MERL Tech News

Tips for solar charging your data collection

Post by Julia Connors of Voltaicsystems. Email Julia with questions: julia@voltaicsystems.com

What is solar for M&E?

Solar technology can be extremely useful for M&E projects in areas with minimal or inconsistent access to power. Portable solar chargers can eliminate power constraints and keep phones and tablets reliably charged up in the field.

In this post we’ll discuss:

  • How to decide if solar is right for your project
  • How to properly size a solar charging system to meet your needs

Do you really need solar?

In many cases solar is not necessary and will simply add complexity and costs to your project. If your team can return every day to a central location with access to power, then the battery power of the tablet is sufficient in most scenarios. If not, we recommend implementing standard power saving tips to reduce power consumption during time out collecting data.

POWER SAVING TIPS

SolarforMEPhoto

If you do have daily access to the grid but find that users need to recharge at least once while out or need to spend more than one day without power, then add an external battery pack. This cost-effective option allows your team to have extra power without carrying a full solar charging system. To size a battery for your needs, skip down to ‘Step 3’ below.

If you don’t have reliable access to grid power, the next section will help you determine which size solar charging system is best for you.

Sizing your solar charger system

The key to making solar successful in your project is finding the best system for your needs. If a system is underpowered then your team can still run out of power when they’re collecting data. On the other hand, if your system is too powerful it will be heavier and more expensive than needed. We recommend the following three steps for sizing your solar needs:

  1. Estimate your daily power consumption
  2. Determine your minimum solar panel size
  3. Determine your minimum battery size

Step 1: Estimate your daily power consumption

Once you have chosen the device you will be using in the field, it’s easy to determine your daily power consumption. First you’ll need to figure out the size of your device’s battery (in Watt hours). This can often be found by looking on the back of the battery itself or doing a quick Google search to find your device’s technical specifications.

Next, you’ll need to determine your battery usage per day. For example, if you use half of your device’s battery on a typical day of data collection, then your usage is 50%. If you need to recharge twice in one day, then your usage is 200%.

Once you have those numbers, use the formula below to find your daily power consumption:

Size of Device’s Battery (Wh) x Battery Usage (per day) =

Daily Power Consumption (Wh/day)

Step 2: Determine your minimum solar panel size

The larger your device, the bigger the solar panel (measured in Watts) you’ll need. This is because larger solar panels can generate more power from the sun than smaller panels. To determine the best solar panel size for your needs, use our formula below:

Daily Power Consumption (from Step 1) / Expected Hours of Good Sun*

x 2 (Standard Power Loss Variable) =

Solar Panel Minimum (Watts)

*We typically use 5 hours as a baseline for good sun and then adjust up or down depending on the conditions. High temperatures, clouds, or shading will reduce the power produced by the panel.

Since solar conditions change frequently throughout the day, we recommend choosing a panel that is 2-4 times the minimum size required.

SolarforMEPhoto2

Step 3: Determine minimum battery size

External batteries offer extra power storage so that your device will be charged when you need it. The battery acts as a perfect backup on cloudy and rainy days so it’s important to choose the right size for your device.

It can vary, but typically about 30% of power is lost in the transfer from the external battery to your device. Therefore, to determine the battery capacity needed for one day of use, we’ll use our power consumption data from Step 1 and divide by 0.7 (100% – 30% power loss).

Watt hours per day / 0.7 hours =

Watt battery capacity needed for 1 day of use

SolarforMEPhoto3

Picking the right system for your project

Now that you’ve done the math, you’re one step closer to choosing a solar charging system for your project. Since solar chargers come in many different forms, the last step to determining your perfect system is to think about how your team will be using the solar chargers in their work. It’s important to factor in storage for device/cables and how the user will be carrying the system.

Most users aren’t that technical, so having a pack that stores the battery and the device can simplify their experience (rather than handing over a battery and a panel that they need to figure how to organize during their day). By simply finding the right style and size, you’ll experience higher usage rates and make your team’s solar-powered data collection go more smoothly.

IVR, Facebook and WhatsApp: tech and M&E at AfrEA

by Linda Raftree

At the African Evaluation Association (AfrEA) Conference in Uganda on March 29th,  we ran a session on how mobile and social media platforms are being used in monitoring and evaluation processes. Our discussants were Jamie Arkin from Human Network International (soon to be merging with VotoMobile) who spoke about interactive voice response (IVR); John Njovu, an independent consultant working with the Ministry of National Development Planning of the Zambian government, who shared experiences with technology tools for citizen feedback to monitor budgets and support transparency and accountability; and Noel Verrinder from Genesis who talked about using WhatsApp in a youth financial education program.

