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Responsible data for philanthropists

Posted in data, ethics, risk, wait... what?, tagged benefit, data, digital safeguarding, ethics, global philanthropy forum, harm, philanthropy, responsible data, risk, to do on April 3, 2019| Leave a Comment »

In the search for evidence of impact, donors and investors are asking that more and more data be generated by grantees and those they serve. Some of those driving this conversation talk about the “opportunity cost” of not collecting, opening and sharing as much data as possible. Yet we need to also talk about the real and tangible risks of data collecting and sharing and the long-term impacts of reduced data privacy and security rights, especially for the vulnerable individuals and groups with whom we work.

This week I’m at the Global Philanthropy Forum Conference in the heart of Silicon Valley speaking on a panel titled “Civil Liberties and Data Philanthropy: When NOT to Ask for More.” It’s often donor requests for innovation or for proof of impact that push implementors to collect more and more data. So donors and investors have a critical role to play in encouraging greater respect and protection of the data of vulnerable individuals and groups. Philanthropists, grantees, and investees can all help to reduce these risks by bringing a values-based responsible data approach to their work.

Here are three suggestions for philanthropists on how to contribute to more responsible data management:

1) Enhance your own awareness and expertise on the potential benefits and harms associated with data.

Adopt processes that take a closer look at the possible risks and harms of collecting and holding data and how to mitigate them. Ensure those aspects are reviewed and considered during investments and grant making.
Conduct risk-benefits-harms assessments early in the program design and/or grant decision-making processes. This type of assessment helps lay out the benefits of collecting and using data, identifies the data-related harms we might we be enabling, and asks us to determine how we are intentionally mitigating harm during the design of our data collection, use and sharing. Importantly, this process also asks us to also identify who is benefiting from data collection and who is taking on the burden of risk. It then aims to assess whether the benefits of having data outweigh the potential harms. Risks-benefits-harms assessments also help us to ensure we are doing a contextual assessment, which is important because every situation is different. When these assessments are done in a participatory way, they tend to be even more useful and accurate ways to reduce risks in data collection and management.
Hire people within your teams who can help provide technical support to grantees when needed in a friendly — not a punitive — way. Building in a ‘data responsibility by design’ approach can help with that. We need to think about the role of data during the early stages of design. What data is collected? Why? How? By and from whom? What are the potential benefits, risks, and harms of gathering, holding, using and sharing that data? How can we reduce the amount of data that we collect and mitigate potential harms?
Be careful with data on your grantees. If you are working with organizations who (because of the nature of their mission) are at risk themselves, it’s imperative that you protect their privacy and don’t expose them to harm by collecting too much data from them or about them. Here’s a good guide for human rights donors on protecting sensitive data.

2) Use your power and influence to encourage grantees and investees to handle data more responsibly. If donors are going to push for more data collection, they should also be signaling to grantees and investees that responsible data management matters and encouraging them to think about it in proposals and more broadly in their work.

Strengthen grantee capacity as part of the process of raising data management standards. Lower-resourced organizations may not be able to meet higher data privacy requirements, so donors should think about how they can support rather than exclude smaller organizations with less capacity as we all work together to raise data management standards.
Invest holistically in both grants and grantees. This starts by understanding grantees’ operational, resource, and technical constraints as well as the real security risks posed to grantee staff, data collectors, and data subjects. For this to work, donors need to create genuinely safe spaces for grantees to voice their concerns and discuss constraints that may limit their ability to safely collect the data that donors are demanding.
Invest in grantees’ IT and other systems and provide operational funds that enable these systems to work. There is never enough funding for IT systems, and this puts the data of vulnerable people and groups at risk. One reason that organizations struggle to fund systems and improve data management is because they can’t bill overhead. Perverse incentives prevent investments in responsible data. Donors can work through this and help find solutions.
Don’t punish organizations that include budget for better data use, protection and security in their proposals. It takes money and staff and systems to manage data in secure ways. Yet stories abound in the sector about proposals that include these elements being rejected because they turn out to be more expensive. It’s critical to remember that safeguarding of all kinds takes resources!
Find out what kind of technical or systems support grantees/investees need to better uphold ethical data use and protection and explore ways that you can provide additional funds and resources to strengthen this area in those grantees and across the wider sector.
Remember that we are talking about long-term organizational behavior change. It is urgent to get moving on improving how we all handle data — but this will take some time. It’s not a quick fix because the skills are in short supply and high demand right now as a result of the GDPR and related laws that are emerging in other countries around the world.
Don’t ask grantees to collect data that might make vulnerable individuals or groups wary of them. Data is an extension of an individual. Trust in how an organization collects and manages an individual’s data leads to trust in an organization itself. Organizations need to be trusted in order to do our work, and collection of highly sensitive data, misuse of data or a data breach can really break that trust compact and reduce an organization’s impact.

3) Think about the responsibility you have for what you do, what you fund, and the type of society that we live in. Support awareness and compliance with new regulations and legislation that can protect privacy. Don’t use “innovation” as an excuse for putting historically marginalized individuals and groups at risk or for allowing our societies to advance in ways that only benefit the wealthiest. Question the current pathway of the “Fourth Industrial Revolution” and where it may take us.

I’m sure I’m leaving out some things. What do you think donors and the wider philanthropic community can do to enhance responsible data management and digital safeguarding?

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A discussion on WFP-Palantir and the ethics of humanitarian data sharing

Posted in accountability, data, ethics, wait... what?, tagged aid, concerns, data, data modeling, due diligence, ethics, humanitarian, NYC, Palantir, partnerships, private, protection, public, risk, Tech Salon, Technology Salon, UN, WFP, World Food Program on March 2, 2019| Leave a Comment »

The recently announced World Food Programme (WFP) partnership with Palantir, IRIN’s article about it, reactions from the Responsible Data Forum, and WFP’s resulting statement inspired us to pull together a Technology Salon in New York City to discuss the ethics of humanitarian data sharing.

(See this crowdsourced document for more background on the WFP-Palantir partnership and resources for thinking about the ethics of data sharing. Also here is an overview of WFP’s SCOPE system for beneficiary identification, management and tracking.)

Our lead discussants were: Laura Walker McDonald, Global Alliance for Humanitarian Innovation; Mark Latonero, Research Lead for Data & Human Rights, Data & Society; Nathaniel Raymond, Jackson Institute of Global Affairs, Yale University; and Kareem Elbayar, Partnerships Manager, Centre for Humanitarian Data at the United Nations Office for the Coordination of Humanitarian Affairs. We were graciously hosted by The Gov Lab.

What are the concerns about humanitarian data sharing and with Palantir?

Some of the initial concerns expressed by Salon participants about humanitarian data sharing included: data privacy and the permanence of data; biases in data leading to unwarranted conclusions and assumptions; loss of stakeholder engagement when humanitarians move to big data and techno-centric approaches; low awareness and poor practices across humanitarian organizations on data privacy and security; tensions between security of data and utility of data; validity and reliability of data; lack of clarity about the true purposes of data sharing; the practice of ‘ethics outsourcing’ (testing things in places where there is a perceived ‘lower ethical standard;’ and less accountability); use of humanitarian data to target and harm aid recipients; disempowerment and extractive approaches to data; lack of checks and balances for safe and productive data sharing; difficulty of securing meaningful consent; and the links between data and surveillance by malicious actors, governments, private sector, military or intelligence agencies.

Palantir’s relationships and work with police, the CIA, ICE, the NSA, the US military and wider intelligence community are one of the main concerns about this partnership. Some ask whether a company can legitimately serve philanthropy, development, social, human rights and humanitarian sectors while also serving the military and intelligence communities and whether it is ethical for those in the former to engage in partnerships with companies who serve the latter. Others ask if WFP and others who partner with Palantir are fully aware of the company’s background, and if so, why these partnerships have been able to pass through due diligence processes. Yet others wonder if a company like Palantir can be trusted, given its background.

Below is a summary of the key points of the discussion, which happened on February 28, 2019. (Technology Salons are Chatham House affairs, so I have not attributed quotes in this post.)

