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At the 2016 American Evaluation Association conference, I chaired a session on benefits and challenges with ICTs in Equity-Focused Evaluation. The session frame came from a 2016 paper on the same topic. Panelists Kecia Bertermann from Girl Effect, and Herschel Sanders from RTI added fascinating insights on the methodological challenges to consider when using ICTs for evaluation purposes and discussant Michael Bamberger closed out with critical points based on his 50+ years doing evaluations.

ICTs include a host of technology-based tools, applications, services, and platforms that are overtaking the world. We can think of them in three key areas: technological devices, social media/internet platforms and digital data.

An equity focus evaluation implies ensuring space for the voices of excluded groups and avoiding the traditional top-down approach. It requires:

  • Identifying vulnerable groups
  • Opening up space for them to make their voices heard through channels that are culturally responsive, accessible and safe
  • Ensuring their views are communicated to decision makers

It is believed that ICTs, especially mobile phones, can help with inclusion in the implementation of development and humanitarian programming. Mobile phones are also held up as devices that can allow evaluators to reach isolated or marginalized groups and individuals who are not usually engaged in research and evaluation. Often, however, mobiles only overcome geographic inclusion. Evaluators need to think harder when it comes to other types of exclusion – such as that related to disability, gender, age, political status or views, ethnicity, literacy, or economic status – and we need to consider how these various types of exclusions can combine to exacerbate marginalization (e.g., “intersectionality”).

We are seeing increasing use of ICTs in evaluation of programs aimed at improving equity. Yet these tools also create new challenges. The way we design evaluations and how we apply ICT tools can make all the difference between including new voices and feedback loops or reinforcing existing exclusions or even creating new gaps and exclusions.

Some of the concerns with the use of ICTs in equity- based evaluation include:

Methodological aspects:

  • Are we falling victim to ‘elite capture’ — only hearing from higher educated, comparatively wealthy men, for example? How does that bias our information? How can we offset that bias or triangulate with other data and multi-methods rather than depending only on one tool-based method?
  • Are we relying too heavily on things that we can count or multiple-choice responses because that’s what most of these new ICT tools allow?
  • Are we spending all of our time on a device rather than in communities engaging with people and seeking to understand what’s happening there in person?
  • Is reliance on mobile devices or self-reporting through mobile surveys causing us to miss contextual clues that might help us better interpret the data?
  • Are we falling into the trap of fallacy in numbers – in other words, imagining that because lots of people are saying something, that it’s true for everyone, everywhere?

Organizational aspects:

  • Do digital tools require a costly, up-front investment that some organizations are not able to make?
  • How do fear and resistance to using digital tools impact on data gathering?
  • What kinds of organizational change processes are needed amongst staff or community members to address this?
  • What new skills and capacities are needed?

Ethical aspects:

  • How are researchers and evaluators managing informed consent considering the new challenges to privacy that come with digital data? (Also see: Rethinking Consent in the Digital Age)?
  • Are evaluators and non-profit organizations equipped to keep data safe?
  • Is it possible to anonymize data in the era of big data given the capacity to cross data sets and re-identify people?
  • What new risks might we be creating for community members? To local enumerators? To ourselves as evaluators? (See: Developing and Operationalizing Responsible Data Policies)

Evaluation of Girl Effect’s online platform for girls

Kecia walked us through how Girl Effect has designed an evaluation of an online platform and applications for girls. She spoke of how the online platform itself brings constraints because it only works on feature phones and smart phones, and for this reason it was decided to work with 14-16 year old urban girls in megacities who have access to these types of devices yet still experience multiple vulnerabilities such as gender-based violence and sexual violence, early pregnancy, low levels of school completion, poor health services and lack of reliable health information, and/or low self-esteem and self-confidence.

The big questions for this program include:

  • Is the content reaching the girls that Girl Effect set out to reach?
  • Is the content on the platform contributing to change?

Because the girl users are on the platform, Girl Effect can use features such as polls and surveys for self-reported change. However, because the girls are under 18, there are privacy and security concerns that sometimes limit the extent to which the organization feels comfortable tracking user behavior. In addition, the type of phones that the girls are using and the fact that they may be borrowing others’ phones to access the site adds another level of challenges. This means that Girl Effect must think very carefully about the kind of data that can be gleaned from the site itself, and how valid it is.

The organization is using a knowledge, attitudes and practices (KAP) framework and exploring ways that KAP can be measured through some of the exciting data capture options that come with an online platform. However it’s hard to know if offline behavior is actually shifting, making it important to also gather information that helps read into the self-reported behavior data.

Girl Effect is complementing traditional KAP indicators with web analytics (unique users, repeat visitors, dwell times, bounce rates, ways that users arrive to the site) with push-surveys that go out to users and polls that appear after an article (“Was this information helpful? Was it new to you? Did it change your perceptions? Are you planning to do something different based on this information?”) Proxy indicators are also being developed to help interpret the data. For example, does an increase in frequency of commenting on the site by a particular user have a link with greater self-esteem or self-efficacy?

However, there is only so much that can be gleaned from an online platform when it comes to behavior change, so the organization is complementing the online information with traditional, in-person, qualitative data gathering. The site is helpful there, however, for recruiting users for focus groups and in-depth interviews. Girl Effect wants to explore KAP and online platforms, yet also wants to be careful about making assumptions and using proxy indicators, so the traditional methods are incorporated into the evaluation as a way of triangulating the data. The evaluation approach is a careful balance of security considerations, attention to proxy indicators, digital data and traditional offline methods.

Using SMS surveys for evaluation: Who do they reach?

Herschel took us through a study conducted by RTI (Sanders, Lau, Lombaard, Baker, Eyerman, Thalji) in partnership with TNS about the use of SMS surveys for evaluation. She noted that the rapid growth of mobile phones, particularly in African countries, opens up new possibilities for data collection. There has been an explosion of SMS surveys for national, population-based surveys.

Like most ICT-enabled MERL methods, use of SMS for general population surveys brings both promise:

  • High mobile penetration in many African countries means we can theoretically reach a large segment of the population.
  • These surveys are much faster and less expensive than traditional face-to- face surveys.
  • SMS surveys work on virtually any GSM phone.
  • SMS offers the promise of reach. We can reach a large and geographically dispersed population, including some areas that are excluded from FTF surveys because of security concerns.

And challenges:

  • Coverage: We cannot include illiterate people or those without access to a mobile phone. Also, some sample frames may not include the entire population with mobile phones.
  • Non-response: Response rates are expected to be low for a variety of reasons, including limited network connectivity or electricity; if two or people share a phone, we may not reach all people associated with that phone; people may feel a lack of confidence with technology. These factors might affect certain sub-groups differently, so we might underrepresent the poor, rural areas, or women.
  • Quality of measurement. We only have 160 CHARACTERS for both the question AND THE RESPONSE OPTIONS. Further, an interviewer is not present to clarify any questions.

