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The private sector has been using dashboards for quite some time, but international development organizations face challenges when it comes to identifying the right data dashboards and accompanying systems for decision-making.

Our May 29th, 2015, Technology Salon (sponsored by The Rockefeller Foundation) explored data dashboards and data visualization for improved decision making with lead discussants John DeRiggi, Senior Data Architect, DAI; Shawna Hoffman, Associate Manager, Evaluation and Learning at The MasterCard Foundation; Stephanie Evergreen, Evergreen Data.

In short, we learned at the Salon that most organizations are struggling with the data dashboard process. There are a number of reasons that dashboards fail. They may never get off the ground, they may not deliver what was promised, they may deliver but no one uses them, or they may deliver but the data is poor and bad decisions are made. Using data for better decision-making is an ongoing process – not a task or product to complete and then relegate to automation. Just getting a dashboard up and running doesn’t guarantee that it’s a success – it’s critical to look deeper to see if the data and its visualization have actually improved decisions and how. Like with any ICT tool, user centered design and ongoing iteration are key. Successful dashboards are organized, useful, include targets, and have trends and predictions. Organizational culture and change management are critical in the process.

Points discussed in detail*:

1) Ask whether you actually need a dashboard

The first question to ask is whether a dashboard is needed or possible. One discussant, who specializes in data visualization, noted that she’s often brought in because someone wants to do data visualization, and she then needs to work backwards with the organization through a number of other preparatory steps before getting to the part on data visualization. It’s critical to have data dashboard discussions with different parts of the organization in order to understand real needs and expectations. Often people will say they need a dashboard because they want to make better decisions, noted another lead discussant. “But what kind of decisions, and what information is needed to make those decisions? Where does that information come from? Who will get it?”

2) Define the audience and type of dashboard

People often think that they can create one dashboard that will fulfill everyone’s needs. As one discussant put it, they will say the audience for the dashboard is “everyone – all decision makers at all levels!” In reality most organizations will need several dashboards for different levels of decision-making. It’s important to know who will own it, use it, keep it up, and collect the data. Will it be internal or externally facing? Discussing all of this is a key part of the process of thinking through the dashboard. As one discussant outlined, dashboards can be strategic, analytical or operational. But it’s difficult for them to be all three at once. So organizations need to come to a clear understanding of their data and decision-making needs. What information, if available, would help different teams at different levels with their decision making? One dashboard can’t be everything to everyone. Creating a charter that outlines what the dashboard project is and what it aims to do is a way to help avoid mission creep, said one discussant.

3) Work with users to develop your dashboard

To start off the process, it’s important to clearly identify the audience and find out what they need – don’t assume you know, recommended one discussant. But also, as a Salon participant pointed out, don’t assume that they know either. Have a conversation where their and your expertise comes together. “The higher up you go, the less people may understand about data. One idea is to just take the ‘data’ out of the conversation. Ask decision-makers what questions they are trying to answer, what problems they are trying to solve. Then find out how to collect and visualize the data that helps them answer their questions,” suggested another participant. Create ownership and accountability at all levels – with users, with staff who will input the data, with project managers, with grantees – you need cooperation from all levels noted others. Clear buy-in will also help with data quality. If people see the results of their data coming out in a data visualization, they may be more inclined to provide quality data. One way to involve users is to gather different teams to talk about their data and to create ‘entity relationship models’ together. “People can get into the weeds, and then you can build a vocabulary for the organization. Then you can use that model to build the system and create commonality across it,” said one discussant. Another idea is to create paper prototypes of dashboards with users so that they can envision them better.

4) Dashboards help people engage with the data they’ve collected

A dashboard is a window into your data, said one participant. In some cases, seeing their data visualized can help staff to see that they have been providing poor quality data. “People didn’t realize how bad their data was until they saw their dashboard,” said one discussant. Another noted that people may disagree with what the data tells them in the dashboard and feel motivated to provide better data. On the other hand, they may realize that their data was actually good, and instead they need to improve ineffective programs. A danger is that putting a dashboard on top of bad data shines a light on the data, said one participant, and this might create an incentive for people to manipulate their data.

5) Don’t be over-ambitious

Align the dashboard with indicators that link to strategic goals and directions and stay focused, recommended one discussant. There is often a temptation to over-complicate with tons of data and visuals. But extraneous data leads to misinterpretation or distraction. Dashboards should make complex data available in an accessible way to users, she said. You can always make more visuals if needed, but you want a concise story told in the data and visuals that you’re depicting. Determine what is useful, productive and credible and leave out what is exciting but extraneous. “Don’t try to have 30 indicators.”

6) Be clear about your data categories and indicators

Rolling up data from a large number of different programs into a dashboard is a huge challenge, especially if different sites or programs are using different data models. For example, if one program is describing an activity as a ‘workshop’ and the other uses ‘training session,’ said one discussant, you have a problem. A Salon participant explained that her organization started with shallow but important common denominators across programs. Over time they aim to go deeper to begin looking at outcomes and impact.

7) Think through how you’ll sustain the dashboard and related system(s)

One discussant said that her organization established three different teams to work on the dashboard process: a) Metrics – Where do we have credible representative data? Where do we have indicators but we don’t have data? b) Plumbing: Where are the data sources? How do they feed into each other? Who is responsible, and can this be aggregated up? And c) Visualization: What visual would help different decision makers make their decisions? Depending on where the organization is in its stage of readiness and its existing staff capacities, different combinations of skill sets may be required to supplement existing ones. Data experts can help teams understand what is possible, yet program or management teams and other dashboard users also need to be involved so that they can identify the questions they are trying to answer with the data and the dashboard.