Using IVR for surveys

Jamie shared how HNI deploys IVR surveys to obtain information about different initiatives or interventions from a wide public or to understand the public’s beliefs about a particular topic. These surveys come in three formats: random dialing of telephone numbers until someone picks up; asking people to call in, for example, on a radio show; or using an existing list of phone numbers. “If there is an 80% phone penetration or higher, it is equal to a normal household level survey,” she said. The organization has list of thousands of phone numbers and can segment these to create a sample. “IVR really amplifies people’s voices. We record in local language. We can ask whether the respondent is a man or a woman. People use their keypads to reply or we can record their voices providing an open response to the question.” The voice responses are later digitized into text for analysis. In order to avoid too many free voice responses, the HNI system can cut the recording off after 30 seconds or limit voice responses to the first 100 calls. Often keypad responses are most effective as people are not used to leaving voice mails.

IVR is useful in areas where there is low literacy. “In Rwanda, 80% of women cannot read a full sentence, so SMS is not a silver bullet,” Jamie noted. “Smartphones are coming, and people want them, but 95% of people in Uganda have a simple feature phone, so we cannot reach them by Facebook or WhatsApp. If you are going with those tools, you will only reach the wealthiest 5% of the population.”

In order to reduce response bias, the survey question order can be randomized. Response rates tend to be ten times higher on IVR than on SMS surveys, Jamie said, in part, because IVR is cheaper for respondents. The HNI system can provide auto-analysis for certain categories such as most popular response. CSV files can also be exported for further analysis. Additionally, the system tracks length of session, language, time of day and other meta data about the survey exercise.

Regulatory and privacy implications in most countries are unclear about IVR, and currently there are few legal restrictions against calling people for surveys. “There are opt-outs for SMS but not for IVRs, if you don’t want to participate you just hang up.” In some case, however, like Rwanda, there are certain numbers that are on “do not disturb” lists and these need to be avoided, she said.

Citizen-led budget monitoring through Facebook

John shared results of a program where citizens were encouraged to visit government infrastructure projects to track whether budget allocations had been properly done. Citizens would visit a health center or a school to inquire about these projects and then fill out a form on Facebook or a website to share their findings. A first issue with the project was that voters were interested in availability and quality of service delivery, not in budget spending. “”I might ask what money you got, did you buy what you said, was it delivered and is it here. Yes. Fine. But the bigger question is: Are you using it? The clinic is supposed to have 1 doctor, 3 nurses and 3 lab technicians. Are they all there? Yes. But are they doing their jobs? How are they treating patients?”

Quantity and budget spend were being captured but quality of service was not addressed, which was problematic. Another challenge with the program was that people did not have a good sense of what the dollar can buy, thus it was difficult for them to assess whether budget had been spent. Additionally, in Zambia, it is not customary for citizens to question elected officials. The idea that the government owes the people something, or that citizens can walk into a government office to ask questions about budget is not a traditional one. “So people were not confident in asking question or pushing government for a response.”

The addition of technology to the program did not resolve any of these underlying issues, and on top of this, there was an apparent mismatch with the idea of using mobile phones to conduct feedback. “In Zambia it was said that everyone has a phone, so that’s why we thought we’d put in mobiles. But the thing is that the number of SIMs doesn’t equal the number of phone owners. The modern woman may have a good phone or two, but as you go down to people in the compound they don’t have even basic types of phones. In rural areas it’s even worse,” said John, “so this assumption was incorrect.” When the program began running in Zambia, there was surprise that no one was reporting. It was then realized that the actual mobile ownership statistics were not so clear.

Additionally, in Zambia only 11% of women can read a full sentence, and so there are massive literacy issues. And language is also an issue. In this case, it was assumed that Zambians all speak English, but often English is quite limited among rural populations. “You have accountability language that is related to budget tracking and people don’t understand it. Unless you are really out there working directly with people you will miss all of this.”

As a result of the evaluation of the program, the Government of Zambia is rethinking ways to assess the quality of services rather than the quantity of items delivered according to budget.

Gathering qualitative input through WhatsApp

Genesis’ approach to incorporating WhatsApp into their monitoring and evaluation was more emergent. “We didn’t plan for it, it just happened,” said Noel Verrinder. Genesis was running a program to support technical and vocational training colleges in peri-urban and rural areas in the Northwest part of South Africa. The young people in the program are “impoverished in our context, but they have smartphones, WhatsApp and Facebook.”

Genesis had set up a WhatsApp account to communicate about program logistics, but it morphed into a space for the trainers to provide other kinds of information and respond to questions. “We started to see patterns and we could track how engaged the different youth were based on how often they engaged on WhatsApp.” In addition to the content, it was possible to gain insights into which of the participants were more engage based on their time and responses on WhatsApp.

Genesis had asked the youth to create diaries about their experiences, and eventually asked them to photograph their diaries and submit them by WhatsApp, given that it made for much easier logistics as compared to driving around to various neighborhoods to track down the diaries. “We could just ask them to provide us with all of their feedback by WhatsApp, actually, and dispense with the diaries at some point,” noted Noel.