RTI’s research aimed to answer the question: How representative are general population SMS surveys and are there ways to improve representativeness?

Three core questions were explored via SMS invitations sent in Kenya, Ghana, Nigeria and Uganda:

  • Does the sample frame match the target population?
  • Does non-response have an impact on representativeness?
  • Can we improve quality of data by optimizing SMS designs?

One striking finding was the extent to which response rates may vary by country, Hershel said. In some cases this was affected by agreements in place in each country. Some required a stronger opt-in process. In Kenya and Uganda, where a higher percentage of users had already gone through an opt-in process and had already participated in SMS-based surveys, there was a higher rate of response.

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These response rates, especially in Ghana and Nigeria, are noticeably low, and the impact of the low response rates in Nigeria and Ghana is evident in the data. In Nigeria, where researchers compared the SMS survey results against the face-to-face data, there was a clear skew away from older females, towards those with a higher level of education and who are full-time employed.

Additionally, 14% of the face-to-face sample, filtered on mobile users, had a post-secondary education, whereas in the SMS data this figure is 60%.

Additionally, Compared to face-to-face data, SMS respondents were:

  • More likely to have more than 1 SIM card
  • Less likely to share a SIM card
  • More likely to be aware of and use the Internet.

This sketches a portrait of a more technological savvy respondent in the SMS surveys, said Herschel.

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The team also explored incentives and found that a higher incentive had no meaningful impact, but adding reminders to the design of the SMS survey process helped achieve a wider slice of the sample and a more diverse profile.

Response order effects were explored along with issues related to questionnaire designers trying to pack as much as possible onto the screen rather than asking yes/no questions. Hershel highlighted that that when multiple-choice options were given, 76% of SMS survey respondents only gave 1 response compared to 12% for the face-to-face data.

screen-shot-2016-11-03-at-2-23-53-pmLastly, the research found no meaningful difference in response rate between a survey with 8 questions and one with 16 questions, she said. This may go against common convention which dictates that “the shorter, the better” for an SMS survey. There was no observable break off rate based on survey length, giving confidence that longer surveys may be possible via SMS than initially thought.

Hershel noted that some conclusions can be drawn:

  • SMS excels for rapid response (e.g., Ebola)
  • SMS surveys have substantial non-response errors
  • SMS surveys overrepresent

These errors mean SMS cannot replace face-to-face surveys … yet. However, we can optimize SMS survey design now by:

  • Using reminders during data collection
  • Be aware of response order effects. So we need to randomize substantive response options to avoid bias.
  • Not using “select all that apply” questions. It’s ok to have longer surveys.

However, she also noted that the landscape is rapidly changing and so future research may shed light on changing reactions as familiarity with SMS and greater access grow.

Summarizing the opportunities and challenges with ICTs in Equity-Focused Evaluation

Finally we heard some considerations from Michael, who said that people often get so excited about possibilities for ICT in monitoring, evaluation, research and learning that they neglect to address the challenges. He applauded Girl Effect and RTI for their careful thinking about the strengths and weaknesses in the methods they are using. “It’s very unusual to see the type of rigor shown in these two examples,” he said.

Michael commented that a clear message from both presenters and from other literature and experiences is the need for mixed methods. Some things can be done on a phone, but not all things. “When the data collection is remote, you can’t observe the context. For example, if it’s a teenage girl answering the voice or SMS survey, is the mother-in-law sitting there listening or watching? What are the contextual clues you are missing out on? In a face-to-face context an evaluator can see if someone is telling the girl how to respond.”

Additionally,“no survey framework will cover everyone,” he said. “There may be children who are not registered on the school attendance list that is being used to identify survey respondents. What about immigrants who are hiding from sight out of fear and not registered by the government?” He cautioned evaluators to not forget about folks in the community who are totally missed out and skipped over, and how the use of new technology could make that problem even greater.

Another point Michael raised is that communicating through technology channels creates a different behavior dynamic. One is not better than the other, but evaluators need to be aware that they are different. “Everyone with teenagers knows that the kind of things we communicate online are very different than what we communicate in a face-to-face situation,” he said. “There is a style of how we communicate. You might be more frank and honest on an online platform. Or you may see other differences in just your own behavior dynamics on how you communicate via different kinds of tools,” he said.

He noted that a range of issues has been raised in connection to ICTs in evaluation, but that it’s been rare to see priority given to evaluation rigor. The study Herschel presented was one example of a focus on rigor and issues of bias, but people often get so excited that they forget to think about this. “Who has access.? Are people sharing phones? What are the gender dynamics? Is a husband restricting what a woman is doing on the phone? There’s a range of selection bias issues that are ignored,” he said.

Quantitative bias and mono-methods are another issue in ICT-focused evaluation. The tool choice will determine what an evaluator can ask and that in turn affects the quality of responses. This leads to issues with construct validity. If you are trying to measure complex ideas like girls’ empowerment and you reduce this to a proxy, there can often be a large jump in interpretation. This doesn’t happen only when using mobile phones for evaluation data collection purposes but there are certain areas that may be exacerbated when the phone is the tool. So evaluators need to better understand behavior dynamics and how they related to the technical constraints of a particular digital or mobile platform.

The aspect of information dissemination is another one worth raising, said Michael. “What are the dynamics? When we incorporate new tools, we tend to assume there is just one-step between the information sharer and receiver, yet there is plenty of literature that shows this is normally at least 2 steps. Often people don’t get information directly, but rather they share and talk with someone else who helps them verify and interpret the information they get on a mobile phone. There are gatekeepers who control or interpret, and evaluators need to better understand those dynamics. Social network analysis can help with that sometimes – looking at who communicates with whom? Who is part of the main infuencer hub? Who is marginalized? This could be exciting to explore more.”

Lastly, Michael reiterated the importance of mixed methods and needing to combine online information and communications with face-to-face methods and to be very aware of invisible groups. “Before you do an SMS survey, you may need to go out to the community to explain that this survey will be coming,” he said. “This might be necessary to encourage people to even receive the survey, to pay attention or to answer it.” The case studies in the paper “The Role of New ICTs in Equity-Focused Evaluation: Opportunities and Challenges” explore some of these aspects in good detail.

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Over the past 4 years I’ve had the opportunity to look more closely at the role of ICTs in Monitoring and Evaluation practice (and the privilege of working with Michael Bamberger and Nancy MacPherson in this area). When we started out, we wanted to better understand how evaluators were using ICTs in general, how organizations were using ICTs internally for monitoring, and what was happening overall in the space. A few years into that work we published the Emerging Opportunities paper that aimed to be somewhat of a landscape document or base report upon which to build additional explorations.