Why were we surprised by this partnership/type of partnership?

Our first discussant asked why this partnership was a surprise to many. He emphasized the importance of stakeholder conversations, transparency, and wider engagement in the lead-up to these kinds of partnerships. “And I don’t mean in order to warm critics up to the idea, but rather to create a safe and trusted ecosystem. Feedback and accountability are really key to this.” He also highlighted that humanitarian organizations are not experts in advanced technologies and that it’s normal for them to bring in experts in areas that are not their forte. However, we need to remember that tech companies are not experts in humanitarian work and put the proper checks and balances in place. Bringing in a range of multidisciplinary expertise and distributed intelligence is necessary in a complex information environment. One possible approach is creating technology advisory boards. Another way to ensure more transparency and accountability is to conduct a human rights impact assessment. The next year will be a major test for these kinds of partnerships, given the growing concerns, he said.

One Salon participant said that the fact that the humanitarian sector engages in partnerships with the private sector is not a surprise at all, as the sector has worked through Public-Private Partnerships (PPPs) for several years now and they can bring huge value. The surprise is that WFP chose Palantir as the partner. “They are not the only option, so why pick them?” Another person shared that the WFP partnership went through a full legal review, and so it was not a surprise to everyone. However, communication around the partnership was not well planned or thought out and the process was not transparent and open. Others pointed out that although a legal review covers some bases, it does not assess the potential negative social impact or risk to ‘beneficiaries.’ For some the biggest surprise was WFP’s own surprise at the pushback on this particular partnership and its unsatisfactory reaction to the concerns raised about it. The response from responsible data advocates and the press attention to the WFP-Palantir partnership might be a turning point for the sector to encourage more awareness of the risks in working with certain types of companies. As many noted, this is not only a problem for WFP, it’s something that plagues the wider sector and needs to be addressed urgently.

Organizations need think beyond reputational harm and consider harm to beneficiaries

“We spend too much time focusing on avoiding risk to institutions and too little time thinking about how to mitigate risk to beneficiaries,” said one person. WFP, for example, has some of the best policies and procedures out there, yet this partnership still passed their internal test. That is a scary thought, because it implies that other agencies who have weaker policies might be agreeing to even more risky partnerships. Are these policies and risk assessments, then, covering all the different types of risk that need consideration? Many at the Salon felt that due diligence and partnership policies focus almost exclusively on organizational and reputational risk with very little attention to the risk that vulnerable populations might face. It’s not just a question of having policies, however, said one person. “Look at the Oxfam Safeguarding situation. Oxfam had some of the best safeguarding policies, yet there were egregious violations that were not addressed by having a policy. It’s a question of power and how decisions get made, and where decision-making power lies and who is involved and listened to.” (Note: one person contacted me pre-Salon to say that there was pushback by WFP country-level representatives about the Palantir partnership, but that it still went ahead. This brings up the same issue of decision-making power, and who has power to decide on these partnerships and why are voices from the frontlines not being heard? Additionally, are those whose data is captured and put into these large data systems ever consulted about what they think?)

Organizations need to assess wider implications, risks, and unintended negative consequences

It’s not only WFP that is putting information into SCOPE, said one person. “Food insecure people have no choice about whether to provide their data if they wish to receive food.” Thus, the question of truly ‘informed consent’ arises. Implementing partners don’t have a lot of choice either, he said. “Implementing agencies are forced to input beneficiary data into SCOPE if they want to work in particular zones or countries.” This means that WFP’s systems and partnerships have an impact on the entire humanitarian community, and therefore these partnerships and systems need to be more broadly consulted about with the wider sector. The optical and reputational impact to organizations aside from WFP is significant, as they may disagree with the Palantir partnership but they are now associated with it by default. This type of harm goes beyond the fear of exploitation of the data in WFP’s “data lake.” It becomes a risk to personnel on the ground who are then seen as collaborating with a CIA contractor by putting beneficiary biometric data into SCOPE. This can also deter food-insecure people from accessing benefits. Additionally, association with CIA or US military has led to humanitarian agencies and workers being targeted, attacked and killed. That is all in addition to the question on whether these kinds of partnerships violate humanitarian principles, such as that of impartiality.

“It’s critical to understand the role of rumor in humanitarian contexts,” said one discussant. “Affected populations are trying to figure out what is happening and there is often a lot of rumor going around.” So, if Palantir has a reputation for giving data to the CIA, people may hear about that and then be afraid to access services for fear of having their data given to the CIA. This can lead to retaliation against humanitarians and humanitarian organizations and escalate their risk of operating. Risk assessments need to go beyond the typical areas of reputation or financial risk. We also need to think about how these partnerships can affect humanitarian access and community trust and how rumors can have wide ripple effects.

The whole sector needs to put better due diligence systems in place. As it is now, noted one person, often it’s someone who doesn’t know much about data who writes up a short summary of the partnership, and there is limited review. “We’ve been struggling for 10 years to get our offices to use data. Now we’re in a situation where they’re just picking up a bunch of data and handing it over to private companies.”

UN immunities and privileges lead to a lack of accountability

The fact that UN agencies have immunities and privileges, means that laws such as the EU’s General Data Protection Regulation (GDPR) do not apply to them and they are left to self-regulate. Additionally, there is no common agreement among UN Agencies on how GDPR applies, and each UN agency interprets it on their own. As one person noted “There is a troubling sense of exceptionalism and lack of accountability in some of these agencies because ‘a beneficiary cannot take me to court.’” An interesting point, however, is that while UN agencies are immune, those contracted as their data processors are not immune — so data processors beware!

Demographically Identifiable Information (DII) can lead to serious group harm

The WFP has stated that personally identifiable information (PII) is not technically accessible to Palantir via this partnership. However, some at the Salon consider that the WFP failed in their statement about the partnership when they used the absence of PII as a defense. Demographically Identifiable Information (DII) and the activity patterns that are visible even in commodity data can be extrapolated as training data for future data modeling. “This is prospective modeling of action-based intelligence patterns as part of multiple screeners of intel,” said one discussant. He went on to explain that privacy discussions have moved from centering on property rights in the 19^th Century, to individual rights in the 20^th Century, to group rights in the 21^st Century. We can use existing laws to emphasize protection of groups and to highlight the risks of DII leading to group harm, he said, as there are well-known cases that exemplify the notion of group harms (Plessy v Ferguson, Brown v Board of Education). Even in logistics data (which is the kind of data that WFP says Palantir will access) that contains no PII, it’s very simple to identify groups. “I can look at supply chain information and tell you where there are lactating mothers. If you don’t want refugees to give birth in the country they have arrived to, this information can be used for targeting.”

Many in the sector do not trust a company like Palantir

Though it is not clear who was in the room when WFP made the decision to partner with Palantir, the overall sector has concerns that the people making these decisions are not assessing partnerships from all angles: legal, privacy, programmatic, ethical, data use and management, social, protection, etc. Technologists and humanitarian practitioners are often not included in making these decisions, said one participant. “It’s the people with MBAs. They trust a tech company to say ‘this is secure’ but they don’t have the expertise to actually know that. Not to mention that yes, something might be secure, but maybe it’s not ethical. Senior people are signing off without having a full view. We need a range of skill sets reviewing these kinds of partnerships and investments.”

Another question arises: What happens when there is scope creep? Is Palantir in essence “grooming” the sector to then abuse data it accesses once it’s trusted and “allowed in”? Others pointed out that the grooming has already happened and Palantir is already on the inside. They first began partnering with the sector via the Clinton Global Initiative meetings back in 2013 and they are very active at World Economic Forum meetings. “This is not something coming out of the Trump administration, it was happening long before that,” said one person, and the company is already “in.” Another person said “Palantir lobbied their way into this, and they’ve gotten past the point of reputational challenge.” Palantir has approached many humanitarian agencies, including all the UN agencies, added a third person. Now that they have secured this contract with the WFP, the door to future work with a lot of other agencies is open and this is very concerning.

We’re in a new political economy: data brokerage.