In future, Genesis plans to incorporate WhatsApp into its monitoring efforts in a more formal way and to consider some of the privacy and consent aspects of using the application for M&E. One challenge with using WhatsApp is that the type of language used in texting is short and less expressive, so the organization will have to figure out how to understand emoticons. Additionally, it will need to ask for consent from program participants so that WhatsApp engagement can be ethically used for M&E purposes.

We have a data problem

by Emily Tomkys, ICT in Programmes at Oxfam GB

Following my presentation at MERL Tech, I have realised that it’s not only Oxfam who have a data problem; many of us have a data problem. In the humanitarian and development space, we collect a lot of data – whether via mobile phone or a paper process, the amount of data each project generates is staggering. Some of this data goes into our MIS (Management Information Systems), but all too often data remains in Excel spreadsheets on computer hard drives, unconnected cloud storage systems or Access and bespoke databases.

(Watch Emily’s MERL Tech London Lightning Talk!)

This is an issue because the majority of our programme data is analysed in silos on a survey-to-survey basis and at best on a project-to-project basis. What about when we want to analyse data between projects, between countries, or even globally? It would currently take a lot of time and resources to bring data together in usable formats. Furthermore, issues of data security, limited support for country teams, data standards and the cost of systems or support mean there is a sustainability problem that is in many people’s interests to solve.

The demand from Oxfam’s country teams is high – one of the most common requests the ICT in Programme Team receive centres around databases and data analytics. Teams want to be able to store and analyse their data easily and safely; and there is growing demand for cross border analytics. Our humanitarian managers want to see statistics on the type of feedback we receive globally. Our livelihoods team wants to be able to monitor prices at markets on a national and regional scale. So this motivated us to look for a data solution but it’s something we know we can’t take on alone.

That’s why MERL Tech represented a great opportunity to check in with other peers about potential solutions and areas for collaboration. For now, our proposal is to design a data hub where no matter what the type of data (unstructured, semi-structured or structured) and no matter how we collect the data (mobile data collection tools or on paper), our data can integrate into a database. This isn’t about creating new tools – rather it’s about focusing on the interoperability and smooth transition between tools and storage options.  We plan to set this up so data can be pulled through into a reporting layer which may have a mixture of options for quantitative analysis, qualitative analysis and GIS mapping. We also know we need to give our micro-programme data a home and put everything in one place regardless of its source or format and make it easy to pull it through for analysis.

In this way we can explore data holistically, spot trends on a wider scale and really know more about our programmes and act accordingly. Not only should this reduce our cost of analysis, we will be able to analyse our data more efficiently and effectively. Moreover, taking a holistic view of the data life cycle will enable us to do data protection by design and it will be easier to support because the process and the tools being used will be streamlined. We know that one tool does not and cannot do everything we require when we work in such vast contexts, so a challenge will be how to streamline at the same time as factoring in contextual nuances.

Sounds easy, right? We will be starting to explore our options and working on the datahub in the coming months. MERL Tech was a great start to make connections, but we are keen to hear from others about how you are approaching “the data problem” and eager to set something up which can also be used by other actors. So please add your thoughts in the comments or get in touch if you have ideas!

Dropping down your ignorance ratio: Campaigns meet KNIME

by Rodrigo Barahona (Oxfam Intermon, @rbarahona77) and Enrique Rodriguez (Consultant, @datanauta)

A few year ago, we ran a Campaign targeting the Guatemalan Government, which generated a good deal of global public support (100,000 signatures, online activism, etc.). This, combined with other advocacy strategies, finally pushed change to happen. We did an evaluation in order to learn from such a success and found a key area where there was little to learn because we were unable to get and analyze the information:  we knew almost nothing about which online channels drove traffic to the online petition and which had better conversion rates. We didn’t know the source of more than 80% of our signatures, so we couldn’t establish recommendations for future similar actions

Building on the philosophy underneath Vanity Metrics, we started developing a system to evaluate public engagement as part of advocacy campaigns and spike actions. We wanted to improve our knowledge on what works and what doesn’t on mobilizing citizens to take action (mostly signing petitions or other online action), and which were the most effective channels in terms of generating traffic and converting petitions. So we started implementing a relatively simple Google Analytics Tracking system that helped us determine the source of the visit/signatures, establish conversion rates, etc. The only caveat was that it was time consuming — the extraction of the information and its analysis was mostly manual.

Later on, we were asked to implement the methodology on a complex campaign that had 3 landing/petition pages, 3 exit pages, and all this in two different languages. Our preliminary analysis was that it would take us up to 8-10 hours of work, with high risk of mistakes as it needed cross analysis of up to 12 pages, and required distinguishing among more than 15 different sources for each page.