As a result of this work, in late April I had the pleasure of talking with the OECD-DAC Evaluation Network about the use of ICTs in Evaluation. I drew from a new paper on The Role of New ICTs in Equity-Focused Evaluation: Opportunities and Challenges that Michael, Veronica Olazabal and I developed for the Evaluation Journal. The core points of the talk are below.

*****

In the past two decades there have been 3 main explosions that impact on M&E: a device explosion (mobiles, tablets, laptops, sensors, dashboards, satellite maps, Internet of Things, etc.); a social media explosion (digital photos, online ratings, blogs, Twitter, Facebook, discussion forums, What’sApp groups, co-creation and collaboration platforms, and more); and a data explosion (big data, real-time data, data science and analytics moving into the field of development, capacity to process huge data sets, etc.). This new ecosystem is something that M&E practitioners should be tapping into and understanding.

In addition to these ‘explosions,’ there’s been a growing emphasis on documentation of the use of ICTs in Evaluation alongside a greater thirst for understanding how, when, where and why to use ICTs for M&E. We’ve held / attended large gatherings on ICTs and Monitoring, Evaluation, Research and Learning (MERL Tech). And in the past year or two, it seems the development and humanitarian fields can’t stop talking about the potential of “data” – small data, big data, inclusive data, real-time data for the SDGs, etc. and the possible roles for ICT in collecting, analyzing, visualizing, and sharing that data.

The field has advanced in many ways. But as the tools and approaches develop and shift, so do our understandings of the challenges. Concern around more data and “open data” and the inherent privacy risks have caught up with the enthusiasm about the possibilities of new technologies in this space. Likewise, there is more in-depth discussion about methodological challenges, bias and unintended consequences when new ICT tools are used in Evaluation.

Why should evaluators care about ICT?

There are 2 core reasons that evaluators should care about ICTs. Reason number one is practical. ICTs help address real world challenges in M&E: insufficient time, insufficient resources and poor quality data. And let’s be honest – ICTs are not going away, and evaluators need to accept that reality at a practical level as well.

Reason number two is both professional and personal. If evaluators want to stay abreast of their field, they need to be aware of ICTs. If they want to improve evaluation practice and influence better development, they need to know if, where, how and why ICTs may (or may not) be of use. Evaluation commissioners need to have the skills and capacities to know which new ICT-enabled approaches are appropriate for the type of evaluation they are soliciting and whether the methods being proposed are going to lead to quality evaluations and useful learnings. One trick to using ICTs in M&E is understanding who has access to what tools, devices and platforms already, and what kind of information or data is needed to answer what kinds of questions or to communicate which kinds of information. There is quite a science to this and one size does not fit all. Evaluators, because of their critical thinking skills and social science backgrounds, are very well placed to take a more critical view of the role of ICTs in Evaluation and in the worlds of aid and development overall and help temper expectations with reality.

Though ICTs are being used along all phases of the program cycle (research/diagnosis and consultation, design and planning, implementation and monitoring, evaluation, reporting/sharing/learning) there is plenty of hype in this space.

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There is certainly a place for ICTs in M&E, if introduced with caution and clear analysis about where, when and why they are appropriate and useful, and evaluators are well-placed to take a lead in identifying and trailing what ICTs can offer to evaluation. If they don’t, others are going to do it for them!

Promising areas

There are four key areas (I’ll save the nuance for another time…) where I see a lot of promise for ICTs in Evaluation:

1. Data collection. Here I’d divide it into 3 kinds of data collection and note that the latter two normally also provide ‘real time’ data:

  • Structured data gathering – where enumerators or evaluators go out with mobile devices to collect specific types of data (whether quantitative or qualitative).
  • Decentralized data gathering – where the focus is on self-reporting or ‘feedback’ from program participants or research subjects.
  • Data ‘harvesting’ – where data is gathered from existing online sources like social media sites, What’sApp groups, etc.
  • Real-time data – which aims to provide data in a much shorter time frame, normally as monitoring, but these data sets may be useful for evaluators as well.

2. New and mixed methods. These are areas that Michael Bamberger has been looking at quite closely. New ICT tools and data sources can contribute to more traditional methods. But triangulation still matters.

  • Improving construct validity – enabling a greater number of data sources at various levels that can contribute to better understanding of multi-dimensional indicators (for example, looking at changes in the volume of withdrawals from ATMs, records of electronic purchases of agricultural inputs, satellite images showing lorries traveling to and from markets, and the frequency of Tweets that contain the words hunger or sickness).
  • Evaluating complex development programs – tracking complex and non-linear causal paths and implementation processes by combining multiple data sources and types (for example, participant feedback plus structured qualitative and quantitative data, big data sets/records, census data, social media trends and input from remote sensors).
  • Mixed methods approaches and triangulation – using traditional and new data sources (for example, using real-time data visualization to provide clues on where additional focus group discussions might need to be done to better understand the situation or improve data interpretation).
  • Capturing wide-scale behavior change – using social media data harvesting and sentiment analysis to better understand wide-spread, wide-scale changes in perceptions, attitudes, stated behaviors and analyzing changes in these.
  • Combining big data and real-time data – these emerging approaches may become valuable for identifying potential problems and emergencies that need further exploration using traditional M&E approaches.

3. Data Analysis and Visualization. This is an area that is less advanced than the data collection area – often it seems we’re collecting more and more data but still not really using it! Some interesting things here include:

  • Big data and data science approaches – there’s a growing body of work exploring how to use predictive analytics to help define what programs might work best in which contexts and with which kinds of people — (how this connects to evaluation is still being worked out, and there are lots of ethical aspects to think about here too — most of us don’t like the idea of predictive policing, and in some ways you could end up in a situation that is not quite what was aimed at.) With big data, you’ll often have a hypothesis and you’ll go looking for patterns in huge data sets. Whereas with evaluation you normally have particular questions and you design a methodology to answer them — it’s interesting to think about how these two approaches are going to combine.
  • Data Dashboards – these are becoming very popular as people try to work out how to do a better job of using the data that is coming into their organizations for decision making. There are some efforts at pulling data from community level all the way up to UN representatives, for example, the global level consultations that were done for the SDGs or using “near real-time data” to share with board members. Other efforts are more focused on providing frontline managers with tools to better tweak their programs during implementation.
  • Meta-evaluation – some organizations are working on ways to better draw conclusions from what we are learning from evaluation around the world and to better visualize these conclusions to inform investments and decision-making.

4. Equity-focused Evaluation. As digital devices and tools become more widespread, there is hope that they can enable greater inclusion and broader voice and participation in the development process. There are still huge gaps however — in some parts of the world 23% less women have access to mobile phones — and when you talk about Internet access the gap is much much bigger. But there are cases where greater participation in evaluation processes is being sought through mobile. When this is balanced with other methods to ensure that we’re not excluding the very poorest or those without access to a mobile phone, it can help to broaden out the pool of voices we are hearing from. Some examples are:

  • Equity-focused evaluation / participatory evaluation methods – some evaluators are seeking to incorporate more real-time (or near real-time) feedback loops where participants provide direct feedback via SMS or voice recordings.
  • Using mobile to directly access participants through mobile-based surveys.
  • Enhancing data visualization for returning results back to the community and supporting community participation in data interpretation and decision-making.

