How to ‘do’ data empowerment

In this post we’re looking at ways to ‘do’ data empowerment. Our aim is to provide those who design, implement, evaluate or fund data for development initiatives with practical guidance on data empowerment.

The following piece was first published on the Data Empowerment blog and was originally published on February 3, 2020.  

In our first two posts we outlined an initial definition of the data empowerment concept and discussed three practical examples of data empowerment. In this post we’re looking at ways to ‘do’ data empowerment. Our aim is to provide those who design, implement, evaluate or fund data for development initiatives with practical guidance on data empowerment.

Some of the practices described below are easy to implement, others require specific expertise. Most of them are currently done by a very few development actors. And each of these practices could make a significant difference in the lives of people your data for development initiative intends to serve.

So here are the five things you can do to integrate data empowerment in your work.

1. Start with ‘do no harm’

Before anything else, be clear about your responsible data strategy if and when you are doing projects that handle data.

There is a tendency in the data for development space to excitedly jump right to data management questions when conceptualizing and incubating new initiatives. However, we need to first think of what could go wrong, who would be negatively impacted and how we can minimize and mitigate these risks. If you do only one thing, ensure your data initiative doesn’t run the risk of doing harm.

A number of organizations have published frameworks, guidelines and tools — including UN Global Pulse, Oxfam and GIZ — that you can use to think through and develop your own responsible data strategy. You will also find a wealth of resources on the responsible data community platform, including a handbook written for development practitioners.

Use these resources as a starting point. They will help you to apply responsible data principles, especially in those contexts where legal frameworks around privacy and data protection are weak or non-existent.

2. Understand and address power dynamics

Be aware that data production and use will impact people in different ways. An initiative may empower one group while reducing the power of another. We need to be mindful of the ways that data initiatives can relieve or worsen existing power inequalities. As much as we can, our job as we design, implement, evaluate or fund data for development initiatives is to ensure that we understand and facilitate these power dynamics.

One way of doing this is by conducting an analysis of the power structures and dynamics that impact people’s ability to control data about them and data of relevance to them and their wellbeing. SIDA has published a practical guide on power analysis and Oxfam provides an overview of what power is and how to analyze it. John Gaventa’s Power Cube is a useful tool that looks at levels, spaces and forms of power. Rosemary McGee’s book Power, Empowerment and Social Change contains frameworks and methods to analyze power.

Even the most rigorous and thorough upfront analysis and planning, however, will not prevent every unintended (negative) consequence. Therefore, we must constantly monitor the changing power dynamics at play during implementation.

Data empowerment is a process and not an end state. Some people will gain, and others will lose power. Expect opposition and resistance from those who stand to lose.

3. Treat people as active users, not passive providers

Treating people as active users of data instead of passive data providers is at the heart of data empowerment.

Instead of thinking of data for development initiatives as a way to help a single decision-maker to derive better insights from aggregated and centralized data (often in the form of a data dashboard), data empowerment is about acknowledging the agency of people affected by a particular challenge. It’s about working with them as active agents of change rather than passive providers of ‘data breadcrumbs’.

This requires helping people to access, understand and use the data that impacts them and their communities. It also requires working with people that are close to a particular problem and who have a much more granular understanding of the context and can use their knowledge to take action.

There is no single framework, method or tool to guide that shift in thinking, but learning from those who’ve successfully done it is a good place to start.

4. Support collective data production and ownership

Beyond just supporting people to use data, data empowerment initiatives can put them in control of data collection through collective, community-driven data production and ownership.

Illustrations from Vincent Beck

Some of the most impactful projects are those in which data is compiled by the people that are affected by the issue at hand. Such citizen-generated data initiatives are a collective, organized and direct response by people to ‘monitor, demand or drive change on issues that affect them’. They are responding to a specific issue for which data was either not available, inaccessible, incomplete, inaccurate or just not timely enough. I Paid A Bribe, an online corruption-reporting platform, and HarassMap, a collection of data on sexual harassment in Egypt, are among the most well-known citizen-generated data initiatives.

As is the case with active data use, there is no single path to follow to empower people when it comes to data production and ownership. It requires a very different way of doing things including long-term investments in areas like data infrastructure literacy to help support, sustain and scale these initiatives.