“Humanitarians have lost their Geneva values and embraced Silicon Valley values” said one discussant. They are becoming data brokers within a colonial data paradigm. “We are making decisions in hierarchies of power, often extralegally,” he said. “We make decisions about other people’s data without their involvement, and we need to be asking: is it humanitarian to commodify for monetary or reasons of value the data of beneficiaries? When is it ethical to trade beneficiary data for something of value?” Another raised the issue of incentives. “Where are the incentives stacked? There is no incentive to treat beneficiaries better. All the incentives are on efficiency and scale and attracting donors.”

Can this example push the wider sector to do better?

One participant hoped there could be a net gain out of the WFP-Palantir case. “It’s a bad situation. But it’s a reckoning for the whole space. Most agencies don’t have these checks and balances in place. But people are waking up to it in a serious way. There’s an opportunity to step into. It’s hard inside of bureaucratic organizations, but it’s definitely an opportunity to start doing better.”

Another said that we need more transparency across the sector on these partnerships. “What is our process for evaluating something like this? Let’s just be transparent. We need to get these data partnership policies into the open. WFP could have simply said ‘here is our process’. But they didn’t. We should be working with an open and transparent model.” Overall, there is a serious lack of clarity on what data sharing agreements look like across the sector. One person attending the Salon said that their organization has been trying to understand current practice with regard to data sharing, and it’s been very difficult to get any examples, even redacted ones.

What needs to happen?

In closing we discussed what needs to happen next. One person noted that in her research on Responsible Data, she found a total lack of capacity in terms of technology at non-profit organizations. “It’s the Economist Syndrome. Someone’s boss reads something on the bus and decides they need a blockchain,” someone quipped. In terms of responsible data approaches, research shows that organizations are completely overwhelmed. “They are keeping silent about their low capacity out of fear they will face consequences,” said one person, “and with GDPR, even more so”. At the wider level, we are still focusing on PII as the issue without considering DII and group rights, and this is a mistake, said another.

Organizations have very low capacity, and we are siloed. “Program officers do not have tech capacity. Tech people are kept in offices or ‘labs’ on their own and there is not a lot of porosity. We need protection advisors, lawyers, digital safety advisors, data protection officers, information management specialists, IT all around the table for this,” noted one discussant. Also, she said, though we do need principles and standards, it’s important that organizations adapt these so that they are their own principles and standards. “We need to adapt these boiler plate standards to our organizations. This has to happen based on our own organizational values. Not everyone is rights-based, not everyone is humanitarian.” So organizations need to take the time to review and adapt standards, policies and procedures to their own vision and mission and to their own situations, contexts and operations and to generate awareness and buy-in. In conclusion, she said, “if you are not being responsible with data, you are already violating your existing values and codes. Responsible Data is already in your values, it’s a question of living it.”

Technology Salons happen in several cities around the world. If you’d like to join a discussion, sign up here. If you’d like to host a Salon, suggest a topic, or support us to keep doing Salons in NYC please get in touch with me! 🙂

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Buckets of data for MERL

Posted in data, development, M&E, M&E Tech, MERL Tech, wait... what?, tagged adaptive management, big data, data, digital, evaluation, feedback, found data, harvested data, learning, MERL, monitoring, real-time, research, traditional data collection on August 7, 2017| 1 Comment »

(I’ve been blogging a little bit over at MERLTech.org. Here’s a repost.)

It can be overwhelming to get your head around all the different kinds of data and the various approaches to collecting or finding data for development and humanitarian monitoring, evaluation, research and learning (MERL).

Though there are many ways of categorizing data, lately I find myself conceptually organizing data streams into four general buckets when thinking about MERL in the aid and development space:

‘Traditional’ data. How we’ve been doing things for(pretty much)ever. Researchers, evaluators and/or enumerators are in relative control of the process. They design a specific questionnaire or a data gathering process and go out and collect qualitative or quantitative data; they send out a survey and request feedback; they do focus group discussions or interviews; or they collect data on paper and eventually digitize the data for analysis and decision-making. Increasingly, we’re using digital tools for all of these processes, but they are still quite traditional approaches (and there is nothing wrong with traditional!).
‘Found’ data. The Internet, digital data and open data have made it lots easier to find, share, and re-use datasets collected by others, whether this is internally in our own organizations, with partners or just in general. These tend to be datasets collected in traditional ways, such as government or agency data sets. In cases where the datasets are digitized and have proper descriptions, clear provenance, consent has been obtained for use/re-use, and care has been taken to de-identify them, they can eliminate the need to collect the same data over again. Data hubs are springing up that aim to collect and organize these data sets to make them easier to find and use.
‘Seamless’ data. Development and humanitarian agencies are increasingly using digital applications and platforms in their work — whether bespoke or commercially available ones. Data generated by users of these platforms can provide insights that help answer specific questions about their behaviors, and the data is not limited to quantitative data. This data is normally used to improve applications and platform experiences, interfaces, content, etc. but it can also provide clues into a host of other online and offline behaviors, including knowledge, attitudes, and practices. One cautionary note is that because this data is collected seamlessly, users of these tools and platforms may not realize that they are generating data or understand the degree to which their behaviors are being tracked and used for MERL purposes (even if they’ve checked “I agree” to the terms and conditions). This has big implications for privacy that organizations should think about, especially as new regulations are being developed such a the EU’s General Data Protection Regulations (GDPR). The commercial sector is great at this type of data analysis, but the development set are only just starting to get more sophisticated at it.
‘Big’ data. In addition to data generated ‘seamlessly’ by platforms and applications, there are also ‘big data’ and data that exists on the Internet that can be ‘harvested’ if one only knows how. The term ‘Big data’ describes the application of analytical techniques to search, aggregate, and cross-reference large data sets in order to develop intelligence and insights. (See this post for a good overview of big data and some of the associated challenges and concerns). Data harvesting is a term used for the process of finding and turning ‘unstructured’ content (message boards, a webpage, a PDF file, Tweets, videos, comments), into ‘semi-structured’ data so that it can then be analyzed. (Estimates are that 90 percent of the data on the Internet exists as unstructured content). Currently, big data seems to be more apt for predictive modeling than for looking backward at how well a program performed or what impact it had. Development and humanitarian organizations (self included) are only just starting to better understand concepts around big data how it might be used for MERL. (This is a useful primer).

Thinking about these four buckets of data can help MERL practitioners to identify data sources and how they might complement one another in a MERL plan. Categorizing them as such can also help to map out how the different kinds of data will be responsibly collected/found/harvested, stored, shared, used, and maintained/ retained/ destroyed. Each type of data also has certain implications in terms of privacy, consent and use/re-use and how it is stored and protected. Planning for the use of different data sources and types can also help organizations choose the data management systems needed and identify the resources, capacities and skill sets required (or needing to be acquired) for modern MERL.

Organizations and evaluators are increasingly comfortable using mobile and/or tablets to do traditional data gathering, but they often are not using ‘found’ datasets. This may be because these datasets are not very ‘find-able,’ because organizations are not creating them, re-using data is not a common practice for them, the data are of questionable quality/integrity, there are no descriptors, or a variety of other reasons.

The use of ‘seamless’ data is something that development and humanitarian agencies might want to get better at. Even though large swaths of the populations that we work with are not yet online, this is changing. And if we are using digital tools and applications in our work, we shouldn’t let that data go to waste if it can help us improve our services or better understand the impact and value of the programs we are implementing. (At the very least, we had better understand what seamless data the tools, applications and platforms we’re using are collecting so that we can manage data privacy and security of our users and ensure they are not being violated by third parties!)

Big data is also new to the development sector, and there may be good reason it is not yet widely used. Many of the populations we are working with are not producing much data — though this is also changing as digital financial services and mobile phone use has become almost universal and the use of smart phones is on the rise. Normally organizations require new knowledge, skills, partnerships and tools to access and use existing big data sets or to do any data harvesting. Some say that big data along with ‘seamless’ data will one day replace our current form of MERL. As artificial intelligence and machine learning advance, who knows… (and it’s not only MERL practitioners who will be out of a job –but that’s a conversation for another time!)