8) Don’t underestimate the time/resources needed for a functional dashboard

People may not realize that you can’t make a dashboard without data to support it, noted one participant. “It’s like a power point presentation… a power point doesn’t just appear out of nowhere. It’s a result of conversations, research, data, design and more. But for some reason, people think a dashboard will just magically create itself out of thin air.” People also seem to think you can create and launch a dashboard and then put it on autopilot, but that is not the case. The dashboard will need constant changes and iteration, and there will be continual work to keep it up. The questions being asked will also likely change over time and so the dashboard may need to shift to take this into consideration. Time will be required to get buy-in for the dashboard and its use. One Salon participant said that in her former organization, they met quarterly to present, use and discuss the dashboard, and it took about 2 years in order for it to become useful and for people to become invested in it. It’s very important, said one participant, to ensure that management knows that the dashboard is not a static thing – it will need ongoing attention and management.

9) Be selective when it comes to the technology

People tend to think that dashboards are just visual, said a Salon participant. They think they are really cool, business solution platforms. Often senior leadership has seen been pitched something really expensive and complicated, with all kinds of bells and whistles, and they may think that is what they need. It’s important to know where your organization is in terms of capacity before determining which technology would be the best fit, however, noted one discussant. She counseled organizations to use whatever they have on hand rather than bringing in new software that takes people 6 months to learn how to use. Simple excel-based dashboards might be the best place to start, she said.

10) Legacy systems can be combined with new data viz capabilities

One discussant shared how his company’s information system, which was set up over 15 years ago, did not allow for the creation of APIs. This meant that the team could not build derivative software products from their massive existing database. It is too expensive to replace the entire system, and building modules to replace some of it would lead to fragmenting the user experience. So the team built a thin web service layer on top of the existing system. This exposed the data to friendly web formats from which developers could build interactive products.

11) Be realistic about “real time” and “data quality”

One question that came up was around the the level of evidence needed to make good decisions. Having perfect data served up into a perfect visualization is utopian, said one Salon participant. The idea is that we could have ‘real time’ data to inform our decisions, she explained, yet it’s hard to quality check data so quickly. “So at what level can we say we’ll make decisions based on a level of certainty – is it when we feel 80% of the data is good quality? Do we need to lower that to 60% so that we have timely data? Is that too low?” Another question was around the kinds of decisions that require ‘real time’ data versus those that could be made based on data that is 3 to 6 months old. Salon participants said this will depend on the kind of program and the type of decision. The sector in which one is working may also determine the level of comfort with real time and with data quality – for example, the humanitarian sector may need more timely data and accept a lower level of verification whereas the development sector may be the opposite.

Another point was that dashboards should include error bars and available metadata, as well as in some cases a link to raw data for those who want to dig into the data and understand what is behind the dashboard. Sometimes the dashboard process will highlight that there is simply not much quality data available for some programs in some countries. This can be an opportunity to work with staff on the ground to strengthen capacity to collect it.

12) Relax

As one discussant said, “much of the concern about data quality is related to our own hang-ups as data nerds and what we feel comfortable putting out there for people to use to make decisions. We always say ‘we need more research.’” But here the context is different. “Stakeholders and management want the answer. We need to just put the data out there with some caveats to help them.” One way to offer more context for a dashboard is creating a dashboard report that provides some narrative alongside the visualization. Dashboards should also show trends, not only what has happened already, she said. People need to see trends towards the future so that decisions can be made. It was also pointed out that a dashboard shouldn’t be the only basis for decisions. Like a car dashboard – these data dashboards signal that something is changing but you still need to look under the hood to see what it is. The dashboard should trigger questions – it should be a launch pad for discussion.

13) Organizational culture is a huge part of this process

The internal culture and people’s attitudes towards data are embedded into how an organization operates, noted one Salon participant. This varies depending on the type of organization – an evaluation focused organization vs. a development organization vs. a contractor vs. a humanitarian organization, for example. Outside consultants can help you to build a dashboard, but it will be critical to have someone managing organizational change on the inside who knows the current culture and where the organization is aiming to go with the dashboard process. The process is getting easier, however. Many organizations are thirsty for data now, noted one lead discussant. “Often the research or evaluation team create a dashboard and send it to the management team, and then everyone loves it and wants one. People are ready for it now.”

More resources on data dashboards and visualization.

Special thanks to our lead discussants and to our hosts for this Salon! If you’d like to join our Salon discussions in the future, sign up at the Technology Salon site.

*Salons run under Chatham House Rule, so no attribution has been made in this post.

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Screen Shot 2014-05-08 at 9.36.00 AMDebate and thinking around data, ethics, ICT have been growing and expanding a lot lately, which makes me very happy!

Coming up on May 22 in NYC, the engine room, Hivos, the Berkman Center for Internet and Society, and Kurante (my newish gig) are organizing the latest in a series of events as part of the Responsible Data Forum.

The event will be hosted at ThoughtWorks and it is in-person only. Space is limited, so if you’d like to join us, let us know soon by filling in this form. 

What’s it all about?

This particular Responsible Data Forum event is an effort to map the ethical, legal, privacy and security challenges surrounding the increased use and sharing of data in development programming. The Forum will aim to explore the ways in which these challenges are experienced in project design and implementation, as well as when project data is shared or published in an effort to strengthen accountability. The event will be a collaborative effort to begin developing concrete tools and strategies to address these challenges, which can be further tested and refined with end users at events in Amsterdam and Budapest.

We will explore the responsible data challenges faced by development practitioners in program design and implementation.

Some of the use cases we’ll consider include:

  • projects collecting data from marginalized populations, aspiring to respect a do no harm principle, but also to identify opportunities for informational empowerment
  • project design staff seeking to understand and manage the lifespan of project data from collection, through maintenance, utilization, and sharing or destruction.
  • project staff that are considering data sharing or joint data collection with government agencies or corporate actors
  • project staff who want to better understand how ICT4D will impact communities
  • projects exploring the potential of popular ICT-related mechanisms, such as hackathons, incubation labs or innovation hubs
  • projects wishing to use development data for research purposes, and crafting responsible ways to use personally identifiable data for academic purposes
  • projects working with children under the age of 18, struggling to balance the need for data to improve programming approaches, and demand higher levels of protection for children

By gathering a significant number of development practitioners grappling with these issues, the Forum aims to pose practical and critical questions to the use of data and ICTs in development programming. Through collaborative sessions and group work, the Forum will identify common pressing issues for which there might be practical and feasible solutions. The Forum will focus on prototyping specific tools and strategies to respond to these challenges.