Too much of the scarce funding for data for development work is invested in the production of data that benefits the few, not the many — further entrenching inequality. It’s important to keep in mind that behind every investment there is a choice about which data to source, collect or produce, as well as who is involved in the process and who is left out. Funders must prioritize projects that strengthen people’s opportunities to benefit from the data collected and analyzed. And program designers should support people to use the data that is collected and, better still, to be active participants in project development and data collection processes.

5. Make the data you collect accessible to others

If it can be done responsibly (see section 1) make the data you’ve collected publicly accessible, or at least provide access to the people from whom it is collected.

Making the data you collect more widely accessible signals your willingness to engage with people outside of your organization. It’s a prerequisite for external data use and gives people the confidence to engage on issues that can be addressed through data. Data access in and of itself will not lead to data use, let alone data empowerment, but unless people have access to data, they don’t even have the opportunity to engage.

There are a number of detailed online resources on opening up and sharing data, such as the Open Data Handbook that provides guidance on the various legal, social and technical issues. What’s more, there is a lot of work going on at the moment to identify appropriate data sharing mechanisms for personal and other sensitive data that cannot be made publicly available, such as data collaboratives. The extent to which these alternative approaches are suited to broaden data access vs further limiting it to a small group of actors remains to be seen.

Data empowerment asks for more from everyone involved

The data empowerment practices outlined in this post can be integrated at various phases of a data for development initiative, at different stages of the data lifecycle and by all actors involved.

Data empowerment asks more of those who design, implement, evaluate or fund data for development initiatives, including: more awareness of potential negative consequences; more investment in understanding and addressing power dynamics; and more involvement of people in the decisions of how their data is collected, used and shared.

In future blog posts we will explore different ways to regulate and control the use of individuals’ data. We will ask how people can collectively influence how their data is collected in the first place. We will also take a closer look at new trends in the data for development space and how they might influence the data empowerment concept over the next few years.

[1] This is a revised version of an earlier collection of data empowerment practices presented to a group of development practitioners at the UN Innovation Bootcamp in Beirut in 2018. We would like to thank Ana Brandusescu, Nanjira Sambuli, and Giulio Quaggiotto for their suggestions on the first version.

The following piece was first published on the Data Empowerment blog and was originally published on February 3, 2020.  

 

In our first two posts we outlined an initial definition of the data empowerment concept and discussed three practical examples of data empowerment. In this post we’re looking at ways to ‘do’ data empowerment. Our aim is to provide those who design, implement, evaluate or fund data for development initiatives with practical guidance on data empowerment.

Some of the practices described below are easy to implement, others require specific expertise. Most of them are currently done by a very few development actors. And each of these practices could make a significant difference in the lives of people your data for development initiative intends to serve.

So here are the five things you can do to integrate data empowerment in your work.

1. Start with ‘do no harm’

Before anything else, be clear about your responsible data strategy if and when you are doing projects that handle data.

There is a tendency in the data for development space to excitedly jump right to data management questions when conceptualizing and incubating new initiatives. However, we need to first think of what could go wrong, who would be negatively impacted and how we can minimize and mitigate these risks. If you do only one thing, ensure your data initiative doesn’t run the risk of doing harm.

A number of organizations have published frameworks, guidelines and tools — including UN Global Pulse, Oxfam and GIZ — that you can use to think through and develop your own responsible data strategy. You will also find a wealth of resources on the responsible data community platform, including a handbook written for development practitioners.

Use these resources as a starting point. They will help you to apply responsible data principles, especially in those contexts where legal frameworks around privacy and data protection are weak or non-existent.

2. Understand and address power dynamics

Be aware that data production and use will impact people in different ways. An initiative may empower one group while reducing the power of another. We need to be mindful of the ways that data initiatives can relieve or worsen existing power inequalities. As much as we can, our job as we design, implement, evaluate or fund data for development initiatives is to ensure that we understand and facilitate these power dynamics.

One way of doing this is by conducting an analysis of the power structures and dynamics that impact people’s ability to control data about them and data of relevance to them and their wellbeing. SIDA has published a practical guide on power analysis and Oxfam provides an overview of what power is and how to analyze it. John Gaventa’s Power Cube is a useful tool that looks at levels, spaces and forms of power. Rosemary McGee’s book Power, Empowerment and Social Change contains frameworks and methods to analyze power.

