This is the kaleidescope.

The BD4R-k provides a holistic perspective of the factors at the intersection of Big Data and resilience building. It offers a dynamic lens to identify and assess those factors, to reflect on their interactions, and to strengthen the role of Big Data in resilience programming.

The color gradients are an important component of the kaleidoscope. The intensity of the colors reflects the emphasis placed on different components by a particular project or initiative: intense colors indicate a higher emphasis placed on that component (for example, through ongoing project activities or demonstrated impact), while less-intense colors suggest lower emphasis or evidence of impact at the time when this model was applied.

Through its color gradients, the kaleidoscope provides a unique ‘snapshot’ of project areas of relative strength or weakness at the intersection of Big Data and resilience building.

These are the layers

The BD4R-k is composed by six distinct layers. Each layer—the components and the interactions among them—is critical to understanding the links between Big Data and resilience building in complex development environments.

Resilience Core

Located at the heart of the BD4R-k, this layer corresponds to the working definition of resilience: the ability of a vulnerable system to cope with, adapt, and potentially transform amid the impacts of shocks and stressors. Ultimately, the use of Big Data and its interactions with the other components of the kaleidoscope should help strengthen these core capacities.

The final, outer layer of the BD4R-k model is again the Resilience Core, emphasizing the need to link the analysis of the different components with the (core) resilience capacities. It provides an opportunity to reflect about how the components of the model, and the feedbacks and interactions among them, are contributing (or not) to build those capacities.

Resilience Foundations

This layer explores the resilience context in which the project takes place, by addressing five key resilience questions: resilience where, of whom, to what, for what purpose and how?

Big Data Basics

This layer identified the 4 “Vs” or key characteristics of Big Data: volume, velocity, variety and veracity (Laney, D. 2001, IBM, n.d). When considered as part of a broader context, these characteristics allow a more in-depth understanding of Big Data’s role, potential and challenges in resilience building.

Resilience Attributes

This layer identifies a set of key attributes of resilient systems: robustness, self-organization, learning, redundancy, rapidity, scale, diversity, flexibility and equity. It provides an opportunity to explore the contributions of Big Data toward each of them (positive contributions as well as potentially negative contributions, recognizing that the use of Big Data could also weaken certain attributes). These attributes are not exhaustive, and complement each other.

BD4R Impact Chain

This layer focuses on the value of Big Data and its role in the achievement of development outcomes. It presents the key stages needed for data to translate into action (i.e. data access, assessment, application to decision making and actionability), building on the components of the “information chain” (Heeks, R. 2005). The analysis moves beyond data availability to consider the capacity of resilience stakeholders to effectively access, understand, and use Big Data to inform pathways of change.

BD4R Enabling Environment

This layer identifies factors that influence (by enabling or by limiting) the role of Big Data in resilience-building processes. These are factors that help shape the specific impact and “uniqueness” of Big Data’s contribution to development.