But then we met KNIME: an Information Miner tool that helped us to extract different sets of data from Google analytics (through plugins), create the data flow in a visual way and automatically execute part of the analysis. So far, we have automated the capture and analysis of statistics of web traffic (Google Analytics), the community of users on Twitter and the relevance of posts in that social network. We’ve been able to minimize the risk of errors, focus on the definition of new indicators and visualizations and provide reports to draw conclusions and design new communication strategies (based on data) in a very short period of time.

KNIME helped us to scale up our evaluation system, making it suitable for very complex campaigns, with a significant reduction of time dedication and also lowering the risk of mistakes. And most important of all, introducing KNIME into our system has dropped down our ignorance ratio significantly, because nowadays we can identify the source of more than 95% of the signatures. This means that we can shed light on how different strategies are working, which channels are bringing more visits to the different landing pages, and which have the higher conversion rate. All this is relevant information to inform decisions and adapt strategies and improve the outputs of a campaign.

Watch Rodrigo’s MERL Tech Lightning Talk here!

 

Thoughts from MERL Tech UK

merltech_uk-2016Post by Christopher Robert, Dobility (Survey CTO)

MERL Tech UK was held in London this week. It was a small, intimate gathering by conference standards (just under 100 attendees), but jam-packed full of passion, accumulated wisdom, and practical knowledge. It’s clear that technology is playing an increasingly useful role in helping us with monitoring, evaluation, accountability, research, and learning – but it’s also clear that there’s plenty of room for improvement. As a technology provider, I walked away with both more inspiration and more clarity for the road ahead.

Some highlights:

  • I’ve often felt that conferences in the ICT4D space have been overly-focused on what’s sexy, shiny, and new over what’s more boring, practical, and able to both scale and sustain. This conference was markedly different: it exceeded even the tradition of prior MERL Tech conferences in shifting from the pathology of “pilotitus” to a more hard-nosed focus on what really works.
  • There was more talk of data responsibility, which I took as another welcome sign of maturation in the space. This idea encompasses much beyond data security and the honoring of confidentiality assurances that we at Dobility/SurveyCTO have long championed, and it amounted to a rare delight: rather than us trying to push greater ethical consideration on others, for once we felt that our peers were pushing us to raise the bar even further. My own ideas in terms of data responsibility were challenged, and I came to realize that data security is just one piece of a larger ethical puzzle.
  • There are far fewer programs and projects re-inventing the wheel in terms of technology, which is yet another welcome sign of maturation. This is helping more resources to flow into the improvement and professionalization of a small but diverse set of technology platforms. Too much donor money still seems to be spent on technologies that have effective, well-established, and sustainable options available, but it’s getting better.
  • However, it’s clear that there are still plenty of ways to re-invent the wheel, and plenty of opportunities for greater collaboration and learning in the space. Most organizations are having to go it alone in terms of procuring and managing devices, training and supporting field teams, designing and monitoring data-collection activities, organizing and managing collected data, and more. Some larger international organizations who adopted digital technologies early have built up some impressive institutional capacity – but every organization still has its gaps and challenges, later adopters don’t have that historical capacity from which to draw, and smaller organizations don’t have the same kind of centralized institutional capacity.
  • Fortunately, MERL Tech organizers and participants like Oxfam GB and World Bank DIME have not only built tremendous internal capacity, but also been extremely generous in thinking through how to share that capacity with others. They share via their blogs and participation in conferences like this, and they are always thinking about new and more effective ways to share. That’s both heartening and inspiring.

I loved the smaller, more intimate nature of MERL Tech UK, but I have quickly come to somewhat regret that it wasn’t substantially larger. My first London day post-MERL-Tech was spent visiting with some other SurveyCTO users, including a wonderfully-well-attended talk on data quality at the Zoological Society of London, a meeting with some members of Imperial College London’s Schistosomiasis Control Initiative, and a discussion about some new University of Cambridge efforts to improve data and research on rare diseases in the UK. Later today, I’ll meet with some members of the TUMIKIA project team at the London School of Hygiene and Tropical Medicine, and in retrospect I now wish that all of these others had been at MERL Tech. I’m trying to share lessons as best I can, but it’s obvious that so many other organizations could both contribute to and profit from the kinds of conversations and sharing that were happening at MERL Tech.

Personally, I’ve always been distrustful of product user conferences as narrow, ego-driven, sales-and-marketing kinds of affairs, but I’m suddenly seeing how a SurveyCTO user conference could make real (social) sense. Our users are doing such incredible things, learning so much in the process, building up so much capacity – and so many of them are also willing to share generously with others. The key is providing mechanisms for that sharing to happen. At Dobility, we’ve just kept our heads down and stayed focused on providing and supporting affordable, accessible technology, but now I’m seeing that we could play a greater role in facilitating greater progress in the space. With thousands of SurveyCTO projects now in over 130 countries, the amount of learning – and the potential social benefits to sharing more – is enormous. We’ll have to think about how we can get better and better about helping. And please comment here if you have ideas for us!

