somatic approaches.

Data Dance team members: Laura Perovich, Ilya Vidrin, Nicole Zizzi,

Data Theatre team members: Rahul Bhargava, Jesse Hinson, Amanda Brea, Victoria Palacin

Data Sonification Archive

This curated collection is part of a broader research endeavor in which data, sonification and design converge to explore the potential of sound in complementing other modes of representation and broadening the publics of data. With visualization still being one of the prominent forms of data transformation, we believe that sound can both enrich the experience of data and build new audiences.

Explore the Sonification Archive

CfD Team Members: Paolo Ciuccarelli, Sara Lenzi, Aashita Jain

COVIC Archive

COVIC is a broad, multi-lingual, multi-cultural view of visualizations created during the global COVID-19 pandemic. The project classifies articles and figures in a format that is available for teaching and research purposes; contains a snapshot of information design practice during the pandemic period; illustrates the range of qualitative and quantitative visualization possibilities; preserves a persistent record of ephemeral online visualization artifacts; provides a portrait of this moment of inflection accelerating the transition from print to online; and represents both a problem space — how can visualization practice be used to address this problem — and a solution space — the techniques being used at different times, in different languages, and in different contexts. If you would like access to the archive, please email the center.

Explore the COVIC Archive.

CfD Team Member: Paolo Ciuccarelli

Other Team Members: Paul Kahn, Hugh Dubberly

Spring 2021 Interns: Devashish Sood, Jonathan Chen, Linda Yan

Polygraphs: Combatting Networks of Ignorance in the Misinformation Age

The world is experiencing a massive, dangerous “infodemic” fuelled by social media. For example, myths and false remedies about coronavirus can cost lives. This project aims to explain how ignorance mushrooms even in groups of ideally rational individuals and how groups can combat it collectively. The team wants to understand how group knowledge and belief (collectively, “attitudes”) form, and if “higher-order” information (e.g. knowing there is a misinformant in the group) improves individual judgment. The basic model of this project, stemming from economics, is that individuals learn from neighbors. The team extends this model in two ways: 1) Build a multi-level, weighted group hierarchy informed by algorithms for identifying influencers and authorities in social networks. 2) Model learning from evidence beyond that which is shared by direct neighbors. This interdisciplinary project employs computer simulations of large, realistic social networks. A few pioneering philosophers now advocate computer simulations as a tool for philosophical investigations. But this proposed simulation workload is not trivially encoded in modern graph analytics engines and requires substantial computational resources to scale. In theory, the results will yield new evidence about the metaphysics of group attitudes and shed light on current debates on the use of higher-order evidence. In turn, the philosophical simulations may provide insights in the deployment of existing economic models of information sharing, challenge assumptions about computational workloads on graphs, and ultimately inform company and government policy.

Explore the project here:

CfD Team Members: Paolo Ciuccarelli, Todd Linkner, Other Team Members: Brian Ball, Alexandros Koliousis, Amil Mohanan

PhD Student: Joli Holmes

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