Not every organization needs to be using all four of these kinds of data, but we should at least be aware that they are out there and consider whether they are of use to our MERL efforts, depending on what our programs look like, who we are working with, and what kind of MERL we are tasked with.

I’m curious how other people conceptualize their buckets of data, and where I’ve missed something or defined these buckets erroneously…. Thoughts?

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Rethinking Informed Consent in the Digital Age

Posted in accountability, data, development, evaluation, ICTs, mobile and technology, M&E, M&E Tech, MandEofTech, MERL Tech, monitoring, monitoring and evaluation, privacy, protection, wait... what?, tagged consent, data, informed, MERL Tech, policy, privacy, technology on November 2, 2016| 4 Comments »

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

At the MERL Tech conference in DC this month, we ran a breakout session on rethinking consent in the digital age. 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?

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 diagram highlights that no matter how someone is engaging with the data, how they do so and the decisions they make will impact on what is disclosed to the data subject.

No matter how someone is engaging with data, how they do so and the decisions they make will impact on what is disclosed to the data subject.

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.

Here we share examples from two organizations who have been exploring consent issues in their tech work.

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 its 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.

https://wiki.responsibledata.io/Framework_for_consent_policies

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.

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 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.

The development and humanitarian sectors really need 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.

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Developing and operationalizing Responsible Data Policies

Posted in data, development, ICT4D, M&E Tech, MERL Tech, mobile and technology, monitoring, monitoring and evaluation, privacy, risk, tagged CRS, data, Emily Tomkys, Girl Effect, Maliha Khan, MERLTech, Oxfam, policy, responsible, Session, Siobhan Green, USAID on October 31, 2016| 2 Comments »

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

A friend reminded me earlier this month 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 who 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.

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Unpacking digital privacy, security & safety in the development space

Posted in data, development, ICT4D, ICTs, ICTs, mobile and technology, privacy, wait... what?, tagged challenges, data, digital development, privacy, recommendations, responsible data, safety, security, tips on May 25, 2016| 3 Comments »

Crowdsourcing our Responsible Data questions, challenges and lessons. (Photo courtesy of Amy O'Donnell).

Crowdsourcing our Responsible Data questions, challenges and lessons. (Photo by Amy O’Donnell).

At Catholic Relief Services’ ICT4D Conference in May 2016, I worked with Amy O’Donnell (Oxfam GB) and Paul Perrin (CRS) to facilitate a participatory session that explored notions of Digital Privacy, Security and Safety. We had a full room, with a widely varied set of experiences and expertise.

The session kicked off with stories of privacy and security breaches. One person told of having personal data stolen when a federal government clearance database was compromised. We also shared how a researcher in Denmark scraped very personal data from the OK Cupid online dating site and opened it up to the public.

A comparison was made between the OK Cupid data situation and the work that we do as development professionals. When we collect very personal information from program participants, they may not expect that their household level income, health data or personal habits would be ‘opened’ at some point.

Our first task was to explore and compare the meaning of the terms: Privacy, Security and Safety as they relate to “digital” and “development.”

What do we mean by privacy?

The “privacy” group talked quite a bit about contextuality of data ownership. They noted that there are aspects of privacy that cut across different groups of people in different societies, and that some aspects of privacy may be culturally specific. Privacy is concerned with ownership of data and protection of one’s information, they said. It’s about who owns data and who collects and protects it and notions of to whom it belongs. Private information is that which may be known by some but not by all. Privacy is a temporal notion — private information should be protected indefinitely over time. In addition, privacy is constantly changing. Because we are using data on our mobile phones, said one person, “Safaricom knows we are all in this same space, but we don’t know that they know.”

Another said that in today’s world, “You assume others can’t know something about you, but things are actually known about you that you don’t even know that others can know. There are some facts about you that you don’t think anyone should know or be able to know, but they do.” The group mentioned website terms and conditions, corporate ownership of personal data and a lack of control of privacy now. Some felt that we are unable to maintain our privacy today, whereas others felt that one could opt out of social media and other technologies to remain in control of one’s own privacy. The group noted that “privacy is about the appropriate use of data for its intended purpose. If that purpose shifts and I haven’t consented, then it’s a violation of privacy.”

What do we mean by security?

The Security group considered security to relate to an individual’s information. “It’s your information, and security of it means that what you’re doing is protected, confidential, and access is only for authorized users.” Security was also related to the location of where a person’s information is hosted and the legal parameters. Other aspects were related to “a barrier – an anti-virus program or some kind of encryption software, something that protects you from harm…. It’s about setting roles and permissions on software and installing firewalls, role-based permissions for accessing data, and cloud security of individuals’ data.” A broader aspect of security was linked to the effects of hacking that lead to offline vulnerability, to a lack of emotional security or feeling intimidated in an online space. Lastly, the group noted that “we, not the systems, are the weakest link in security – what we click on, what we view, what we’ve done. We are our own worst enemies in terms of keeping ourselves and our data secure.”

What do we mean by safety?

The Safety group noted that it’s difficult to know the difference between safety and security. “Safety evokes something highly personal. Like privacy… it’s related to being free from harm personally, physically and emotionally.” The group raised examples of protecting children from harmful online content or from people seeking to harm vulnerable users of online tools. The aspect of keeping your online financial information safe, and feeling confident that a service was ‘safe’ to use was also raised. Safety was considered to be linked to the concept of risk. “Safety engenders a level of trust, which is at the heart of safety online,” said one person.

In the context of data collection for communities we work with – safety was connected to data minimization concepts and linked with vulnerability, and a compounded vulnerability when it comes to online risk and safety. “If one person’s data is not safely maintained it puts others at risk,” noted the group. “And pieces of information that are innocuous on their own may become harmful when combined.” Lastly, the notion of safety as related to offline risk or risk to an individual due to a specific online behavior or data breach was raised.

It was noted that in all of these terms: privacy, security and safety, there is an element of power, and that in this type of work, a power relations analysis is critical.

The Digital Data Life Cycle

After unpacking the above terms, Amy took the group through an analysis of the data life cycle (courtesy of the Engine Room’s Responsible Data website) in order to highlight the different moments where the three concepts (privacy, security and safety) come into play.

Plan/Design
Collect/Find/Acquire
Store
Transmit
Access
Share
Analyze/use
Retention
Disposal
Afterlife

Participants added additional stages in the data life cycle that they passed through in their work (coordinate, monitor the process, monitor compliance with data privacy and security policies). We placed the points of the data life cycle on the wall, and invited participants to:

Place a pink sticky note under the stage in the data life cycle that resonates or interests them most and think about why.
Place a green sticky note under the stage that is the most challenging or troublesome for them or their organizations and think about why.
Place a blue sticky note under the stage where they have the most experience, and to share a particular experience or tip that might help others to better manage their data life cycle in a private, secure and safe way.

Challenges, concerns and lessons

Design as well as policy are important!

Design drives everything else. We often start from the point of collection when really it’s at the design stage when we should think about the burden of data collection and define what’s the minimum we can ask of people? How we design – even how we get consent – can inform how the whole process happens.
When we get part-way through the data life cycle, we often wish we’d have thought of the whole cycle at the beginning, during the design phase.
In addition to good design, coordination of data collection needs to be thought about early in the process so that duplication can be reduced. This can also reduce fatigue for people who are asked over and over for their data.
Informed consent is such a critical issue that needs to be linked with the entire process of design for the whole data life cycle. How do you explain to people that you will be giving their data away, anonymizing, separating out, encrypting? There are often flow down clauses in some contracts that shifts responsibilities for data protection and security and it’s not always clear who is responsible for those data processes? How can you be sure that they are doing it properly and in a painstaking way?
Anonymization is also an issue. It’s hard to know to what level to anonymize things like call data records — to the individual? Township? District Level? And for how long will anonymization actually hold up?
The lack of good design and policy contributes to overlapping efforts and poor coordination of data collection efforts across agencies. We often collect too much data in poorly designed databases.
Policy is not enough – we need to do a much better job of monitoring compliance with policy.
Institutional Review Boards (IRBs) and compliance aspects need to be updated to the new digital data reality. At the same time, sometimes IRBs are not the right instrument for what we are aiming to achieve.