Thanks again to Oxfam GB, Comic Relief, and everybody else who made MERL Tech UK possible. It was a wonderful event.

5 Insights from MERL Tech 2016

By Katherine Haugh, a visual note taker who summarizes content in a visually simple manner while keeping the complexity of the subject matter. Originally published on Katherine’s blog October 20, 2015 and here on ICT Works January 18th, 2016. 

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Recently, I had the opportunity to participate in the 2015 MERL Tech conference that brought together over 260 people from 157 different organizations. I joined the conference as a “visual note-taker,” and I documented the lightning talks, luncheon discussions, and breakout sessions with a mix of infographics, symbols and text.

Experiencing several “a-ha” moments myself, I thought it would be helpful to go a step further than just documenting what was covered and add some insights on my own. Five clear themes stood out to me: 1) There is such a thing as “too much data”2) “Lessons learned” is like a song on repeat 3) Humans > computers 4) Sharing is caring 5) Social impact investment is crucial.

1) There is such a thing as “too much data.”

MERLTech 2015 began with a presentation by Ben Ramalingham, who explained that, “big data is like teenage sex. No one knows how to do it and everyone thinks that everyone else is doing it.” In addition to being the most widely tweeted quote at the conference and eliciting a lot of laughter and nods of approval, Ben’s point was well-received by the audience. The fervor for collecting more and more data has been, ironically, limiting the ability of organizations to meaningfully understand their data and carry out data-driven decision-making.

Additionally, I attended the breakout session on “data minimalism” with Vanessa CorlazzoliMonalisa Salib, from USAID LEARN, and Teresa Crawford that further emphasized this point.

The session covered the ways that we can identify key learning questions and pinpoint need-to-have-data (not nice-to-have-data) to be able to answer those questions. [What this looks like in practice: a survey with onlyfive questions. Yes, just five questions.] This approach to data collection enforces the need to think critically each step of the way about what is needed and absolutely necessary, as opposed to collecting as much as possible and then thinking about what is “usable” later.

2) “Lessons learned” is like a song on repeat.

Similar to a popular song, the term “lessons learned” has been on repeat for many M&E practitioners (including myself). How many reports have we seen that conclude with lessons learned that are never actually learned? Having concluded my own capstone project with a set of “lessons learned,” I am at fault for this as well. In her lightning talk on “Lessons Not Learned in MERL,” Susan Davis explained that, “while it’s OK to re-invent the wheel, it’s not OK to re-invent a flat tire.”

It seems that we are learning the same “lessons” over and over again in the M&E-tech field and never implementing or adapting in accordance with those lessons. Susan suggested we retire the “MERL” acronym and update to “MERLA” (monitoring, evaluation, research, learning and adaptation).

How do we bridge the gap between M&E findings and organizational decision-making? Dave Algoso has some answers. (In fact, just to get a little meta here: Dave Algoso wrote about “lessons not learned” last year at M&E Tech and now we’re learning about “lessons not learned” again at MERLTech 2015. Just some food for thought.). A tip from Susan for not re-inventing a flat wheel: find other practitioners who have done similar work and look over their “lessons learned” before writing your own. Stay tuned for more on this at FailFest 2015 in December!

3) Humans > computers.

Who would have thought that at a tech-related conference, a theme would be the need for more human control and insight? Not me. That’s for sure! A funny aside: I have (for a very long time) been fearful that the plot of the Will Smith movie, “I-Robot” would become a reality. I now feel slightly more assured that this won’t happen, given that there was a consensus at this conference and others on the need for humans in the M&E process (and in the world). As Ben Ramalingham so eloquently explained, “you can’t use technology to substitute humans; use technology to understand humans.”

4) Sharing is caring.

Circling back to the lessons learned on repeat point, “sharing is caring” is definitely one we’ve heard before. Jacob Korenblum emphasized the need for more sharing in the M&E field and suggested three mechanisms for publicizing M&E results: 1)Understanding the existing eco-system (i.e. the decision between using WhatsApp in Jordan or in Malawi) 2) Building feedback loops directly into M&E design and 3) Creating and tracking indicators related to sharing. Dave Algoso also expands on this concept in TechChange’s TC111 course on Technology for Monitoring and Evaluation; Dave explains that bridging the gaps between the different levels of learning (individual, organizational, and sectoral) is necessary for building the overall knowledge of the field, which spans beyond the scope of a singular project.

5) Social impact investment is crucial.