What will be accomplished?

Some outputs from the event may include:

  • Tools and checklists for managing responsible data challenges for specific project modalities, such as sms surveys, constructing national databases, or social media scraping and engagement.
  • Best practices and ethical controls for data sharing agreements with governments, corporate actors, academia or civil society
  • Strategies for responsible program development
  • Guidelines for data-driven projects dealing with communities with limited representation or access to information
  • Heuristics and frameworks for understanding anonymity and re-identification of large development data sets
  • Potential policy interventions to create greater awareness and possibly consider minimum standards

Hope to see some of you on the 22nd! Sign up here if you’re interested in attending, and read more about the Responsible Data Forum here.

 

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The NYC Technology Salon on February 28th examined the connection between bigger, better data and resilience. We held morning and afternoon Salons due to the high response rate for the topic. Jake Porway, DataKind; Emmanuel Letouzé, Harvard Humanitarian Initiative; and Elizabeth Eagen, Open Society Foundations; were our lead discussants for the morning. Max Shron, Data Strategy; joined Emmanuel and Elizabeth for the afternoon session.

This post summarizes key discussions from both Salons.

What the heck do we mean by ‘big data’?

The first question at the morning salon was: What precisely do we mean by the term ‘big data’? Participants and lead discussants had varying definitions. One way of thinking about big data is that it is comprised of small bits of unintentionally produced ‘data exhaust’ (website cookies, cellphone data records, etc.) that add up to a dataset. In this case, the term big data refers to the quality and nature of the data, and we think of non-sampled data that are messy, noisy and unstructured. The mindset that goes with big data is one of ‘turning mess into meaning.’

Some Salon participants understood big data as datasets that are too large to be stored, managed and analyzed via conventional database technologies or managed on normal computers. One person suggested dropping the adjective ‘big,’ forgetting about the size, and instead considering the impact of the contribution of the data to understanding. For example, if there were absolutely no data on something and 1000 data points were contributed, this might have a greater impact than adding another 10,000 data points to an existing set of 10 million.

The point here was that when the emphasis is on big (understood as size and/or volume), someone with a small data set (for example, one that fits into an excel sheet) might feel inadequate, yet their data contribution may be actually ‘bigger’ than a physically larger data set (aha! it’s not the size of the paintbrush…). There was a suggestion that instead of talking about big data we should talk about smart data.

How can big data support development?

Two frameworks were shared for thinking about big data in development. One from UN Global Pulse considers that big data can improve a) real-time awareness, b) early warning and c) real-time monitoring. Another looks at big data being used for three kinds of analysis: a) descriptive (providing a summary of something that has already happened), b) predictive (likelihood and probability of something occurring in the future), and c) diagnostic (causal inference and understanding of the world).

What’s the link between big data and resilience?

‘Resilience’ as a concept is contested, difficult to measure and complex. In its most simple definition, resilience can be thought of as the ability to bounce back or bounce forward. (For an interesting discussion on whether we should be talking about sustainability or resilience, see this piece). One discussant noted that global processes and structures are not working well for the poor, as evidenced from continuing cycles of poverty and glaring wealth inequalities. In this view, people are poor as a result of being more exposed and vulnerable to shocks, at the same time, their poverty increases their vulnerability, and it’s difficult to escape from the cycle where over time, small and large shocks deplete assets. An assets-based model of resilience would help individuals, families and communities who are hit by a shock in one sphere — financial, human, capital, social, legal and/or political — to draw on the assets within another sphere to bounce back or forward.

Big data could help this type of an assets-based model of resilience by predicting /helping poor and vulnerable people predict when a shock might happen and preparing for it. Big data analytics, if accessible to the poor, could help them to increase their chances of making better decisions now and for the future. Big data then, should be made accessible and available to communities so that they can self-organize and decrease their own exposure to shocks and hazards and increase their ability to bounce back and bounce forward. Big data could also help various actors to develop a better understanding of the human ecosystem and contribute to increasing resilience.

Can ivory tower big data approaches contribute to resilience?

The application of big data approaches to efforts that aim to increase resilience and better understand human ecosystems often comes at things from the wrong angle, according to one discussant. We are increasingly seeing situations where a decision is made at the top by people who know how to crunch data yet have no way of really understanding the meaning of the data in the local context. In these cases, the impact of data on resilience will be low, because resilience can only truly be created and supported at the local level. Instead of large organizations thinking about how they can use data from afar to ‘rescue’ or ‘help’ the poor, organizations should be working together with communities in crisis (or supporting local or nationally based intermediaries to facilitate this process) so that communities can discuss and pull meaning from the data, contextualize it and use it to help themselves. They can also be more informed what data exist about them and more aware of how these data might be used.

For the Human Rights community, for example, the story is about how people successfully use data to advocate for their own rights, and there is less emphasis on large data sets. Rather, the goal is to get data to citizens and communities. It’s to support groups to define and use data locally and to think about what the data can tell them about the advocacy path they could take to achieve a particular goal.

Can data really empower people?

To better understand the opportunities and challenges of big data, we need to unpack questions related to empowerment. Who has the knowledge? The access? Who can use the data? Salon participants emphasized that change doesn’t come by merely having data. Rather it’s about using big data as an advocacy tool to tell the world to change processes and to put things normally left unsaid on the table for discussion and action. It is also about decisions and getting ‘big data’ to the ‘small world,’ e.g., the local level. According to some, this should be the priority of ‘big data for development’ actors over the next 5 years.