Data collection needs more attention.

Data collection is the easy part – where institutions struggle is with analyzing and doing something with the data we collect.
Organizations often don’t have a well-structured or systematic process for data collection.
We need to be clearer about what type of information we are collecting and why.
We need to update our data protection policy.

Reasons for data sharing are not always clear.

How can share data securely and efficiently without building duplicative systems? We should be thinking more during the design and collection phase about whether the data is going to be interoperable and who needs to access it.
How can we get the right balance in terms of data sharing? Some donors really push for information that can put people in real danger – like details of people who have participated in particular programs that would put them at risk with their home governments. Organizations really need to push back against this. It’s an education thing with donors. Middle management and intermediaries are often the ones that push for this type of data because they don’t really have a handle on the risk it represents. They are the weak points because of the demands they are putting on people. This is a challenge for open data policies – leaving it open to people leaves it to doing the laziest job possible of thinking about the potential risks for that data.
There are legal aspects of sharing too – such as the USAID open data policy where those collecting data have to share with the government. But we don’t have a clear understanding of what the international laws are about data sharing.
There are so many pressures to share data but they are not all fully thought through!

Data analysis and use of data are key weak spots for organizations.

We are just beginning to think through capturing lots of data.
Data is collected but not always used. Too often it’s extractive data collection. We don’t have the feedback loops in place, and when there are feedback loops we often don’t use the the feedback to make changes.
We forget often to go back to the people who have provided us with data to share back with them. It’s not often that we hold a consultation with the community to really involve them in how the data can be used.

Secure storage is a challenge.

We have hundreds of databases across the agency in various formats, hard drives and states of security, privacy and safety. Are we able to keep these secure?
We need to think more carefully about where we hold our data and who has access to it. Sometimes our data is held by external consultants. How should we be addressing that?

Disposing of data properly in a global context is hard!

It’s difficult to dispose of data when there are multiple versions of it and a data footprint.
Disposal is an issue. We’re doing a lot of server upgrades and many of these are remote locations. How do we ensure that the right disposal process is going on globally, short of physically seeing that hard drives are smashed up!
We need to do a better job of disposal on personal laptops. I’ve done a lot of data collection on my personal laptop – no one has ever followed up to see if I’ve deleted it. How are we handling data handover? How do you really dispose of data?
Our organization hasn’t even thought about this yet!

Tips and recommendations from participants

Organizations should be using different tools. They should be using Pretty Good Privacy techniques rather than relying on free or commercial tools like Google or Skype.
People can be your weakest link if they are not aware or they don’t care about privacy and security. We send an email out to all staff on a weekly basis that talks about taking adequate measures. We share tips and stories. That helps to keep privacy and security front and center.
Even if you have a policy the hard part is enforcement, accountability, and policy reform. If our organizations are not doing direct policy around the formation of best practices in this area, then it’s on us to be sure we understand what is best practice, and to advocate for that. Let’s do what we can before the policy catches up.
The Responsible Data Forum and Tactical Tech have a great set of resources.
Oxfam has a Responsible Data Policy and Girl Effect have developed a Girls’ Digital Privacy, Security and Safety Toolkit that can also offer some guidance.

In conclusion, participants agreed that development agencies and NGOs need to take privacy, security and safety seriously. They can no longer afford to implement security at a lower level than corporations. “Times are changing and hackers are no longer just interested in financial information. People’s data is very valuable. We need to change and take security as seriously as corporates do!” as one person said.

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Responsible open data: an oxymoron? Discuss!

Posted in accountability, data, development, IATI, ICTs, mobile and technology, open data, open development, open government, open knowledge, participation, privacy, risk, technology salon, wait... what?, tagged debate, human subjects, open data, personally identifiable data, policy, practice, privacy, responsible data, Technology Salon on April 13, 2016| 1 Comment »

At our April 5^th Salon in Washington, DC we had the opportunity to take a closer look at open data and privacy and discuss the intersection of the two in the framework of ‘responsible data’. Our lead discussants were Amy O’Donnell, Oxfam GB; Rob Baker, World Bank; Sean McDonald, FrontlineSMS. I had the pleasure of guest moderating.

What is Responsible Data?

We started out by defining ‘responsible data‘ and some of the challenges when thinking about open data in a framework of responsible data.

The Engine Room defines ‘responsible data’ as

the duty to ensure people’s rights to consent, privacy, security and ownership around the information processes of collection, analysis, storage, presentation and reuse of data, while respecting the values of transparency and openness.

Responsible Data can be like walking a tightrope, noted our first discussant, and you need to find the right balance between opening data and sharing it, all the while being ethical and responsible. “Data is inherently related to power – it can create power, redistribute it, make the powerful more powerful or further marginalize the marginalized. Getting the right balance involves asking some key questions throughout the data lifecycle from design of the data gathering all the way through to disposal of the data.

How can organizations be more responsible?

If an organization wants to be responsible about data throughout the data life cycle, some questions to ask include:

In whose interest is it to collect the data? Is it extractive or empowering? Is there informed consent?
What and how much do you really need to know? Is the burden of collecting and the liability of storing the data worth it when balanced with the data’s ability to represent people and allow them to be counted and served? Do we know what we’ll actually be doing with the data?
How will the data be collected and treated? What are the new opportunities and risks of collecting and storing and using it?
Why are you collecting it in the first place? What will it be used for? Will it be shared or opened? Is there a data sharing MOU and has the right kind of consent been secured? Who are we opening the data for and who will be able to access and use it?
What is the sensitivity of the data and what needs to be stripped out in order to protect those who provided the data?

Oxfam has developed a data deposit framework to help assess the above questions and make decisions about when and whether data can be open or shared.

(The Engine Room’s Responsible Development Data handbook offers additional guidelines and things to consider)

(See: https://wiki.responsibledata.io/Data_in_the_project_lifecycle for more about the data lifecycle)

Is ‘responsible open data’ an oxymoron?

Responsible Data policies and practices don’t work against open data, our discussant noted. Responsible Data is about developing a framework so that data can be opened and used safely. It’s about respecting the time and privacy of those who have provided us with data and reducing the risk of that data being hacked. As more data is collected digitally and donors are beginning to require organizations to hand over data that has been collected with their funding, it’s critical to have practical resources and help staff to be more responsible about data.

Some disagreed that consent could be truly informed and that open data could ever be responsible since once data is open, all control over the data is lost. “If you can’t control the way the data is used, you can’t have informed people. It’s like saying ‘you gave us permission to open your data, so if something bad happens to you, oh well….” Informed consent is also difficult nowadays because data sets are being used together and in ways that were not possible when informed consent was initially obtained.

Others noted that standard informed consent practices are unhelpful, as people don’t understand what might be done with their data, especially when they have low data literacy. Involving local communities and individuals in defining what data they would like to have and use could make the process more manageable and useful for those whose data we are collecting, using and storing, they suggested.

One person said that if consent to open data was not secured initially; the data cannot be opened, say, 10 years later. Another felt that it was one thing to open data for a purpose and something entirely different to say “we’re going to open your data so people can do fun things with it, to play around with it.”

But just what data are we talking about?

USAID was questioned for requiring grantees to share data sets and for leaning towards de-identification rather than raising the standard to data anonymity. One person noted that at one point the agency had proposed a 22-step process for releasing data and even that was insufficient for protecting program participants in a risky geography because “it’s very easy to figure out who in a small community recently received 8 camels.” For this reason, exclusions are an important part of open data processes, he said.

It’s not black or white, said another. Responsible open data is possible, but openness happens along a spectrum. You have financial data on the one end, which should be very open as the public has a right to know how its tax dollars are being spent. Human subjects research is on the other end, and it should not be totally open. (Author’s note: The Open Knowledge Foundation definition of open data says: “A key point is that when opening up data, the focus is on non-personal data, that is, data which does not contain information about specific individuals.” The distinction between personal data, such as that in household level surveys, and financial data on agency or government activities seems to be blurred or blurring in current debates around open data and privacy.) “Open data will blow up in your face if it’s not done responsibly,” he noted. “But some of the open data published via IATI (the International Aid Transparency Initiative) has led to change.”