I’ve heard this at other conferences I’ve attended, like the Millennial Action Project’sCongressional Summit on Next Generation Leadership and many others.  As a panelist on “The Future of MERLTech: A Donor View,” Nancy McPherson got right down to business: she addressed the elephant in the room by asking questions about “who the data is really for” and “what projects are really about.” Nancy emphasized the need for role reversal if we as practitioners and researchers are genuine in our pursuit of “locally-led initiatives.” I couldn’t agree more. In addition to explaining that social impact investing is the new frontier of donors in this space, she also gave a brief synopsis of trends in the evaluation field (a topic that my brilliant colleague Deborah Grodzicki and I will be expanding on. Stay tuned!)

 

How to Develop and Implement Responsible Data Policies

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A friend reminded me at the MERL Tech Conference that a few years ago when we brought up the need for greater attention to privacy, security and ethics when using ICTs and digital data in humanitarian and development contexts, people pointed us to Tor, encryption and specialized apps. “No, no, that’s not what we mean!” we kept saying. “This is bigger. It needs to be holistic. It’s not just more tools and tech.”

So, even if as a sector we are still struggling to understand and address all the different elements of what’s now referred to as “Responsible Data” (thanks to the great work of the Engine Room and key partners), at least we’ve come a long way towards framing and defining the areas we need to tackle. We understand the increasing urgency of the issue that the volume of data in the world is increasing exponentially and the data in our sector is becoming more and more digitalized.

This year’s MERL Tech included several sessions on Responsible Data, including Responsible Data Policies, the Human Element of the Data Cycle, The Changing Nature of Informed Consent, Remote Monitoring in Fragile Environments and plenary talks that mentioned ethics, privacy and consent as integral pieces of any MERL Tech effort.

The session on Responsible Data Policies was a space to share with participants why, how, and what policies some organizations have put in place in an attempt to be more responsible. The presenters spoke about the different elements and processes their organizations have followed, and the reasoning behind the creation of these policies. They spoke about early results from the policies, though it is still early days when it comes to implementing them.

What do we mean by Responsible Data?

Responsible data is about more than just privacy or encryption. It’s a wider concept that includes attention to the data cycle at every step, and puts the rights of people reflected in the data first:

  • Clear planning and purposeful collection and use of data with the aim of improving humanitarian and development approaches and results for those we work with and for
  • Responsible treatment of the data and respectful and ethical engagement with people we collect data from, including privacy and security of data and careful attention to consent processes and/or duty of care
  • Clarity on data sharing – what data, from whom and with whom and under what circumstances and conditions
  • Attention to transparency and accountability efforts in all directions (upwards, downwards and horizontally)
  • Responsible maintenance, retention or destruction of data.

Existing documentation and areas to explore

There is a huge bucket of concepts, frameworks, laws and policies that already exist in various other sectors and that can be used, adapted and built on to develop responsible approaches to data in development and humanitarian work. Some of these are in conflict with one another, however, and those conflicts need to be worked out or at least recognized if we are to move forward as a sector and/or in our own organizations.

Some areas to explore when developing a Responsible Data policy include:

  • An organization’s existing policies and practices (IT and equipment; downloading; storing of official information; confidentiality; monitoring, evaluation and research; data collection and storage for program administration, finance and audit purposes; consent and storage for digital images and communications; social media policies).
  • Local and global laws that relate to collection, storage, use and destruction of data, such as: Freedom of information acts (FOIA); consumer protection laws; data storage and transfer regulations; laws related to data collection from minors; privacy regulations such as the latest from the EU.
  • Donor grant requirements related to data privacy and open data, such as USAID’s Chapter 579 or International Aid Transparency Initiative (IATI) stipulations.

Experiences with Responsible Data Policies

At the MERL Tech Responsible Data Policy session, organizers and participants shared their experiences. The first step for everyone developing a policy was establishing wide agreement and buy-in for why their organizations should care about Responsible Data. This was done by developing Values and Principles that form the foundation for policies and guidance.

Oxfam’s Responsible Data policy has a focus on rights, since Oxfam is a rights-based organization. The organization’s existing values made it clear that ethical use and treatment of data was something the organization must consider to hold true to its ethos.

It took around six months to get all of the global affiliates to agree on the Responsible Program Data policy, a quick turnaround compared to other globally agreed documents because all the global executive directors recognized that this policy was critical.

A core point for Oxfam was the belief that digital identities and access will become increasingly important for inclusion in the future, and so the organization did not want to stand in the way of people being counted and heard. However, it wanted to be sure that this was done in a way that balanced and took privacy and security into consideration.

The policy is a short document that is now in the process of operationalization in all the countries where Oxfam works. Because many of Oxfam’s affiliate headquarters reside in the European Union, it needs to consider the new EU regulations on data, which are extremely strict, for example, providing everyone with an option for withdrawing consent.