Though some participants at the Salon felt that data on their own do not empower individuals; others noted that knowing your credit score or tracking how much you are eating or exercising can indeed be empowering to individuals. In addition, the process of gathering data can help communities understand their own realities better, build their self-esteem and analytical capacities, and contribute to achieving a more level playing field when they are advocating for their rights or for a budget or service. As one Salon participant said, most communities have information but are not perceived to have data unless they collect it using ‘Western’ methods. Having data to support and back information, opinions and demands can serve communities in negotiations with entities that wield more power. (See the book “Who Counts, the power of participatory statistics” on how to work with communities to create ‘data’ from participatory approaches).

On the other hand, data are not enough if there is no political will to make change to respond to the data and to the requests or demands being made based on the data. As one Salon participant said: “giving someone a data set doesn’t change politics.”

Should we all jump on the data bandwagon?

Both discussants and participants made a plea to ‘practice safe statistics!’ Human rights organizations wander in and out of statistics and don’t really understand how it works, said one person. ‘You wouldn’t go to court without a lawyer, so don’t try to use big data unless you can ensure it’s valid and you know how to manage it.’ If organizations plan to work with data, they should have statisticians and/or data scientists on staff or on call as partners and collaborators. Lack of basic statistical literacy is a huge issue amongst the general population and within many organizations, thought leaders, and journalists, and this can be dangerous.

As big data becomes more trendy, the risk of misinterpretation is growing, and we need to place more attention on the responsible use of statistics and data or we may end up harming people by bad decisions. ‘Everyone thinks they are experts who can handle statistics – bias, collection, correlation’ these days. And ‘as a general rule, no matter how many times you say the data show possible correlation not causality, the public will understand that there is causality,’ commented one discussant. And generally, he noted, ‘when people look at data, they believe them as truth because they include numbers, statistics, science.’ Greater statistical literacy could help people to not just read or access data and information but to use them wisely, to understand and question how data are interpreted, and to detect political or other biases. What’s more, organizations today are asking questions about big data that have been on statisticians’ minds for a very long time, so reaching out to those who understand these issues can be useful to avoid repeating mistakes and re-learning lessons that have already been well-documented.

This poor statistical literacy becomes a serious ethical issue when data are used to determine funding or actions that impact on people’s lives, or when they are shared openly, accidentally or in ways that are unethical. In addition, privacy and protection are critical elements in using and working with data about people, especially when the data involve vulnerable populations. Organizations can face legal action and liability suits if their data put people at harm, as one Salon participant noted. ‘An organization could even be accused of manslaughter… and I’m speaking from experience,’ she added.

What can we do to move forward?

Some potential actions for moving forward included:

  • Emphasis with donors that having big data does not mean that in order to cut costs, you should eliminate community level processes related to data collection, interpretation, analysis, and ownership;
  • Evaluations and literature/documentation on the effectiveness of different tools and methods, and when and in which contexts they might be applicable, including things like cost-benefit analyses of using big data and evaluation of its impact on development/on communities when combined with community level processes vs used alone/without community involvement — practitioner gut feelings are that big data without community involvement is irresponsible and ineffective in terms of resilience, and it would be good to have evidence to help validate or disprove this;
  • More and better tools and resources to support data collection, visualization and use and to help organizations with risk analysis, privacy impact assessments, strategies and planning around use of big data; case studies and a place to share and engage with peers, creation of a ‘cook book’ to help organizations understand the ingredients, tools, processes of using data/big data in their work;
  • ‘Normative conventions’ on how big data should be used to avoid falling into tech-driven dystopia;
  • Greater capacity for ‘safe statistics’ among organizations;
  • A community space where frank and open conversations around data/big data can occur in an ongoing way with the right range of people and cross-section of experiences and expertise from business, data, organizations, etc.

In conclusion?

We touched upon all types of data and various levels of data usage for a huge range of purposes at the two Salons. One closing thought was around the importance of having a solid idea of what questions we trying to answer before moving on to collecting data, and then understanding what data collection methods are adequate for our purpose, what ICT tools are right for which data collection and interpretation methods, what will done with the data/what is the purpose of collecting data, how we’ll interpret them, and how data will be shared, with whom, and in what format.

See this growing list of resources related to Data and Resilience here and add yours!

Thanks to participants and lead discussants for the fantastic exchange, and a big thank you to ThoughtWorks for hosting us at their offices for this Salon. Thanks also to Hunter Goldman, Elizabeth Eagen and Emmanuel Letouzé for their support developing this Salon topic, and to Somto Fab-Ukozor for support with notes and the summary. Salons are held under Chatham House Rule, therefore no attribution has been made in this post. If you’d like to attend future Salons, sign up here!

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This is a cross-post by Duncan Edwards from the Institute of Development Studies. Duncan and I collaborated on some sessions for the Open Development stream at September’s Open Knowledge Conference, and we are working on a few posts to sum up what we discussed there and highlight some lingering thoughts on open development and open data. This post was originally published on the Open Knowledge Foundation blog on October 21, 2013

by Duncan Edwards

I’ve had a lingering feeling of unease that things were not quite right in the world of open development and ICT4D (Information and communication technology for development), so at September’s Open Knowledge Conference in Geneva I took advantage of the presence of some of the world’s top practitioners in these two areas to explore the question: How does “openness” really effect change within development?

Inspiration for the session came from a number of conversations I’ve had over the last few years. My co-conspirator/co-organiser of the OKCon side event “Reality check: Ethics and Risk in Open Development,” Linda Raftree, had also been feeling uncomfortable with the framing of many open development projects, assumptions being made about how “openness + ICTs = development outcomes,” and a concern that risks and privacy were not being adequately considered. We had been wondering whether the claims made by Open Development enthusiasts were substantiated by any demonstrable impact. For some reason, as soon as you introduce the words “open data” and “ICT,” good practice in development gets thrown out the window in the excitement to reach “the solution”.