A participant followed this comment up by sharing information from a research project conducted on stakeholders’ use of IATI data in 3 countries. When people knew that the open data sets existed they were very excited, she said. “These are countries where there is no Freedom of Information Act (FOIA), and where people cannot access data because no one will give it to them. They trusted the US Government’s data more than their own government data, and there was a huge demand for IATI data. People were very interested in who was getting what funding. They wanted information for planning, coordination, line ministries and other logistical purposes. So let’s not underestimate open data. If having open data sets means that governments, health agencies or humanitarian organizations can do a better job of serving people, that may make for a different kind of analysis or decision.”

‘Open by default’ or ‘open by demand’?

Though there are plenty of good intentions and rationales for open data, said one discussant, ‘open by default’ is a mistake. We may have quick wins with a reduction in duplicity of data collection, but our experiences thus far do not merit ‘open by default’. We have not earned it. Instead, he felt that ‘open by demand’ is a better idea. “We can put out a public list of the data that’s available and see what demand for data comes in. If we are proactive on what is available and what can be made available, and we monitor requests, we can avoid putting out information that no one is interested in. This would lower the overhead on what we are releasing. It would also allow us to have a conversation about who needs this data and for what.”

One participant agreed, positing that often the only reason that we collect data is to provide proof and evidence that we’re doing our job, spending the money given to us, and tracking back. “We tend to think that the only way to provide this evidence is to collect data: do a survey, talk to people, look at website usage. But is anyone actually using this data, this evidence to make decisions?”

Is the open data honeymoon over?

“We need to do a better job of understanding the impact at a wider level,” said another participant, “and I think it’s pretty light. Talking about open data is too general. We need to be more service oriented and problem driven. The conversation is very different when you are using data to solve a particular problem and you can focus on something tangible like service delivery or efficiency. Open data is expensive and not sustainable in the current setup. We need to figure this out.”

Another person shared results from an informal study on the use of open data portals around the world. He found around 2,500 open data portals, and only 3.8% of them use https (the secure version of http). Most have very few visitors, possibly due to poor Internet access in the countries whose open data they are serving up, he said. Several exist in countries with a poor Freedom House ranking and/or in countries at the bottom end of the World Bank’s Digital Dividends report. “In other words, the portals have been built for people who can’t even use them. How responsible is this?” he asked, “And what is the purpose of putting all that data out there if people don’t have the means to access it and we continue to launch more and more portals? Where’s all this going?”

Are we conflating legal terms?

Legal frameworks around data ownership were debated. Some said that the data belonged to the person or agency that collected it or paid for the cost of collecting in terms of copyright and IP. Others said that the data belonged to the individual who provided it. (Author’s note: Participants may have been referring to different categories of data, eg., financial data from government vs human subjects data.) The question was raised of whether informed consent for open data in the humanitarian space is basically a ‘contract of adhesion’ (a term for a legally binding agreement between two parties wherein one side has all the bargaining power and uses it to its advantage). Asking a person to hand over data in an emergency situation in order to enroll in a humanitarian aid program is akin to holding a gun to a person’s head in order to get them to sign a contract, said one person.

There’s a world of difference between ‘published data’ and ‘openly licensed data,’ commented our third discussant. “An open license is a complete lack of control, and you can’t be responsible with something you can’t control. There are ways to be responsible about the way you open something, but once it’s open, your responsibility has left the port.” ‘Use-based licensing’ is something else, and most IP is governed by how it’s used. For example, educational institutions get free access to data because they are educational institutions. Others pay and this subsidized their use of this data, he explained.

One person suggested that we could move from the idea of ‘open data’ to sub-categories related to how accessible the data would be and to whom and for what purposes. “We could think about categories like: completely open, licensed, for a fee, free, closed except for specific uses, etc.; and we could also specify for whom, whose data and for what purposes. If we use the term ‘accessible’ rather than ‘open’ perhaps we can attach some restrictions to it,” she said.

Is data an asset or a liability?

Our current framing is wrong, said one discussant. We should think of data as a toxic asset since as soon as it’s in our books and systems, it creates proactive costs and proactive risks. Threat modeling is a good approach, he noted. Data can cause a lot of harm to an organization – it’s a liability, and if it’s not used or stored according to local laws, an agency could be sued. “We’re far under the bar. We are not compliant with ‘safe harbor’ or ECOWAS regulations. There are libel questions and property laws that our sector is ignorant of. Our good intentions mislead us in terms of how we are doing things. There is plenty of room to build good practice here, he noted, for example through Civic Trusts. Another participant noted that insurance underwriters are already moving into this field, meaning that they see growing liability in this space.

How can we better engage communities and the grassroots?

Some participants shared examples of how they and their organizations have worked closely at the grassroots level to engage people and communities in protecting their own privacy and using open data for their own purposes. Threat modeling is an approach that helps improve data privacy and security, said one. “When we do threat modeling, we treat the data that we plan to collect as a potential asset. At each step of collection, storage, sharing process – we ask, ‘how will we protect those assets? What happens if we don’t share that data? If we don’t collect it? If we don’t delete it?’”

In one case, she worked with very vulnerable women working on human rights issues and together the group put together an action plan to protect its data from adversaries. The threats that they had predicted actually happened and the plan was put into action. Threat modeling also helps to “weed the garden once you plant it,” she said, meaning that it helps organizations and individuals keep an eye on their data, think about when to delete data, pay attention to what happens after data’s opened and dedicate some time for maintenance rather than putting all their attention on releasing and opening data.

More funding needs to be made available for data literacy for those whose data has been collected and/or opened. We need to help people think about what data is of use to them also. One person recalled hearing people involved in the creation of the Kenya Open Government Data portal say that the entire process was a waste of time because of low levels of use of any of the data. There are examples, however, of people using open data and verifying it at community level. For example, high school students in one instance found the data on all the so-called grocery stores in their community and went one-by-one checking into them, and identifying that some of these were actually liquor stores selling potato chips, not actual grocery stores. Having this information and engaging with it can be powerful for local communities’ advocacy work.

Are we the failure here? What are we going to do about it?

One discussant felt that ‘data’ and ‘information’ are often and easily conflated. “Data alone is not power. Information is data that is contextualized into something that is useful.” This brings into question the value of having so many data portals, and so much risk, when so little is being done to turn data into information that is useful to the people our sector says it wants to support and empower.

He gave the example of the Weather Channel, a business built around open data sets that are packaged and broadcast, which just got purchased for $2 billion. Channels like radio that would have provided information to the poor were not purchased, only the web assets, meaning that those who benefit are not the disenfranchised. “Our organizations are actually just like the Weather Channel – we are intermediaries who are interested in taking and using open data for public good.”

As intermediaries, we can add value in the dissemination of this open data, he said. If we have the skills, the intention and the knowledge to use it responsibly, we have a huge opportunity here. “However our enlightened intent has not yet turned this data into information and knowledge that communities can use to improve their lives, so are we the failure here? And if so, what are we doing about it? We could immediately begin engaging communities and seeing what is useful to them.” (See this article for more discussion on how ‘open’ may disenfranchise the poor.)

Where to from here?

Some points raised that merit further discussion and attention include:

There is little demand or use of open data (such as government data and finances) and preparing and maintaining data sets is costly – ‘open by demand’ may be a more appropriate approach than ‘open by default.’
There is a good deal of disagreement about whether data can be opened responsibly. Some of this disagreement may stem from a lack of clarity about what kind of data we are talking about when we talk about open data.
Personal data and human subjects data that was never foreseen to be part of “open data” is potentially being opened, bringing with it risks for those who share it as well as for those who store it.
Informed consent for personal/human subject data is a tricky concept and it’s not clear whether it is even possible in the current scenario of personal data being ‘opened’ and the lack of control over how it may be used now or in the future, and the increasing ease of data re-identification.
We may want to look at data as a toxic asset rather than a beneficial one, because of the liabilities it brings.
Rather than a blanket “open” categorization, sub-categorizations that restrict data sets in different ways might be a possibility.
The sector needs to improve its understanding of the legal frameworks around data and data collection, storage and use or it may start to see lawsuits in the near future.
Work on data literacy and community involvement in defining what data is of interest and is collected, as well as threat modeling together with community groups is a way to reduce risk and improve data quality, demand and use; but it’s a high-touch activity that may not be possible for every kind of organization.
As data intermediaries, we need to do a much better job as a sector to see what we are doing with open data and how we are using it to provide services and contextualized information to the poor and disenfranchised. This is a huge opportunity and we have not done nearly enough here.