This poses a challenge for development agencies that normally do not have the type of detailed databases on ‘beneficiaries’ as they do on private donors. Shifting thinking about ‘beneficiaries’ and treating them more as clients may be in order as one result of these new regulations. As Oxfam moves into implementation, challenges continue to arise.

For example, data protection in Yemen is different than data protection in Haiti. Knowing all the national level laws and frameworks and mapping these out alongside donor requirements and internal policies is extremely complicated, and providing guidance to country staff is difficult given that each country has different laws.

Girl Effect’s policy has a focus on privacy, security and safety of adolescent girls, who are the core constituency of the organization.

The policy became clearly necessary because although the organization had a strong girl safeguarding policy and practice, the effect of digital data had not previously been considered, and the number of programs that involve digital tools and data is increasing. The Girl Effect policy currently has four core chapters: privacy and security during design of a tool, service or platform; content considerations; partner vetting; and MEAL considerations.

Girl Effect looks at not only the privacy and security elements, but also aims to spur thinking about potential risks and unintended consequences for girls who access and use digital tools, platforms and content. One core goal is to stimulate implementers to think through a series of questions that help them to identify risks. Another is to establish accountability for decisions around digital data.

The policy has been in process of implementation with one team for a year and will be updated and adapted as the organization learns. It has proven to have good uptake so far from team members and partners, and has become core to how the teams and the wider organization think about digital programming. Cost and time for implementation increase with the incorporation of stricter policies, however, and it is challenging to find a good balance between privacy and security, the ability to safely collect and use data to adapt and improve tools and platforms, and user friendliness/ease of use.

Catholic Relief Services has an existing set of eight organizational principles: Sacredness and Dignity of the human person; Rights and responsibilities; Social Nature of Humanity; The Common Good; Subsidiarity; Solidarity; Option for the Poor; Stewardship.

It was a natural fit to see how these values that are already embedded in the organization could extend to the idea of Responsible Data. Data is an extension of the human person, therefore it should be afforded the same respect as the individual. The principle of ‘common good’ easily extends to responsible data sharing.

The notion of subsidiarity says that decision-making should happen as close as possible to the place where the impact of the decision will be the strongest, and this is nicely linked with the idea of sharing data back with communities where CRS works and engaging them in decision-making. The option for the poor urges CRS to place a preferential value on privacy, security and safety of the data of the poor over the data demands of other entities.

The organization is at the initial phase of creating its Responsible Data Policy. The process includes the development of the values and principles, two country learning visits to understand the practices of country programs and their concerns about data, development of the policy, and a set of guidelines to support staff in following the policy.

USAID recently embarked on its process of developing practical Responsible Data guidance to pair with its efforts in the area of open data. (See ADS 579). More information will be available soon on this initiative.

Where are we now?

Though several organizations are moving towards the development of policies and guidelines, it was clear from the session that uncertainties are the order of the day, as Responsible Data is an ethical question, often relying on tradeoffs and decisions that are not hard and fast. Policies and guidelines generally aim to help implementers ask the right questions, sort through a range of possibilities and weigh potential risks and benefits.

Another critical aspect that was raised at the MERL Tech session was the financial and staff resources that can be required to be responsible about data. On the other hand, for those organizations receiving funds from the European Union or residing in the EU or the UK (where despite Brexit, organizations will likely need to comply with EU Privacy Regulations), the new regulations mean that NOT being responsible about data may result in hefty fines and potential legal action.

Going from policy to implementation is a challenge that involves both capacity strengthening in this new area as well as behavior change and a better understanding of emerging concepts and multiple legal frameworks. The nuances by country, organization and donor make the process difficult to get a handle on.

Because staff and management are already overburdened, the trick to developing and implementing Responsible Data Policies and Practice will be finding ways to strengthen staff capacity and to provide guidance in ways that do not feel overwhelmingly complex. Though each situation will be different, finding ongoing ways to share resources and experiences so that we can advance as a sector will be one key step for moving forward.

This post was written with input from Maliha Khan, Independent Consultant; Emily Tomkys, Oxfam GB; Siobhan Green, Sonjara and Zara Rahman, The Engine Room.

Rethinking Informed Consent in Digital Development

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Most INGOs have not updated their consent forms and policies for many years, yet the growing use of technology in our work, for many different purposes, raises many questions and insecurities that are difficult to address.

  • Our old ways of requesting and managing consent need to be modernized to meet the new realities of digital data and the changing nature of data.
  • Is informed consent even possible when data is digital and/or opened?
  • Do we have any way of controlling what happens with that data once it is digital?
  • How often are organizations violating national and global data privacy laws?
  • Can technology be part of the answer?

At the MERL Tech conference, we looked at these issues in a breakout session on rethinking consent in the digital age.

What Is Consent?

Let’s take a moment to clarify what kind of consent we are talking about in this post. Being clear on this point is important because there are many synchronous conversations on consent in relation to technology.