A common narrative in many “open” development projects goes along the lines of “provide access to data/information –> some magic occurs –> we see positive change.” In essence, because of the newness of this field, we only know what we THINK happens, we don’t know what REALLY happens because there is a paucity of documentation and evidence.

It’s problematic that we often use the terms data, information, and knowledge interchangeably, because:
Data is NOT knowledge.
Data is NOT information.
Information is NOT knowledge.
Knowledge IS what you know. It’s the result of information you’ve consumed, your education, your culture, beliefs, religion, experience – it’s intertwined with the society within which you live.

Data cake metaphor developed by Mark Johnstone.

Understanding and thinking through how we get from the “openness” of data, to how this affects how and what people think, and consequently how they MIGHT act, is critical in whether “open” actually has any additional impact.

At Wednesday’s session, panellist Matthew Smith from the International Development Research Centre (IDRC) talked about the commonalities across various open initiatives. Matthew argued that a larger Theory of Change (ToC) around how ‘open’ leads to change on a number of levels could allow practitioners to draw out common points. The basic theory we see in open initiatives is “put information out, get a feedback loop going, see change happen.” But open development can be sliced in many ways, and we tend to work in silos when talking about openness. We have open educational resources, open data, open government, open science, etc. We apply ideas and theories of openness in a number of domains but we are not learning across these domains.

We explored the theories of change underpinning two active programmes that incorporate a certain amount of “openness” in their logic. Simon Colmer from the Knowledge Services department at the Institute of Development Studies outlined his department’s theory of change of how research evidence can help support decision-making in development policy-making and practice. Erik Nijland from HIVOS presented elements of the theory of change that underpins the Making All Voices Count programme, which looks to increase the links between citizens and governments to improve public services and deepen democracy. Both of these ToCs assume that because data/information is accessible, people will use it within their decision-making processes.

They also both assume that intermediaries play a critical role in analysis, translation, interpretation, and contextualisation of data and information to ensure that decision makers (whether citizens, policy actors, or development practitioners) are able to make use of it. Although access is theoretically open, in practice even mediated access is not equal – so how might this play out in respect to marginalised communities and individuals?

What neither ToC really does is unpack who these intermediaries are. What are their politics? What are their drivers for mediating data and information? What is the effect of this? A common assumption is that intermediaries are somehow neutral and unbiased – does this assumption really hold true?

What many open data initiatives do not consider is what happens after people are able to access and internalise open data and information. How do people act once they know something? As Vanessa Herringshaw from the Transparency and Accountability Initiative said in the “Raising the Bar for ambition and quality in OGP” session, “We know what transparency should look like but things are a lot less clear on the accountability end of things”.

There are a lot of unanswered questions. Do citizens have the agency to take action? Who holds power? What kind of action is appropriate or desirable? Who is listening? And if they are listening, do they care?

Linda finished up the panel by raising some questions around the assumptions that people make decisions based on information rather than on emotion, and that there is a homogeneous “public” or “community” that is waiting for data/information upon which to base their opinions and actions.

So as a final thought, here’s my (perhaps clumsy) 2013 update on Gil Scott Heron’s 1970 song “The Revolution will not be televised”:

“The revolution will NOT be in Open data,
It will NOT be in hackathons, data dives, and mobile apps,
It will NOT be broadcast on Facebook, Twitter, and YouTube,
It will NOT be live-streamed, podcast, and available on catch-up
The revolution will not be televised”

Heron’s point, which holds true today, was that “the revolution” or change, starts in the head. We need to think carefully about how we get far beyond access to data.

Look out for a second post coming soon on Theories of Change in Open, and a third post on ethics and risk in open data and open development.

And if you’re interested in joining the conversation, \sign up to our Open Development mailing list.

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Here’s a recap of my panel talk at the Engineers Without Borders, Canada, Annual Policy Forum. (A summary of the wider discussions on Open Government and Community and Economic Development at the Forum is here)

Slide01Open data are having some impact as seen in 4 key areas (according to what I heard at July’s International Open Government Data Conference). These are:

  • economic growth/entrepreneurship
  • transparency, accountability and governance
  • improved resource allocation and provision of services
  • connecting data dots and telling stories the public needs to know

Open data should be part of the public’s right to information, not a service that government can decide whether to provide or not. Open government should include open attitudes, open ways of being, not only open data and use of technology. It should be inclusive and seek to engage those who do not normally participate, as well as those who are already active. It should go further than data about public services and also encompass those aspects that may be uncomfortable and politically charged.

Slide04

Opening data is only a first step – and there are still big gaps. ‘Open’ does not automatically mean accessible, useful, relevant or accountable. Although new ICTs offer huge potential, focusing too much on technologies and data can marginalize a range of voices from the current discussion about (and implementation of) open government initiatives and processes. Much about these processes is currently top down and focused at the international and national levels, or sometimes district level. Community level data would be a huge step towards local accountability work

Slide06We can address the gaps. First we need to understand, acknowledge and design for the barriers and/or challenges in each particular environment, including the barriers of ICT access for some groups; e.g:

  • lack of connectivity and electricity
  • cost of devices, cost of connection
  • lack of time and resources to participate
  • low education levels, low capacity to interpret data
  • power and culture, apathy, lack of incentives and motivation, lack of interest and/or fatalism, disempowerment
  • poor capacity and/or lack of interest by duty bearers/governments (or particular individuals within government) to respond to citizen demand for services or transparency/accountability

We also need to support:

  • consultations with and engagement of citizens in different places, different sectors, economic levels, etc., from the very beginning of the open government process
  • better understanding of what is important to citizens and communities
  • generation of awareness and demand, better local ownership, expectations of responsive government
  • champions within local and national government, strengthened capacity and motivation to collect and share data; strengthened coordination
  • space for dialogue and discussion among citizens, communities, civil society organizations and governments

Slide10Government responsiveness matters. A lot. So when working in open government we need to ensure that if there are ways to input and report, that there is also responsiveness, willingness on government side and the right attitude(s) or it will not succeed.