Challenges

Alongside all the potential, of course there are also challenges. I’d divide these into 3 main areas:

1. Operational/institutional

Some of the biggest challenges to improving the use of ICTs in evaluation are institutional or related to institutional change processes. In focus groups I’ve done with different evaluators in different regions, this was emphasized as a huge issue. Specifically:

  • Potentially heavy up-front investment costs, training efforts, and/or maintenance costs if adopting/designing a new system at wide scale.
  • Tech or tool-driven M&E processes – often these are also donor driven. This happens because tech is perceived as cheaper, easier, at scale, objective. It also happens because people and management are under a lot of pressure to “be innovative.” Sometimes this ends up leading to an over-reliance on digital data and remote data collection and time spent developing tools and looking at data sets on a laptop rather than spending time ‘on the ground’ to observe and engage with local organizations and populations.
  • Little attention to institutional change processes, organizational readiness, and the capacity needed to incorporate new ICT tools, platforms, systems and processes.
  • Bureaucracy levels may mean that decisions happen far from the ground, and there is little capacity to make quick decisions, even if real-time data is available or the data and analysis are provided frequently to decision-makers sitting at a headquarters or to local staff who do not have decision-making power in their own hands and must wait on orders from on high to adapt or change their program approaches and methods.
  • Swinging too far towards digital due to a lack of awareness that digital most often needs to be combined with human. Digital technology always works better when combined with human interventions (such as visits to prepare folks for using the technology, making sure that gatekeepers; e.g., a husband or mother-in-law is on-board in the case of women). A main message from the World Bank 2016 World Development Report “Digital Dividends” is that digital technology must always be combined with what the Bank calls “analog” (a.k.a. “human”) approaches.

B) Methodological

Some of the areas that Michael and I have been looking at relate to how the introduction of ICTs could address issues of bias, rigor, and validity — yet how, at the same time, ICT-heavy methods may actually just change the nature of those issues or create new issues, as noted below:

  • Selection and sample bias – you may be reaching more people, but you’re still going to be leaving some people out. Who is left out of mobile phone or ICT access/use? Typical respondents are male, educated, urban. How representative are these respondents of all ICT users and of the total target population?
  • Data quality and rigor – you may have an over-reliance on self-reporting via mobile surveys; lack of quality control ‘on the ground’ because it’s all being done remotely; enumerators may game the system if there is no personal supervision; there may be errors and bias in algorithms and logic in big data sets or analysis because of non-representative data or hidden assumptions.
  • Validity challenges – if there is a push to use a specific ICT-enabled evaluation method or tool without it being the right one, the design of the evaluation may not pass the validity challenge.
  • Fallacy of large numbers (in cases of national level self-reporting/surveying) — you may think that because a lot of people said something that it’s more valid, but you might just be reinforcing the viewpoints of a particular group. This has been shown clearly in research by the World Bank on public participation processes that use ICTs.
  • ICTs often favor extractive processes that do not involve local people and local organizations or provide benefit to participants/local agencies — data is gathered and sent ‘up the chain’ rather than shared or analyzed in a participatory way with local people or organizations. Not only is this disempowering, it may impact on data quality if people don’t see any point in providing it as it is not seen to be of any benefit.
  • There’s often a failure to identify unintended consequences or biases arising from use of ICTs in evaluation — What happens when you introduce tablets for data collection? What happens when you collect GPS information on your beneficiaries? What risks might you be introducing or how might people react to you when you are carrying around some kind of device?

C) Ethical and Legal

This is an area that I’m very interested in — especially as some donors have started asking for the raw data sets from any research, studies or evaluations that they are funding, and when these kinds of data sets are ‘opened’ there are all sorts of ramifications. There is quite a lot of heated discussion happening here. I was happy to see that DFID has just conducted a review of ethics in evaluationSome of the core issues include:

  • Changing nature of privacy risks – issues here include privacy and protection of data; changing informed consent needs for digital data/open data; new risks of data leaks; and lack of institutional policies with regard to digital data.
  • Data rights and ownership: Here there are some issues with proprietary data sets, data ownership when there are public-private partnerships, the idea of data philanthropy’ when it’s not clear whose data is being donated, personal data ‘for the public good’, open data/open evaluation/ transparency, poor care taken when vulnerable people provide personally identifiable information; household data sets ending up in the hands of those who might abuse them, the increasing impossibility of data anonymization given that crossing data sets often means that re-identification is easier than imagined.
  • Moving decisions and interpretation of data away from ‘the ground’ and upwards to the head office/the donor.
  • Little funding for trialing/testing the validity of new approaches that use ICTs and documenting what is working/not working/where/why/how to develop good practice for new ICTs in evaluation approaches.

Recommendations: 12 tips for better use of ICTs in M&E

Despite the rapid changes in the field in the 2 years since we first wrote our initial paper on ICTs in M&E, most of our tips for doing it better still hold true.

  1. Start with a high-quality M&E plan (not with the tech).
    • But also learn about the new tech-related possibilities that are out there so that you’re not missing out on something useful!
  2. Ensure design validity.
  3. Determine whether and how new ICTs can add value to your M&E plan.
    • It can be useful to bring in a trusted tech expert in this early phase so that you can find out if what you’re thinking is possible and affordable – but don’t let them talk you into something that’s not right for the evaluation purpose and design.
  4. Select or assemble the right combination of ICT and M&E tools.
    • You may find one off the shelf, or you may need to adapt or build one. This is a really tough decision, which can take a very long time if you’re not careful!
  5. Adapt and test the process with different audiences and stakeholders.
  6. Be aware of different levels of access and inclusion.
  7. Understand motivation to participate, incentivize in careful ways.
    • This includes motivation for both program participants and for organizations where a new tech-enabled tool/process might be resisted.
  8. Review/ensure privacy and protection measures, risk analysis.
  9. Try to identify unintended consequences of using ICTs in the evaluation.
  10. Build in ways for the ICT-enabled evaluation process to strengthen local capacity.
  11. Measure what matters – not what a cool ICT tool allows you to measure.
  12. Use and share the evaluation learnings effectively, including through social media.

 

 

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I used to write blog posts two or three times a week, but things have been a little quiet here for the past couple of years. That’s partly because I’ve been ‘doing actual work’ (as we like to say) trying to implement the theoretical ‘good practices’ that I like soapboxing about. I’ve also been doing some writing in other places and in ways that I hope might be more rigorously critiqued and thus have a wider influence than just putting them up on a blog.