For example there are people exploring the use of the consent frameworks or rhetoric in ICT user agreements – asking whether signing such user agreements can really be considered consent. There are others exploring the issue of consent for content distribution online, in particular personal or sensitive content such as private videos and photographs.

And while these (and other) consent debates are related and important to this post, what we are specifically talking about is how we, our organizations and projects, address the issue of consent when we are collecting and using data from those who participate in programs or monitoring, evaluation, research and learning (MERL) that we are implementing.

This is as timely as ever because introducing new technologies and kinds of data means we need to change how we build consent into project planning and implementation. In fact, it gives us an amazing opportunity to build consent into our projects in ways that our organizations may not have considered in the past.

While it used to be that informed consent was the domain of frontline research staff, the reality is that getting informed consent – where there is disclosure, voluntariness, comprehension and competence of the data subject – is the responsibility of anyone ‘touching’ the data.

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Two Examples of Digital Consent

Over the past two years, Girl Effect has been incorporating a number of mobile and digital tools into its programs. These include both the Girl Effect Mobile (GEM) and the Technology Enabled Girl Ambassadors (TEGA) programs.

Girl Effect Mobile is a global digital platform that is active in 49 countries and 26 languages. It is being developed in partnership with Facebook’s Free Basics initiative. GEM aims to provide a platform that connects girls to vital information, entertaining content and to each other.

Girl Effect’s digital privacy, safety and security policy directs the organization to review and revise its terms and conditions to ensure that they are ‘girl-friendly’ and respond to local context and realities, and that in addition to protecting the organization (as many T&Cs are designed to do), they also protect girls and their rights.

The GEM terms and conditions were initially a standard T&C. They were too long to expect girls to look at them on a mobile, the language was legalese, and they seemed one-sided. So the organization developed a new T&C with simplified language and removed some of the legal clauses that were irrelevant to the various contexts in which GEM operates.

Consent language was added to cover polls and surveys, since Girl Effect uses the platform to conduct research and for it’s monitoring, evaluation and learning work. In addition, summary points are highlighted in a shorter version of the T&Cs with a link to the full T&Cs. Girl Effect also develops short articles about online safety, privacy and consent as part of the GEM content as a way of engaging girls with these ideas as well.

TEGA is a girl-operated mobile-enabled research tool currently operating in Northern Nigeria. It uses data-collection techniques and mobile technology to teach girls aged 18-24 how to collect meaningful, honest data about their world in real time. TEGA provides Girl Effect and partners with authentic peer-to-peer insights to inform their work.

Because Girl Effect was concerned that girls being interviewed may not understand the consent they were providing during the research process, they used the mobile platform to expand on the consent process. They added a feature where the TEGA girl researchers play an audio clip that explains the consent process. Afterwards, girls who are being interviewed answer multiple-choice follow up questions to show whether they have understood what they have agreed to.

(Note: The TEGA team report that they have incorporated additional consent features into TEGA based on examples and questions shared in our session).

Oxfam, in addition to developing out their Responsible Program Data Policy, has been exploring ways in which technology can help address contemporary consent challenges.

The organization had doubts on how much its informed consent statement (which explains who the organization is, what the research is about and why Oxfam is collecting data as well as asks whether the participant is willing to be interviewed) was understood and whether informed consent is really possible in the digital age.

All the same, the organization wanted to be sure that the consent information was being read out in its fullest by enumerators (the interviewers). There were questions about what the variation might be on this between enumerators as well as in different contexts and countries of operation.

To explore whether communities were hearing the consent statement fully, Oxfam is using mobile data collection with audio recordings in the local language and using speed violations to know whether the time spent on the consent page is sufficient, according to the length of the audio file played. This is by no means foolproof but what Oxfam has found so far is that the audio file is often not played in full and or not at all.

Efforts like these are only the beginning, but they help to develop a resource base and stimulate more conversations that can help organizations and specific projects think through consent in the digital age.

Additional resources include this framework for Consent Policies developed at a Responsible Data Forum gathering.

Other Digital Consent Ideas

Additionally, because of how quickly technology and data use is changing, one idea that was shared was that rather than using informed consent frameworks, organizations may want to consider defining and meeting a ‘duty of care’ around the use of the data they collect.

This can be somewhat accomplished by through the creation of organizational-level Responsible Data Policies. There are also interesting initiatives exploring new ways of enabling communities to define consent themselves – like this data licenses prototype.

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The development and humanitarian sectors really needs to take notice, adapt and update their thinking constantly to keep up with technology shifts. We should also be doing more sharing about these experiences. By working together on these types of wicked challenges, we can advance without duplicating our efforts.

This post is co-authored by Emily Tomkys, Oxfam GB; Danna Ingleton, Amnesty International; and Linda Raftree, Independent.