The Technology Salon is conducted under Chatham House Rule so attribution has not been made in this post. If you’d like to attend future Salons, sign up here.

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A discussion on girls’ digital privacy, security and safety

Posted in children, data, development, m4D, mobile and technology, privacy, protection, technology salon, TSNYC, tagged girls, guidelines, NYC, privacy, protection, risk, risk matrix, security, Technology Salon, toolkit on January 6, 2016| 1 Comment »

Our December 2015 Technology Salon discussion in NYC focused on approaches to girls’ digital privacy, safety and security. By extension, the discussion included ways to reduce risk for other vulnerable populations. Our lead discussants were Ximena Benavente, Girl Effect Mobile (GEM) and Jonathan McKay, Praekelt Foundation. I also shared a draft Girls’ Digital Privacy, Safety and Security Policy and Toolkit I’ve been working on with both organizations over the past year.

Girls’ digital privacy, safety and security risks

Our first discussant highlighted why it’s important to think specifically about girls and digital security. In part, this is because different factors and vulnerabilities combine, exacerbating girls’ levels of risk. For example, girls living on less than $2 per day likely only have access to basic mobile phones, which are often borrowed from parents or siblings. The organization she works with always starts with deep research on aspects like ownership vs. borrowship and whether girls’ mobile usage is free/unlimited and un-supervised or controlled by gatekeepers such as parents, brothers, or other relatives. This helps to design better tools, services and platforms and to design for safety and security, she said. “Gatekeepers are very restrictive in many cases, but parental oversight is not necessarily a bad thing. We always work with parents and other gatekeepers as well as with girls themselves when we design and test.” When girls are living in more traditional or conservative societies, she said, we also need to think about how content might affect girls both online and offline. For example, “is content sufficiently progressive in terms of girls’ rights, yet safe for girls to read, comment on or discuss with friends and family without severe retaliation?”

Research suggests that girls who are more vulnerable offline (due to poverty or other forms of marginalization), are likely also more vulnerable to certain risks online, so we design with that in mind, she said. “When we started off on this project, our team members were experts in digital, but we had less experience with the safety and privacy aspects when it comes to girls living under $2/day or who were otherwise vulnerable. “Having additional guidance and developing a policy on this aspect has helped immensely – but has also slowed our processes down and sometimes made them more expensive,” she noted. “We had to go back to everything and add additional layers of security to make it as safe as possible for girls. We have also made sure to work very closely with our local partners to be sure that everyone involved in the project is aware of girls’ safety and security.”

Social media sites: Open, Closed, Private, Anonymous?

One issue that came up was safety for children and youth on social media networks. A Salon participant said his organization had thought about developing this type of a network several years back but decided in the end that the security risks outweighed the advantages. Participants discussed whether social media networks can ever be safe. One school of thought is that the more open a platform, the safer it is, as “there is no interaction in private spaces that cannot be constantly monitored or moderated.” Some worry about open sites, however, and set up smaller, closed, private groups that were closely monitored. “We work with victims of violence to share their stories and coping mechanisms, so, for us, private groups are a better option.”

Some suggested that anonymity on a social media site can protect girls and other vulnerable groups, however there is also research showing that Internet anonymity contributes to an increase in activities such as bullying and harassment. Some Salon participants felt that it was better to leverage existing platforms and try to use them safely. Others felt that there are no existing social media platforms that have enough security for girls or other vulnerable groups to use with appropriate levels of risk. “We sometimes recruit participants via existing social media platforms,” said one discussant, “but we move people off of those sites to our own more secure sites as soon as we can.”

Moderation and education on safety

Salon participants working with vulnerable populations said that they moderate their sites very closely and remove comments if users share personal information or use offensive language. “Some project budgets allow us to have a moderator check every 2 hours. For others, we sweep accounts once a day and remove offensive content within 24 hours.” One discussant uses moderation to educate the community. “We always post an explanation about why a comment was removed in order to educate the larger user base about appropriate ways to use the social network,” he said.

Close moderation becomes difficult and costly, however, as the user base grows and a platform scales. This means individual comments cannot be screened and pre-approved, because that would take too long and defeat the purpose of an engaging platform. “We need to acknowledge the very real tension between building a successful and engaging community and maintaining privacy and security,” said one Salon participant. “The more you lock it down and the more secure it is, the harder you find it is to create a real and active community.”

Another participant noted that they use their safe, closed youth platform to educate and reinforce messaging about what is safe and positive use of social media in hopes that young people will practice safe behaviors when they use other platforms. “We know that education and awareness raising can only go so far, however,” she said, “and we are not blind to that fact.” She expressed concern about risk for youth who speak out about political issues, because more and more governments are passing laws that punish critics and censor information. The organization, however, does not want to encourage youth to stop voicing opinions or participating politically.

Data breaches and project close-out

One Salon participant asked if organizations had examples of actual data breaches, and how they had handled them. Though no one shared examples, it was recommended that every organization have a contingency plan in place for accidental data leaks or a data breach or data hack. “You need to assume that you will get hacked,” said one person, “and develop your systems with that as a given.”

In addition to the day-to-day security issues, we need to think about project close-out, said one person. “Most development interventions are funded for a short, specific period of time. When a project finishes, you get a report, you do your M&E, and you move on. However, the data lives on, and the effects of the data live on. We really need to think more about budgeting for proper project wind-down and ensure that we are accountable beyond the lifetime of a project.”

Data security, anonymization, consent

Another question was related to using and keeping girls’ (and others’) data safe. “Consent to collect and use data on a website or via a mobile platform can be tricky, especially if we don’t know how to explain what we might do with the data,” said one Salon participant. Others suggested it would be better not to collect any data at all. “Why do we even need to collect this data? Who is it for?” he asked. Others countered that this data is often the only way to understand what people are doing on the site, to make adjustments and to measure impact.

One scenario was shared where several partner organizations discussed opening up a country’s cell phone data records to help contain a massive public health epidemic, but the privacy and security risks were too great, so the idea was scrapped. “Some said we could anonymize the data, but you can never really and truly anonymize data. It would have been useful to have a policy or a rubric that would have guided us in making that decision.”

Policy and Guidelines on Girls Privacy, Security and Safety

Policy guidelines related to aspects such as responsible data for NGOs, data security, privacy and other aspects of digital security in general do exist. (Here are some that we compiled along with some other resources). Most IT departments also have strict guidelines when it comes to donor data (in the case of credit card and account information, for example). This does not always cross over to program-level ICT or M&E efforts that involve the populations that NGOs are serving through their programming.

General awareness around digital security is increasing, in part due to recent major corporate data hacks (e.g., Target, Sony) and the Edward Snowden revelations from a few years back, but much more needs to be done to educate NGO staff and management on the type of privacy and security measures that need to be taken to protect the data and mitigate risk for those who participate in their programs. There is an argument that NGOs should have specific digital privacy, safety and security policies that are tailored to their programming and that specifically focus on the types of digital risks that girls, women, children or other vulnerable people face when they are involved in humanitarian or development programs.