One of those bits of work that’s recently been released publicly is a first version of a monitoring and evaluation framework for SIMLab. We started discussing this at the first M&E Tech conference in 2014. Laura Walker McDonald (SIMLab CEO) outlines why in a blog post.

Evaluating the use of ICTs—which are used for a variety of projects, from legal services, coordinating responses to infectious diseases, media reporting in repressive environments, and transferring money among the unbanked or voting—can hardly be reduced to a check-list. At SIMLab, our past nine years with FrontlineSMS has taught us that isolating and understanding the impact of technology on an intervention, in any sector, is complicated. ICTs change organizational processes and interpersonal relations. They can put vulnerable populations at risk, even while improving the efficiency of services delivered to others. ICTs break. Innovations fail to take hold, or prove to be unsustainable.

For these and many other reasons, it’s critical that we know which tools do and don’t work, and why. As M4D edges into another decade, we need to know what to invest in, which approaches to pursue and improve, and which approaches should be consigned to history. Even for widely-used platforms, adoption doesn’t automatically mean evidence of impact….

FrontlineSMS is a case in point: although the software has clocked up 200,000 downloads in 199 territories since October 2005, there are few truly robust studies of the way that the platform has impacted the project or organization it was implemented in. Evaluations rely on anecdotal data, or focus on the impact of the intervention, without isolating how the technology has affected it. Many do not consider whether the rollout of the software was well-designed, training effectively delivered, or the project sustainably planned.

As an organization that provides technology strategy and support to other organizations — both large and small — it is important for SIMLab to better understand the quality of that support and how it may translate into improvements as well as how introduction or improvement of information and communication technology contributes to impact at the broader scale.

This is a difficult proposition, given that isolating a single factor like technology is extremely tough, if not impossible. The Framework thus aims to get at the breadth of considerations that go into successful tech-enabled project design and implementation. It does not aim to attribute impact to a particular technology, but to better understand that technology’s contribution to the wider impact at various levels. We know this is incredibly complex, but thought it was worth a try.

As Laura notes in another blogpost,

One of our toughest challenges while writing the thing was to try to recognize the breadth of success factors that we see as contributing to success in a tech-enabled social change project, without accidentally trying to write a design manual for these types of projects. So we reoriented ourselves, and decided instead to put forward strong, values-based statements.* For this, we wanted to build on an existing frame that already had strong recognition among evaluators – the OECD-DAC criteria for the evaluation of development assistance. There was some precedent for this, as ALNAP adapted them in 2008 to make them better suited to humanitarian aid. We wanted our offering to simply extend and consider the criteria for technology-enabled social change projects.

Here are the adapted criteria that you can read more about in the Framework. They were designed for internal use, but we hope they might be useful to evaluators of technology-enabled programming, commissioners of evaluations of these programs, and those who want to do in-house examination of their own technology-enabled efforts. We welcome your thoughts and feedback — The Framework is published in draft format in the hope that others working on similar challenges can help make it better, and so that they could pick up and use any and all of it that would be helpful to them. The document includes practical guidance on developing an M&E plan, a typical project cycle, and some methodologies that might be useful, as well as sample log frames and evaluator terms of reference.

Happy reading and we really look forward to any feedback and suggestions!!

*****

The Criteria

Criterion 1: Relevance

The extent to which the technology choice is appropriately suited to the priorities, capacities and context of the target group or organization.

Consider: are the activities and outputs of the project consistent with the goal and objectives? Was there a good context analysis and needs assessment, or another way for needs to inform design – particularly through participation by end users? Did the implementer have the capacity, knowledge and experience to implement the project? Was the right technology tool and channel selected for the context and the users? Was content localized appropriately?

Criterion 2: Effectiveness

A measure of the extent to which an information and communication channel, technology tool, technology platform, or a combination of these attains its objectives.

Consider: In a technology-enabled effort, there may be one tool or platform, or a set of tools and platforms may be designed to work together as a suite. Additionally, the selection of a particular communication channel (SMS, voice, etc) matters in terms of cost and effectiveness. Was the project monitored and early snags and breakdowns identified and fixed, was there good user support? Did the tool and/or the channel meet the needs of the overall project? Note that this criterion should be examined at outcome level, not output level, and should examine how the objectives were formulated, by whom (did primary stakeholders participate?) and why.

Criterion 3: Efficiency

Efficiency measures the outputs – qualitative and quantitative – in relation to the inputs. It is an economic term which signifies that the project or program uses the least costly technology approach (including both the tech itself, and what it takes to sustain and use it) possible in order to achieve the desired results. This generally requires comparing alternative approaches (technological or non-technological) to achieving the same outputs, to see whether the most efficient tools and processes have been adopted. SIMLab looks at the interplay of efficiency and effectiveness, and to what degree a new tool or platform can support a reduction in cost, time, along with an increase in quality of data and/or services and reach/scale.

Consider: Was the technology tool rollout carried out as planned and on time? If not, what were the deviations from the plan, and how were they handled? If a new channel or tool replaced an existing one, how do the communication, digitization, transportation and processing costs of the new system compare to the previous one? Would it have been cheaper to build features into an existing tool rather than create a whole new tool? To what extent were aspects such as cost of data, ease of working with mobile providers, total cost of ownership and upgrading of the tool or platform considered?

Criterion 4: Impact

Impact relates to consequences of achieving or not achieving the outcomes. Impacts may take months or years to become apparent, and often cannot be established in an end-of-project evaluation. Identifying, documenting and/or proving attribution (as opposed to contribution) may be an issue here. ALNAP’s complex emergencies evaluation criteria include ‘coverage’ as well as impact; ‘the need to reach major population groups wherever they are.’ They note: ‘in determining why certain groups were covered or not, a central question is: ‘What were the main reasons that the intervention provided or failed to provide major population groups with assistance and protection, proportionate to their need?’ This is very relevant for us.

For SIMLab, a lack of coverage in an inclusive technology project means not only failing to reach some groups, but also widening the gap between those who do and do not have access to the systems and services leveraging technology. We believe that this has the potential to actively cause harm. Evaluation of inclusive tech has dual priorities: evaluating the role and contribution of technology, but also evaluating the inclusive function or contribution of the technology. A platform might perform well, have high usage rates, and save costs for an institution while not actually increasing inclusion. Evaluating both impact and coverage requires an assessment of risk, both to targeted populations and to others, as well as attention to unintended consequences of the introduction of a technology component.

Consider: To what extent does the choice of communications channels or tools enable wider and/or higher quality participation of stakeholders? Which stakeholders? Does it exclude certain groups, such as women, people with disabilities, or people with low incomes? If so, was this exclusion mitigated with other approaches, such as face-to-face communication or special focus groups? How has the project evaluated and mitigated risks, for example to women, LGBTQI people, or other vulnerable populations, relating to the use and management of their data? To what extent were ethical and responsible data protocols incorporated into the platform or tool design? Did all stakeholders understand and consent to the use of their data, where relevant? Were security and privacy protocols put into place during program design and implementation/rollout? How were protocols specifically integrated to ensure protection for more vulnerable populations or groups? What risk-mitigation steps were taken in case of any security holes found or suspected? Were there any breaches? How were they addressed?