Even the most rigorous and thorough upfront analysis and planning, however, will not prevent every unintended (negative) consequence. Therefore, we must constantly monitor the changing power dynamics at play during implementation.

Data empowerment is a process and not an end state. Some people will gain, and others will lose power. Expect opposition and resistance from those who stand to lose.

3. Treat people as active users, not passive providers

Treating people as active users of data instead of passive data providers is at the heart of data empowerment.

Instead of thinking of data for development initiatives as a way to help a single decision-maker to derive better insights from aggregated and centralized data (often in the form of a data dashboard), data empowerment is about acknowledging the agency of people affected by a particular challenge. It’s about working with them as active agents of change rather than passive providers of ‘data breadcrumbs’.

This requires helping people to access, understand and use the data that impacts them and their communities. It also requires working with people that are close to a particular problem and who have a much more granular understanding of the context and can use their knowledge to take action.

There is no single framework, method or tool to guide that shift in thinking, but learning from those who’ve successfully done it is a good place to start.

4. Support collective data production and ownership

Beyond just supporting people to use data, data empowerment initiatives can put them in control of data collection through collective, community-driven data production and ownership.

Illustrations: Vincent Beck

Some of the most impactful projects are those in which data is compiled by the people that are affected by the issue at hand. Such citizen-generated data initiatives are a collective, organized and direct response by people to ‘monitor, demand or drive change on issues that affect them’. They are responding to a specific issue for which data was either not available, inaccessible, incomplete, inaccurate or just not timely enough. I Paid A Bribe, an online corruption-reporting platform, and HarassMap, a collection of data on sexual harassment in Egypt, are among the most well-known citizen-generated data initiatives.

As is the case with active data use, there is no single path to follow to empower people when it comes to data production and ownership. It requires a very different way of doing things including long-term investments in areas like data infrastructure literacy to help support, sustain and scale these initiatives.

Too much of the scarce funding for data for development work is invested in the production of data that benefits the few, not the many — further entrenching inequality. It’s important to keep in mind that behind every investment there is a choice about which data to source, collect or produce, as well as who is involved in the process and who is left out. Funders must prioritize projects that strengthen people’s opportunities to benefit from the data collected and analyzed. And program designers should support people to use the data that is collected and, better still, to be active participants in project development and data collection processes.

5. Make the data you collect accessible to others

If it can be done responsibly (see section 1) make the data you’ve collected publicly accessible, or at least provide access to the people from whom it is collected.

Making the data you collect more widely accessible signals your willingness to engage with people outside of your organization. It’s a prerequisite for external data use and gives people the confidence to engage on issues that can be addressed through data. Data access in and of itself will not lead to data use, let alone data empowerment, but unless people have access to data, they don’t even have the opportunity to engage.

There are a number of detailed online resources on opening up and sharing data, such as the Open Data Handbook that provides guidance on the various legal, social and technical issues. What’s more, there is a lot of work going on at the moment to identify appropriate data sharing mechanisms for personal and other sensitive data that cannot be made publicly available, such as data collaboratives. The extent to which these alternative approaches are suited to broaden data access vs further limiting it to a small group of actors remains to be seen.

Data empowerment asks for more from everyone involved

The data empowerment practices outlined in this post can be integrated at various phases of a data for development initiative, at different stages of the data lifecycle and by all actors involved.

Data empowerment asks more of those who design, implement, evaluate or fund data for development initiatives, including: more awareness of potential negative consequences; more investment in understanding and addressing power dynamics; and more involvement of people in the decisions of how their data is collected, used and shared.

In future blog posts we will explore different ways to regulate and control the use of individuals’ data. We will ask how people can collectively influence how their data is collected in the first place. We will also take a closer look at new trends in the data for development space and how they might influence the data empowerment concept over the next few years.

[1] This is a revised version of an earlier collection of data empowerment practices presented to a group of development practitioners at the UN Innovation Bootcamp in Beirut in 2018. We would like to thank Ana Brandusescu, Nanjira Sambuli, and Giulio Quaggiotto for their suggestions on the first version.

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