One such policy (focusing on vulnerable girls) and toolkit (its accompanying principles and values, guidelines, checklists and a risk matrix template); was shared at the Salon. (Disclosure: – This policy toolkit is one that I am working on. It should be ready to share in early 2016). The policy and toolkit take program implementers through a series of issues and questions to help them assess potential risks and tradeoffs in a particular context, and to document decisions and improve accountability. The toolkit covers:

data privacy and security –using approaches like Privacy by Design, setting limits on the data that is collected, achieving meaningful consent.
platform content and design –ensuring that content produced for girls or that girls produce or volunteer is not putting girls at risk.
partnerships –vetting and managing partners who may be providing online/offline services or who may partner on an initiative and want access to data, monetizing of girls’ data.
monitoring, evaluation, research and learning (MERL) – how will program implementers gather and store digital data when they are collecting it directly or through third parties for organizational MERL purposes.

Privacy, Security and Safety Implications

Our final discussant spoke about the implications of implementing the above-mentioned girls’ privacy, safety and security policy. He started out saying that the policy starts off with a manifesto: We will not compromise a girl in any way, nor will we opt for solutions that cut corners in terms of cost, process or time at the expense of her safety. “I love having this as part of our project manifesto, he said. “It’s really inspiring! On the flip side, however, it makes everything I do more difficult, time consuming and expensive!”

To demonstrate some of the trade-offs and decisions required when working with vulnerable girls, he gave examples of how the current project (implemented with girls’ privacy and security as a core principle) differed from that of a commercial social media platform and advertising campaign he had previously worked on (where the main concern was the reputation of the corporation, not that of the users of the platform and the potential risks they might put themselves in by using the platform).

Moderation

On the private sector platform, said the discussant, “we didn’t have the option of pre-moderating comments because of the budget and because we had 800 thousand users. To meet the campaign goals, it was more important for users to be engaged than to ensure content was safe. We focused on removing pornographic photos within 24 hours, using algorithms based on how much skin tone was in the photo.” In the fields of marketing and social media, it’s a fairly well-known issue that heavy-handed moderation kills platform engagement. “The more we educated and informed users about comment moderation, or removed comments, the deader the community became. The more draconian the moderation, the lower the engagement.”

The discussant had also worked on a platform for youth to discuss and learn about sexual health and practices, where he said that users responded angrily to moderators and comments that restricted their participation. “We did expose our participants to certain dangers, but we also knew that social digital platforms are more successful when they provide their users with sense of ownership and control. So we identified users that exhibited desirable behaviors and created a different tier of users who could take ownership (super users) to police and flag comments as inappropriate or temporarily banned users.” This allowed a 25% decrease in moderation. The organization discovered, however, that they had to be careful about how much power these super users had. “They ended up creating certain factions on the platform, and we then had to develop safeguards and additional mechanisms by which we moderated our super users!”

Direct Messages among users

In the private sector project example, engagement was measured by the number of direct or private messages sent between platform users. In the current scenario, however, said the discussant, “we have not allowed any direct messages between platform users because of the potential risks to girls of having places on the site that are hidden from moderators. So as you can see, we are removing some of our metrics by disallowing features because of risk. These activities are all things that would make the platform more engaging but there is a big fear that they could put girls at risk.”

Adopting a privacy, security, and safety policy

One discussant highlighted the importance of having privacy, safety and security policies before a project or program begins. “If you start thinking about it later on, you may have to go back and rebuild things from scratch because your security holes are in the design….” The way a database is set up to capture user data can make it difficult to query in the future or for users to have any control of what information is or is not being shared about them. “If you don’t set up the database with security and privacy in mind from the beginning, it might be impossible to make the platform safe for girls without starting from scratch all over again,” he said.

He also cautioned that when making more secure choices from the start, platform and tool development generally takes longer and costs more. It can be harder to budget because designers may not have experience with costing and developing the more secure options.

“A valuable lesson is that you have to make sure that what you’re trying to do in the first place is worth it if it’s going to be that expensive. It is worth a girls’ while to use a platform if she first has to wade through a 5-page terms and conditions on a small mobile phone screen? Are those terms and conditions even relevant to her personally or within her local context? Every click you ask a user to make will reduce their interest in reaching the platform. And if we don’t imagine that a girl will want to click through 5 screens of terms and conditions, the whole effort might not be worth it.” Clearly, aspects such as terms and conditions and consent processes need to be designed specifically to fit new contexts and new kinds of users.

Making responsible tradeoffs

The Girls Privacy, Security and Safety policy and toolkit shared at the Salon includes a risk matrix where project implementers rank the intensity and probability of risks as high, medium and low. Based on how a situation, feature or other potential aspect is ranked and the possibility to mitigate serious risks, decisions are made to proceed or not. There will always be areas with a certain level of risk to the user. The key is in making decisions and trade-offs that balance the level of risk with the potential benefits or rewards of the tool, service, or platform. The toolkit can also help project designers to imagine potential unintended consequences and mitigate risk related to them. The policy also offers a way to systematically and pro-actively consider potential risks, decide how to handle them, and document decisions so that organizations and project implementers are accountable to girls, peers and partners, and organizational leadership.

“We’ve started to change how we talk about user data in our organization,” said one discussant. “We have stopped thinking about it as something WE create and own, but more as something GIRLS own. Banks don’t own people’s money – they borrow it for a short time. We are trying to think about data that way in the conversations we’re having about data, funding, business models, proposals and partnerships. You don’t get to own your users’ data, we’re not going to share de-anonymized data with you. We’re seeing legislative data in some of the countries we work that are going that way also, so it’s good to be thinking about this now and getting prepared”

Take a look at our list of resources on the topic and add anything we may have missed!

Thanks to our friends at ThoughtWorks for hosting this Salon! If you’d like to join discussions like this one, sign up at Technology Salon. Salons are held under Chatham House Rule, therefore no attribution has been made in this post.

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ICT for M&E: Diploma Course

Posted in data, development, evaluation, ICT4D, ICTs, M&E, M&E Tech, mobile and technology, monitoring, monitoring and evaluation, technology, tagged course, diploma, evaluation, higher education, learning, monitoring, online, techchange, technology on September 9, 2015| 1 Comment »

Back in 2010, I wrote a post called “Where’s the ICT4D distance learning?” which lead to some interesting discussions, including with the folks over at TechChange, who were just getting started out. We ended up co-hosting a Twitter chat (summarized here) and having some great discussions on the lack of opportunities for humanitarian and development practitioners to professionalize their understanding of ICTs in their work.

It’s pretty cool today, then, to see that in addition to having run a bunch of on-line short courses focused on technology and various aspects of development and social change work, TechChange is kicking off their first Diploma program focusing on using ICT for monitoring and evaluation — an area that has become increasingly critical over the past few years.

I’ve participated in a couple of these short courses, and what I like about them is that they are not boring one-way lectures. Though you are studying at a distance, you don’t feel like you’re alone. There are variations on the type and length of the educational materials including short and long readings, videos, live chats and discussions with fellow students and experts, and smaller working groups. The team and platform do a good job of providing varied pedagogical approaches for different learning styles.

The new Diploma in ICT and M&E program has tracks for working professionals (launching in September of 2015) and prospective Graduate Students (launching in January 2016). Both offer a combination of in-person workshops, weekly office hours, a library of interactive on-demand courses, access to an annual conference, and more. (Disclaimer – you might see some of my blog posts and publications there).

The graduate student track will also have a capstone project, portfolio development support, one-on-one mentorship, live simulations, and a job placement component. Both courses take 16 weeks of study, but these can be spread out over a whole year to provide maximum flexibility.

For many of us working in the humanitarian and development sectors, work schedules and frequent travel make it difficult to access formal higher-level schooling. Not to mention, few universities offer courses related to ICTs and development. The idea of incurring a huge debt is also off-putting for a lot of folks (including me!). I’m really happy to see good quality, flexible options for on-line learning that can improve how we do our work and that also provides the additional motivation of a diploma certificate.

You can find out more about the Diploma program on the TechChange website (note: registration for the fall course ends September 11th).

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13 tips on data dashboards for decision-making

Posted in data, development, evaluation, ICTs, M&E, M&E Tech, monitoring, monitoring and evaluation, technology salon, TSNYC, wait... what?, tagged dashboards, data, decision, evaluation, making, monitoring, Technology Salon, TSNYC on June 5, 2015| 3 Comments »

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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