Criterion 5: Sustainability

Sustainability is concerned with measuring whether the benefits of a technology tool or platform are likely to continue after donor funding has been withdrawn. Projects need to be environmentally as well as financially sustainable. For SIMLab, sustainability includes both the ongoing benefits of the initiatives and the literal ongoing functioning of the digital tool or platform.

Consider: If the project required financial or time contributions from stakeholders, are they sustainable, and for how long? How likely is it that the business plan will enable the tool or platform to continue functioning, including background architecture work, essential updates, and user support? If the tool is open source, is there sufficient capacity to continue to maintain changes and updates to it? If it is proprietary, has the project implementer considered how to cover ongoing maintenance and support costs? If the project is designed to scale vertically (e.g., a centralized model of tool or platform management that rolls out in several countries) or be replicated horizontally (e.g., a model where a tool or platform can be adopted and managed locally in a number of places), has the concept shown this to be realistic?

Criterion 6: Coherence

The OECD-DAC does not have a 6th Criterion. However we’ve riffed on the ALNAP additional criterion of Coherence, which is related to the broader policy context (development, market, communication networks, data standards and interoperability mandates, national and international law) within which a technology was developed and implemented. We propose that evaluations of inclusive technology projects aim to critically assess the extent to which the technologies fit within the broader market, both local, national and international. This includes compliance with national and international regulation and law.

Consider: Has the project considered interoperability of platforms (for example, ensured that APIs are available) and standard data formats (so that data export is possible) to support sustainability and use of the tool in an ecosystem of other products? Is the project team confident that the project is in compliance with existing legal and regulatory frameworks? Is it working in harmony or against the wider context of other actions in the area? Eg., in an emergency situation, is it linking its information system in with those that can feasibly provide support? Is it creating demand that cannot feasibly be met? Working with or against government or wider development policy shifts?

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Our March 18th Technology Salon NYC covered the Internet of Things and Global Development with three experienced discussants: John Garrity, Global Technology Policy Advisor at CISCO and co-author of Harnessing the Internet of Things for Global Development; Sylvia Cadena, Community Partnerships Specialist, Asia Pacific Network Information Centre (APNIC) and the Asia Information Society Innovation Fund (ISIF); and Andy McWilliams, Creative Technologist at ThoughtWorks and founder and director of Art-A-Hack and Hardware Hack Lab.

By Wilgengebroed on Flickr [CC BY 2.0 (http://creativecommons.org/licenses/by/2.0)%5D, via Wikimedia Commons

What is the Internet of Things?

One key task at the Salon was clarifying what exactly is the “Internet of Things.” According to Wikipedia:

The Internet of Things (IoT) is the network of physical objects—devices, vehicles, buildings and other items—embedded with electronics, software, sensors, and network connectivity that enables these objects to collect and exchange data.[1] The IoT allows objects to be sensed and controlled remotely across existing network infrastructure,[2] creating opportunities for more direct integration of the physical world into computer-based systems, and resulting in improved efficiency, accuracy and economic benefit;[3][4][5][6][7][8] when IoT is augmented with sensors and actuators, the technology becomes an instance of the more general class of cyber-physical systems, which also encompasses technologies such as smart grids, smart homes, intelligent transportation and smart cities. Each thing is uniquely identifiable through its embedded computing system but is able to interoperate within the existing Internet infrastructure. Experts estimate that the IoT will consist of almost 50 billion objects by 2020.[9]

As one discussant explained, the IoT involves three categories of entities: sensors, actuators and computing devices. Sensors read data in from the world for computing devices to process via a decision logic which then generates some type of action back out to the world (motors that turn doors, control systems that operate water pumps, actions happening through a touch screen, etc.). Sensors can be anything from video cameras to thermometers or humidity sensors. They can be consumer items (like a garage door opener or a wearable device) or industrial grade (like those that keep giant machinery running in an oil field). Sensors are common in mobile phones, but more and more we see them being de-coupled from cell phones and integrated into or attached to all manner of other every day things. The boom in the IoT means that in whereas in the past, a person may have had one URL for their desktop computer, now they might be occupying several URLs:  through their phone, their iPad, their laptop, their Fitbit and a number of other ‘things.’

Why does IoT matter for Global Development?

Price points for sensors are going down very quickly and wireless networks are steadily expanding — not just wifi but macro cellular technologies. According to one lead discussant, 95% of the world is covered by 2G and two-thirds by 3G networks. Alongside that is a plethora of technology that is wide range and low tech. This means that all kinds of data, all over the world, are going to be available in massive quantities through the IoT. Some are excited about this because of how data can be used to track global development indicators, for example, the type of data being sought to measure the Sustainable Development Goals (SDGs). Others are concerned about the impact of data collected via the IoT on privacy.

What are some examples of the IoT in Global Development?

Discussants and others gave many examples of how the IoT is making its way into development initiatives, including:

  • Flow meters and water sensors to track whether hand pumps are working
  • Protecting the vaccine cold chain – with a 2G thermometer, an individual can monitor the cold chain for local use and the information also goes directly to health ministries and to donors
  • Monitoring the environment and tracking animals or endangered species
  • Monitoring traffic routes to manage traffic systems
  • Managing micro-irrigation of small shareholder plots from a distance through a feature phone
  • As a complement to traditional monitoring and evaluation (M&E) — a sensor on a cook stove can track how often a stove is actually used (versus information an individual might provide using recall), helping to corroborate and reduce bias
  • Verifying whether a teacher is teaching or has shown up to school using a video camera

The CISCO publication on the IoT and Global Development provides many more examples and an overview of where the area is now and where it’s heading.

How advanced is the IoT in the development space?

Currently, IoT in global development is very much a hacker space, according to one discussant. There are very few off the shelf solutions that development or humanitarian organizations can purchase and readily implement. Some social enterprises are ramping up activity, but there is no larger ecosystem of opportunities for off the shelf products.

Because the IoT in global development is at an early phase, challenges abound. Technical issues, power requirements, reliability and upkeep of sensors (which need to be calibrated), IP issues, security and privacy, technical capacity, and policy questions all need to be worked out. One discussant noted that these challenges carry on from the mobile for development (m4d) and information and communication technologies for development (ICT4D) work of the past.

Participants agreed that challenges are currently huge. For example, devices are homogeneous, making them very easy to hack and affect a lot of devices at once. No one has completely gotten their head around the privacy and consent issues, which are are very different than those of using FB. There are lots of interoperability issues also. As one person highlighted — there are over 100 different communication protocols being used today. It is more complicated than the old “BetaMax v VHS” question – we have no idea at this point what the standard will be for IoT.