Open Data/Open Government portals are not enough. I’ve heard that donors know more about the open government portal in Kenya than Kenyan NGOs, Kenyan media and Kenyan citizens.  It’s important to work with skilled intermediaries, infomediaries and civil society organizations who have a transparency mandate to achieve bigger picture, social motivation, large-scale awareness and education, and help create demand from public. But these intermediaries need to strive to be as objective and unbiased as possible. If there is no response to citizen demand, the initiative is sunk. You may either go back to nothing, increase apathy, or find people using less peaceful approaches.

Great tech examples exist! But…. how to learn from them, adapt them or combine them to address the aforementioned barriers? Initiatives like Huduma, U-Report, I Paid a Bribe have gotten great press. We heard from Ugandan colleagues at the Open Knowledge Festival that people will use SMS and pay for it when the information they get is relevant; but we still need to think about who is being left out or marginalized and how to engage them.

Slide08We need to also consider age-old (well, 1970s) communication for development (C4D) and ‘educación popular’ approaches. New ICT tools can be added to these in some cases as well. For example, integrating SMS or call-in options make it possible for radio stations to interact more dynamically with listeners. Tools like FrontlineSMS Radio allow tracking, measuring and visualization of listener feedback.  The development of ‘critical consciousness’ and critical thinking should be a key part of these processes.

Existing successful social accountability tools, like community scorecardsparticipatory budget advocacysocial auditsparticipatory videoparticipatory theater and community mapping have all been used successfully in accountability and governance work and may be more appropriate tools in some cases than Internet and mobile apps to generate citizen engagement around open data.

Combining new ICTs with these well-established approaches can help take open data offline and bring community knowledge and opinions online, so that open data is not strictly a top-down thing and so that community knowledge and processes can be aggregated, added to or connected back to open data sets and more widely shared via the Internet (keeping in mind a community’s right also to not have their data shared).

A smart combination of information and communication tools – whether Internet, mobile apps, posters, print media, murals, song, drama, face-to-face, radio, video, comics, community bulletin boards, open community fora or others – and a bottom-up, consultative, ‘educación popular’ approach to open data could help open data reach a wider group of citizens and equip them not only with information but with a variety of channels through which to participate more broadly in the definition of the right questions to ask and a wider skill set to use open data to question power and push for more accountability and positive social change. Involved and engaged media or “data journalists” can help to bring information to the public and stimulate a culture of more transparency and accountability. Responsiveness and engagement of government and opportunities for open dialogue and discussion among various actors in a society are also key. Community organizing will remain a core aspect of successful civic participation and accountability efforts.

[Photo credits: (1) Phone charging in a community with limited electricity, photo by youth working with the Youth Empowerment through Arts and Media (YETAM) program in Senegal; (2) Youth training session during YETAM project Cameroon, photo by me (3) Gaps in open data and open government work, diagram by Liza Douglas, Plan International USA; (4) Local government authority and communities during discussions in Cameroon, photo by me; (5) Youth making a map of their community in Cameroon, photo by Ernest Kunbega]

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The November 14, 2012, Technology Salon NYC (TSNYC) focused on ways that ICTs can support work with children who migrate. An earlier post covers the discussion around Population Council’s upcoming ‘Adolescent Girls on the Move’ report. The current post focuses on the strategic use of data visualization for immigration advocacy, based on opening points from Brian Root and Enrique Piracés of Human Rights Watch (HRW).

Visualizing the US Detention Network and the transfers between detention centers.

The project

The HRW initiative used data to track and visualize the movement of people through the US immigration detention system after noticing that U.S. Immigration and Customs Enforcement (ICE) was moving people very freely without notifying their families or attorneys. HRW was aware of the problem but not its pervasiveness. The team obtained some large data sets from the US government via Freedom of Information Act (FOIA) requests. They used the data to track individuals’ routes through the immigration detention system, eventually mapping the whole system out at both aggregate levels and the level of individual. The patterns in the data informed HRW’s advocacy at the state and federal levels. In the process, HRW was able to learn some key lessons on advocacy and the importance of targeting data visualizations to specific advocacy purposes.

Data advocacy and storytelling

The data set HRW obtained included over 5.4 million records of 2.3 million people, with 10-12 variables. The team was able to connect these records to individuals, which helped tell a meaningful story to a broad audience. By mapping out all the US facilities involved and using geo-location to measure the distance that any individual had been transferred, the number of times an individual from Country X in Age Range X was transferred from one facility to another was visible, and patterns could be found. For example, often people on the East Coast were transferred to Texas, where there is a low ratio of immigration lawyers per detainee.

Even though the team had data and good stories to tell with the data, the two were not enough to create change. Human rights are often not high priority for decision makers, but budgeting is; so the team attached a cost to each vector that would allow HRW to tell decision makers how much was being spent for each of these unnecessary transfers.

They were also able to produce aggregated data at the local level. They created a state dashboard so that people could understand the data at the state level, since the detention facilities are state-run. The data highlighted local-level inefficiencies. The local press was then able to tell locally relevant stories, thus generating public opinion around the issue. This is a good example of the importance of moving from data to story telling in order to strengthen advocacy work.

HRW conveyed information and advocated both privately and publicly for change in the system. Their work resulted in the issuing of a new directive in January 2012.

FOIA and the data set

Obtaining data via FOIA acts can be quite difficult if an organization is a known human rights advocate. For others it can be much easier. It is a process of much letter sending and sometimes legal support.

Because FOIA data comes from the source, validation is not a major issue. Publishing methodologies openly helps with validation because others can observe how data are being used. In the case of HRW, data interpretations were shared with the US Government for discussion and refutation. The organization’s strength is in its credibility, thus HRW makes every effort to be conservative with data interpretation before publishing or making any type of statement.

