Welcome

The Big Data for Resilience kaleidoscope (BD4R-k) is a new analytical model designed to help practitioners and decision makers gain a better understanding of the role of Big Data in resilience building processes. It helps to visualize the links between Big Data and resilience, to assess areas of strength and weakness, and to identify gaps and opportunities in order to strengthen resilience programming.

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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.

  • 1. Resilience Core
  • 2. Resilience Foundations
  • 3. Big Data Basics
  • 4. Resilience Attributes
  • 5. BD4R Data Impact Chain
  • 6. BD4R Enabling Environment

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.

  • 1. Resilience Core
  • 2. Resilience Foundations
  • 3. Big Data Basics
  • 4. Resilience Attributes
  • 5. BD4R Data Impact Chain
  • 6. BD4R Enabling Environment

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.

  • 1. Resilience Core
  • 2. Resilience Foundations
  • 3. Big Data Basics
  • 4. Resilience Attributes
  • 5. BD4R Data Impact Chain
  • 6. BD4R Enabling Environment

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?

  • 1. Resilience Core
  • 2. Resilience Foundations
  • 3. Big Data Basics
  • 4. Resilience Attributes
  • 5. BD4R Data Impact Chain
  • 6. BD4R Enabling Environment

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.

  • 1. Resilience Core
  • 2. Resilience Foundations
  • 3. Big Data Basics
  • 4. Resilience Attributes
  • 5. BD4R Data Impact Chain
  • 6. BD4R Enabling Environment

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.

  • 1. Resilience Core
  • 2. Resilience Foundations
  • 3. Big Data Basics
  • 4. Resilience Attributes
  • 5. BD4R Data Impact Chain
  • 6. BD4R Enabling Environment

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.

  • 1. Resilience Core
  • 2. Resilience Foundations
  • 3. Big Data Basics
  • 4. Resilience Attributes
  • 5. BD4R Data Impact Chain
  • 6. BD4R Enabling Environment

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.

These are the layer detail controls.

Click once on the layer's circle to reveal details. Click again to turn layer off. Again to return to natural state (no details).

These are the kaleidoscope controls.

Click Rotate to change the mouse to rotate tool, and Pan to change it to the move tool - click and drag on kaleidescope to activate.

Random Rotation.

Clicking this will activate a randomized rotation animation.

Zoom

Adjusting this will zoom the kaleidoscope in and out. Use this to take a closer look at the kaleidoscope's components.

Drag & Rotate

Click a layer to drag and rotate.

Definitions and Examples

Click a shape to reveal data.

Meaningful Connections.

Click to see the interactions that take place between selected parts of the kaleidoscope. This close-up view will help you to take a ‘deeper dive’ into the connections that can help strengthen the role of Big Data in resilience building, to discover new interactions, and to identify areas that could be strengthened as part of resilience initiatives.

Continue

Thank you for taking the tutorial. Please click to continue to the kaleidoscope.

See For Yourself

Replay this Tutorial.

Clicking this will re-play the introduction animation.

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  • Resilience Core
  • Resilience Foundations
  • Big Data Basics
  • Resilience Attributes
  • BD4R Data Impact Chain
  • BD4R Enabling Environment

Now What?

1. Select Resilience Capacity

Focus your analysis on Big Data’s contribution to one core resilience capacity at a time: cope, adapt or transform.

2. Rotate the Layers

Explore how different components of the BD4R-k contribute to the capacity selected.

3. Analyze the Connections

Reflect and discuss with your peers how these different components interact with each other. This cross-level analysis is crucial to strengthen the role of Big Data in resilience programming, and to inform decision making. Questions could include:

  • How are the different components identified in the kaleidoscope contributing to the resilience capacities of the system (such as its ability to cope with, adapt and/or transform)?
  • What components of the model would need to be strengthened in order to strengthen resilience capacities and achieve the desired development outcome?
  • Which interactions should be prioritized through programming/implementation?; are there unintended (positive and negative) effects from the use of Big Data?

Resilience of Whom?

This question identifies the system that the project or initiative focuses on (e.g., individual, household, community, institution), as well as specific people who benefit from the project (e.g., men, women, boys and girls).

About

The BD4R-k was developed as part of the Big Data for Resilience initiative of the International Institute for Sustainable Development (IISD). Further details about the model and its application can be found in the Big Data for Resilience Storybook.

The BD4R-k builds on different resilience conceptual approaches, including the Resilience Assessment Benchmarking and Impact Toolkit (RABIT) developed by the University of Manchester, England, and the Dynamic Resilience Wheel (DREW) developed by Lutheran World Relief (LWR).

In Action

Explore the BD4R-k in action! See the model applied to Big Data for Resilience projects of different international organizations.

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View Kaleidoscope Website

Contact

Angelica V. Ospina, PhD

Senior Researcher, Resilience Program

International Institute for Sustainable Development (IISD)

aospina@iisd.ca