For those who see the IoT as a follow-on from ICT4D and m4d, the big question is how to make sure we are applying what we’ve learned and avoiding the same mistakes and pitfalls. “We need to be sure we’re not committing the error of just seeing the next big thing, the next shiny device, and forgetting what we already know,” said one discussant. There is plenty of material and documentation on how to avoid repeating past mistakes, he noted. “Read ICT works. Avoid pilotitis. Don’t be tech-led. Use open source and so on…. Look at the digital principles and apply them to the IoT.”

A higher level question, as one person commented, is around the “inconvenient truth” that although ICTs drive economic growth at the macro level, they also drive income inequality. No one knows how the IoT will contribute or create harm on that front.

Are there any existing standards for the IoT? Should there be?

Because there is so much going on with the IoT – new interventions, different sectors, all kinds of devices, a huge variety in levels of use, from hacker spaces up to industrial applications — there are a huge range of standards and protocols out there, said one discussant. “We don’t really want to see governments picking winners or saying ‘we’re going to us this or that.’ We want to see the market play out and the better protocols to bubble up to the surface. What’s working best where? What’s cost effective? What open protocols might be most useful?”

Another discussant pointed out that there is a legacy predating the IOT: machine-to-machine (M2M), which has not always been Internet based. “Since this legacy is still there. How can we move things forward with regard to standardization and interoperability yet also avoid leaving out those who are using M2M?”

What’s up with IPv4 and IPv6 and the IoT? (And why haven’t I heard about this?)

Another crucial technical point raised is that of IPv4 and IPv6, something that not many Salon participant had heard of, but that will greatly impact on how the IoT rolls out and expands, and just who will be left out of this new digital divide. (Note: I found this video to be helpful for explaining IPv4 vs IPv6.)

“Remember when we used Netscape and we understood how an IP number translated into an IP address…?” asked one discussant. “Many people never get that lovely experience these days, but it’s important! There is a finite number of IP4 addresses and they are running out. Only Africa and Latin America have addresses left,” she noted.

IPv6 has been around for 20 years but there has not been a serious effort to switch over. Yet in order to connect the next billion and the multiple devices that they may bring online, we need more addresses. “Your laptop, your mobile, your coffee pot, your fridge, your TV – for many of us these are all now connected devices. One person might be using 10 IP addresses. Multiply that by millions of people, and the only thing that makes sense is switching over to IPv6,” she said.

There is a problem with the technical skills and the political decisions needed to make that transition happen. For much of the world, the IoT will not happen very smoothly and entire regions may be left out of the IoT revolution if high level decision makers don’t decide to move ahead with IPv6.

What are some of the other challenges with global roll-out of IoT?

In addition to the IPv4 – IPv6 transition, there are all kinds of other challenges with the IoT, noted one discussant. The technical skills required to make the transition that would enable IoT in some regions, for example Asia Pacific, are sorely needed. Engineers will need to understand how to make this shift happen, and in some places that is going to be a big challenge. “Things have always been connected to the Internet. There are just going to be lots more, different things connected to the Internet now.”

One major challenge is that there are huge ethical questions along with security and connectivity holes (as I will outline later in this summary post, and as discussed in last year’s salon on Wearable Technologies). In addition, noted one discussant, if we are designing networks that are going to collect data for diseases, for vaccines, for all kinds of normal businesses, and put the data in the cloud, developing countries need to have the ability to secure the data, the computing capacity to deal with it, and the skills to do their own data analysis.

“By pushing the IoT onto countries and not supporting the capacity to manage it, instead of helping with development, you are again creating a giant gap. There will be all kinds of data collected on climate change in the Pacific Island Countries, for example, but the countries don’t have capacity to deal with this data. So once more it will be a bunch of outsiders coming in to tell the Pacific Islands how to manage it, all based on conclusions that outsiders are making based on sensor data with no context,” alerted one discussant. “Instead, we should be counseling our people, our countries to figure out what they want to do with these sensors and with this data and asking them what they need to strengthen their own capacities.”

“This is not for the SDGs and ticking off boxes,” she noted. “We need to get people on the ground involved. We need to decentralize this so that people can make their own decisions and manage their own knowledge. This is where the real empowerment is – where local people and country leaders know how to collect data and use it to make their own decisions. The thing here is ownership — deploying your own infrastructure and knowing what to do with it.”

How can we balance the shiny devices with the necessary capacities?

Although the critical need to invest in and support country-level capacity to manage the IoT has been raised, this type of back-end work is always much less ‘sexy’ and less interesting for donors than measuring some development programming with a flashy sensor. “No one wants to fund this capacity strengthening,” said one discussant. “Everyone just wants to fund the shiny sensors. This chase after innovation is really damaging the impact that technology can actually have. No one just lets things sit and develop — to rest and brew — instead we see everyone rushing onto the next big thing. This is not a good thing for a small country that doesn’t have the capacity to jump right into it.”

All kinds of things can go wrong if people are not trained on how to manage the IoT. Devices can be hacked and they may be collecting and sharing data without an individuals’ knowledge (see Geoff Huston on The Internet of Stupid Things). Electrical short outs, common in places with poor electricity ecosystems, can also cause big problems. In addition, the Internet is affected by legacy systems – so we need interoperability that goes backwards, said one discussant. “If we don’t make at least a small effort to respect those legacy systems, we’re basically saying ‘if you don’t have the funding to update your system, you’re out.’ This then reinforces a power dynamic where countries need the international community to give them equipment, or they need to buy this or buy that, and to bring in international experts from the outside….’ The pressure on poor countries to make things work, to do new kinds of M&E, to provide evidence is huge. With that pressure comes a higher risk of falling behind very quickly. We are also seeing pilot projects that were working just fine without fancy tech being replaced by new fangled tech-type programs instead of being supported over the longer term,” she said.

Others agreed that the development sector’s fascination with shiny and new is detrimental. “There is very little concern for the long-term, the legacy system, future upgrades,” said one participant. “Once the blog post goes up about the cool project, the sensors go bad or stop working and no one even knows because people have moved on.” Another agreed, citing that when visiting numerous clinics for a health monitoring program in one country, the running joke among the M&E staff was “OK, now let’s go and find the broken solar panel.” “When I think of the IoT,” she said, “I think of a lot of broken devices in 5 years.” The aspect of eWaste and the IoT has not even begun to be examined or quantified, noted another.

It is increasingly important for governments to understand how the Internet works, because they are making policy about it. Manufacturers need to better understand how the tech works on the ground, especially in different contexts that they are not accustomed to working in. Users need a better understanding of all of this because their privacy is at risk. Legal frameworks around data and national laws need more attention as well. “When you are working with restrictive governments, your organization’s or start-up’s idea might actually be illegal or close to a sedition law and you may end up in jail,” noted one discussant.

What choices will organizations need to make regarding the IoT?