One important issue is knowing what data to ask for and what is possible or available. Phrasing the FOI request to obtain the right data can be a challenge. In addition, sometimes agencies do not know how to generate the requested information from their data systems. Google searches for additional data sets that others have obtained can help. Sites such as CREW (Citizens for Responsibility and Ethics in Washington), which has 20,000 documents open on Scribd, and the Government Attic project, which collects and lists FOI requests, are attempting to consolidate existing FOI information.

The type of information available in the US could help identify which immigration facilities are dealing with the under-18 population and help speculate on the flow of child migrants. Gender and nationality variables could also tell stories about migration in the US. In addition, the data can be used to understand probability: If you are a Mexican male in San Jose, California, what is the likelihood of being detained? Of being deported?

The US Government collects and shares this type of data, however many other countries do not. Currently only 80 countries have FOI laws. Obtaining these large data sets is both a question of whether government ministries are collecting statistics and whether there are legal mechanisms to obtain data and information.

Data parsing

Several steps and tools helped HRW with data parsing. To determine whether data were stable, data were divided by column and reviewed, using a SHELL. Then the data were moved to a database (MySQL), however other programs may be a better choice. A set of programs and scripts was built to analyze the data, and detention facilities were geo-located using GeoNames. The highest quality result was used to move geo-location down to the block level and map all the facilities. Then TileMill and Quantum GIS (QGIS) were used to make maps and ProtoViz (now D3) was used to create data visualizations.

Once the data were there, common variables were noted throughout the different fields and used to group and link information and records to individuals. Many individuals had been in the system multiple times. The team then looked at different ways that the information could be linked. They were able to measure time, distance and the “bounce factor”, eg.., how many times an individual was transferred from one place to the other.

Highlighting problematic cases: One man’s history of transfers.

Key learning:

Remember the goal. Visualization tools are very exciting, and it is easy to be seduced by cool visualizations. It is critical to keep in mind the goal of the project. In the HRW case the goal was to change policy, so the team needed to create visualizations that would specifically lead to policy change. In discussions with the advocacy team, they defined that the visualizations needed to 1) demonstrate the complexity 2) allow people to understand the distance 3) show the vast numbers of people being moved.

Privacy. It is possible to link together individual records and other information to tell a broader story, but one needs to be very careful about this type of information identifying individuals and putting them at risk. For this reason not all information needs to be shared publicly for advocacy purposes. It can be visualized in private conversations with decision makers.

Data and the future

Open data, open source, data visualization, and big data are shaping the world we are embedded in. More and more information is being released, whether through open data, FOIA or information leaks like Wikileaks. Organizations need to begin learning how to use this information in more and better ways.

Many thanks to the Women’s Refugee Commission and the International Rescue Committee for hosting the Salon.

The next Technology Salon NYC will be coming up soon. Stay tuned for more information, and if you’d like to receive notifications about future salons, sign up for the mailing list!

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New technologies are changing the nature of monitoring and evaluation, as discussed in our previous Salon on the use of ICTs in M&E. However, the use of new technologies in M&E efforts can seem daunting or irrelevant to those working in low resource settings, especially if there is little experience or low existing capacity with these new tools and approaches.

What is the role of donors and other intermediaries in strengthening local capacity in communities and development partners to use new technologies to enhance monitoring and evaluation efforts?

On August 30, the Rockefeller Foundation and the Community Systems Foundation (CSF) joined up with the Technology Salon NYC to host the second in a series of 3 Salons on the use of ICTs in monitoring and evaluating development outcomes and to discuss just this question. Our lead discussants were: Revati Prasad from Internews, Tom O’Connell from UNICEF and Jake Watson from the International Rescue Committee. (Thanks Jake for stepping in at the last minute!)

We started off with the comment that “Many of us are faced with the “I” word – in other words, having to demonstrate impact on the ground. But how can we do that if we are 4 levels removed from where change is happening?” How can organizations and donors or those sitting in offices in Washington DC or New York City support grantees and local offices to feed back more quickly and more accurately? From this question, the conversation flowed into a number of directions and suggestions.

1) Determine what works locally

Donor shouldn’t be coming in to say “here’s what works.” Instead, they should be creating local environments for innovation. Rather than pushing things down to people, we need to start thinking from the eyes of the community and incorporate that into how we think and what we do. One participant confirmed that idea with a concrete example. “We went in with ideas – wouldn’t SMS be great… but it became clear that SMS was not the right tool, it was voice. So we worked to establish a hotline. This has connected [the population] with services, it also connects with a database that came from [their] own needs and it tracks what they want to track.” As discussed in the last Salon, however, incentive and motivation are critical. “Early on, even though indicators were set by the community, there was no direct incentive to report.” Once the mentioned call center connected the reporting to access to services, people were more motivated to report.

2) Produce local, not national-level information

If you want to leverage technology for local decision-making, you need local level information, not broad national level information. You also need to recognize that the data will be messy. As one participant said, we need to get away from the idea of imperfect data, and instead think: is the information good enough to enable us to reach that child who wasn’t reached before? We need to stop thinking of knowledge as discrete chunks that endure for 3-4 years. We are actually processing information all the time. We can help managers to think of information as something to filter and use constantly and we can help them with tools to filter information, create simpler dashboards, see bottlenecks, and combine different channels of information to make decisions.

3) Remember why you are using ICTs in M&E

We should be doing M&E in order to achieve better results and leveraging technologies to achieve better impact for communities. Often, however, we end up doing it for the donor. “Donors get really excited about this multicolored thing with 50,000 graphs, but the guy on the ground doesn’t use a bit of it. We need to let go.” commented one participant. “I don’t need to know what the district manager knows. I need to know that he or she has a system in place that works for him or her. My job is to support local staff to have that system working. We need to focus on helping people do their jobs.”

4) Excel might be your ‘killer app’

Worldwide, the range of capacities is huge. Sometimes ICT sounds very sexy, but the greatest success might be teaching people how to use Excel, how to use databases to track human rights violations and domestic violence or setting up a front-end and a data entry system in a local language.

5) Technology capacity doesn’t equal M&E capacity

One participant noted that her organization is working with a technology hub that has very good tech skills but lacks capacity in development and M&E. Their work over the past year has been less about using technology and more about working with the hub to develop these other capacities: how to conduct focus groups, surveys, network analysis, developing toolkits and guides. There’s often excitement on the ground – ‘We can get data in 48 hours! Wow! Let’s go!’ However creating good M&E surveys to be used via technology tools is difficult. One participant expressed that finding local expertise in this area is not easy, especially considering staff turnover. “We don’t always have M&E experts on the ground.” In addition, “there is an art to polls and survey trees, especially when trying to take them from English into other languages. How do you write a primer for staff to create meaningful questions.”

6) Find the best level for ICTs to support the process

ICTs are not always the best tool at the community or district level, given issues of access, literacy, capacity, connection, electricity, etc., but participants mentioned working in blended ways, eg., doing traditional data collection and using ICTs to analyze the data, compile it, produce localized reports, and working with the community to interpret the information for better decision-making. Others use hand-drawn maps, examine issues from the community angle and then incorporate that into digital literacy work and expression work, using new technology tools to tell and document the communities’ stories.

7) Discover the shadow systems and edge of network

One participant noted that people will comply and they will move data through the system as requested from on high, but they simultaneously develop their own ways of tracking information that are actually useful to them. By discovering these ‘shadow systems’, you can see what is really useful. This ‘edge of network’ is where people with whom headquarters doesn’t have contact live and work. We rely on much of their information to build M&E systems yet we don’t consult and work with them often enough. Understanding this ‘edge of network’ is critical to designing and developing good M&E systems and supporting local level M&E for better information and decision-making.

8 ) The devil is in the details

There are many M&E tools to choose from and each has its pros and cons. Participants mentioned KoBo, RapidSMSNokia Data GatheringFrontlineSMS and Episurveyor. While there is a benefit to getting more clean data and getting it in real-time, there will always be post-processing tasks. The data can, however, be thrown on a dashboard for better decision-making. Challenges exist, however. For example, in Haiti, as one participant commented, there is a 10% electrification rate, so solar is required. “It’s difficult to get a local number with Clickatell [an SMS gateway]; you can only get an international number. But getting a local number is very complicated. If you go that route, you need a project coordinator. And if you are using SMS, how do you top off the beneficiaries so that they can reply? The few pennies it costs for people to reply are a deterrent. Yet working with telecom providers is very time-consuming and expensive in any country. Training local staff is an issue – trying to train everyone on the ICT package that you are giving them. You can’t take anything for granted. People usually don’t have experience with these systems.” Literacy is another stumbling block, so some organizations are looking at Interactive Voice Response (IVR) and trying to build a way for it to be rapidly deployed.

9) Who is the M&E for?

Results are one thing, but as one participant noted, “part of results measuring means engaging communities in saying whether the results are good for them.” Another participant commented that Ushahidi maps are great and donors love them. But in CAR, for example, there is 1% internet penetration and maybe 9% of the people text. “If you are creating a crisis map about the incidence of violence, your humanitarian actors may access it, it may improve service delivery, but it is in no way useful for people on the ground. There is reliance on technology, but how to make it useful for local communities is still the big question…. It’s hard to talk about citizen engagement and citizen awareness if you are not reaching citizens because they don’t have access to technology.” And “what about the opportunity cost for the poor? ”asked one participant. “Time is restricted. CSOs push things down to the people least able to use the time for participation. There is a cost to participation, yet we assume participation is a global good. The poorest are really scraping for time and resources.  ‘Who is the data for?’ is still a huge question. Often it’s ‘here’s what we’re going to do for you’ rather than meeting with people first, asking what’s wrong, then listening and asking what they would like to do about it, and listening some more.”

10) Reaching the ‘unreachable’

Reaching and engaging the poorest is still difficult, and the truly unreached will require very different approaches. “We’re really very much spoke to hub,” said one participant, “This is not enough. How can we innovate and resolve this.” Another emphasized the need to find out who’s not part of the conversation, who is left out or not present when these community discussions take place. “You might find out that adolescent girls with mobility issues are not there. You can ask those with whom you are consulting if they know of someone who is not at the meeting. You need to figure out how to reach the invisible members of the community.”  However, as noted, “we also have to protect them. Sometimes identifying people can expose them. There is no clear answer.”

11) Innovation or building on what’s already there?

So will INGOs and donors continue to try to adapt old survey ideas to new technology tools? And will this approach survive much longer? “Aren’t we mostly looking for information that we can act on? Are we going to keep sending teams out all the time or will we begin to work with information we can access differently? Can we release ourselves from that dependence on survey teams?” Some felt that ‘data exhaust’ might be one way of getting information differently; for example a mode like Google Flu Trends. But others noted the difficulty of getting information from non-online populations, who are the majority. In addition, with these new ICT-based methods, there is still a question about representativeness and coverage. Integrated approaches where ICTs are married with traditional methods seem to be the key. This begs the question: “Is innovation really better than building up what’s already there?” as one participant commented. “We need to ask – does it add value? Is it better than what is already there? If it does add perceived value locally, then how do we ensure that it comes to some kind of result. We need to keep our eye on the results we want to achieve. We need to be more results-oriented and do reality checks. We need to constantly ask ourselves:  Are we listening to folks?”

In conclusion

There is much to think about in this emerging area of ICTs and Monitoring and Evaluation.  Join us for the third Salon in the series on October 17 where we’ll continue discussions. If you are not yet on the Technology Salon mailing list, you can sign up here. A summary of the first Salon in the series is here. (A summary of the October 17th Salon is here.)

Salons run by Chatham House Rule, thus no attribution has been made. 

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