When it comes to actually making decisions on how involved an organization should and can be in supporting or using the IoT, one critical choice will be related the suites of devices, said our third discussant. Will it be a cloud device? A local computing device? A computer?

Organizations will need to decide if they want a vendor that gives them a package, or if they want a modular, interoperable approach of units. They will need to think about aspects like whether they want to go with proprietary or open source and will it be plug and play?

There are trade-offs here and key technical infrastructure choices will need to be made based on a certain level of expertise and experience. If organizations are not sure what they need, they may wish to get some advice before setting up a system or investing heavily.

As one discussant put it, “When I talk about the IOT, I often say to think about what the Internet was in the 90s. Think about that hazy idea we had of what the Internet was going to be. We couldn’t have predicted in the 90s what today’s internet would look like, and we’re in the same place with the IoT,” he said. “There will be seismic change. The state of the whole sector is immature now. There are very hard choices to make.”

Another aspect that’s representative of the IoT’s early stage, he noted, is that the discussion is all focusing on http and the Internet. “The IOT doesn’t necessarily even have to involve the Internet,” he said.

Most vendors are offering a solution with sensors to deploy, actuators to control and a cloud service where you log in to find your data. The default model is that the decision logic takes place there in the cloud, where data is stored. In this model, the cloud is in the middle, and the devices are around it, he said, but the model does not have to be that way.

Other models can offer more privacy to users, he said. “When you think of privacy and security – the healthcare maxim is ‘do no harm.’ However this current, familiar model for the IoT might actually be malicious.” The reason that the central node in the commercial model is the cloud is because companies can get more and more detailed information on what people are doing. IoT vendors and IoT companies are interested in extending their profiles of people. Data on what people do in their virtual life can now be combined with what they do in their private lives, and this has huge commercial value.

One option to look at, he shared, is a model that has a local connectivity component. This can be something like bluetooth mesh, for example. In this way, the connectivity doesn’t have to go to the cloud or the Internet at all. This kind of set-up may make more sense with local data, and it can also help with local ownership, he said. Everything that happens in the cloud in the commercial model can actually happen on a local hub or device that opens just for the community of users. In this case, you don’t have to share the data with the world. Although this type of a model requires greater local tech capacity and can have the drawback that it is more difficult to push out software updates, it’s an option that may help to enhance local ownership and privacy.

This requires a ‘person first’ concept of design. “When you are designing IOT systems, he said, “start with the value you are trying to create for individuals or organizations on the ground. And then implement the local part that you need to give local value. Then, only if needed, do you add on additional layers of the onion of connectivity, depending on the project.” The first priority here are the goals that the technology design will achieve for individual value, for an individual client or community, not for commercial use of people’s data.

Another point that this discussant highlighted was the need to conduct threat modeling and to think about unintended consequences. “If someone hacked this data – what could go wrong?” He suggested working backwards and thinking: “What should I take offline? How do I protect it better? How do I anonymize it better.”

In conclusion….

It’s critical to understand the purpose of an IoT project or initiative, discussants agreed, to understand if and why scale is needed, and to be clear about the drivers of a project. In some cases, the cloud is desirable for quicker, easier set up and updates to software. At the same time, if an initiative is going to be sustainable, then community and/or country capacity to run it, sustain it, keep it protected and private, and benefit from it needs to be built in. A big part of that capacity includes the ability to understand the different layers that surround the IoT and to make grounded decisions on the various trade-offs that will come to a head in the process of design and implementation. These skills and capacities need to be developed and supported within communities, countries and organizations if the IoT is to contribute ethically and robustly to global development.

Thanks to APNIC for sponsoring and supporting this Salon and to our friends at ThoughtWorks for hosting! If you’d like to join discussions like this one in cities around the world, 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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At our November 18th Technology Salon, we discussed how different organizations are developing their ICT for development (ICT4D) strategies. We shared learning on strategy development and buy-in, talked about whether organizations should create special teams or labs for ICT- and innovation-related work or mainstream the ICT4D function, and thought about how organizations can define and find the skill sets needed for taking their ICT-enabled work forward. Population Council’s Stan Mierzwa, Oxfam America’s Neal McCarthy, and Cycle Technologies’ Leslie Heyer joined as lead discussants, and we heard from Salon participants about their experiences too.

Participating organizations were at various stages of ICT4D work, but most had experienced similar challenges and frustrations with taking their work forward. Even organizations that had created ICT4D strategies a couple of years ago said that implementation was slow.

Some of the key elements mentioned by our first discussant as important for managing and strategically moving ICT forward in an organization included:

  • being more informed about where different offices and staff were using ICTs for programmatic work,
  • establishing a standard set of technology tools for organizational use,
  • improved knowledge management about ICTs,
  • publishing on how ICTs were being used in programs (to help with credibility),
  • engaging with different teams and leadership to secure support and resources
  • working more closely with human resources teams who often do not understand ICT4D-related job descriptions and the profile needed.

Our second discussant said that his organization developed an ICT4D strategy in order to secure resources and greater support for moving ICT4D forward. It was also starting to be unwieldy to manage all of the different ideas and tools being used across the organization, and it seemed that greater harmonization would allow for improved IT support for more established tools as well as establishment of other ways to support new innovations.

In this case, the organization looked at ICTs as two categories: technology for development workers and technology for development outcomes. They used Gartner’s ‘pace layered’ model (which characterizes systems of innovation, systems of differentiation, and systems of record) as a way of analyzing the support roles of different departments.

One of the initial actions taken by this organization was establishing a small tech incubation fund that different offices could apply for in order to try something new with ICTs in their programs and campaigns. Another action was to take 10 staff to the Catholic Relief Services (CRS) ICT4D conference to learn more about ICT4D and to see what their peers from similar organizations were doing. In return for attending the conference, staff were required to submit a proposal for the tech incubation fund.

For the development of the strategy document and action plan, the ICT4D strategy team worked with a wider group of staff to develop a list of current ICT-enabled initiatives and a visual heat map of actions and activities across the organization. This formed the basis for discussions on where lots of ICT4D activities were happening and where there was nothing going on with ICTs. The team then discussed what the organization should do strategically to support and potentially consolidate existing activities and what should be done about areas where there were few ICT-related activities – should those areas be left alone or was there a reason to look at them to see if ICT should be incorporated?

Having done that, the organization adapted Nethope’s Organizational Guide to ICT4D to fit its own structure and culture, and used it as a framework for ICT4D strategy discussions with key staff from different teams. The Nethope guide suggests five key functions for strategic, organization-wide ICT4D: lead organizational change, drive knowledge exchange, build a portfolio, manage processes, and develop an advisory service (see below). The aforementioned activities were also clustered according to which of these 5 areas they fell into.

Screen Shot 2015-11-24 at 8.53.12 AM

(Table of contents from Nethope’s Guide.)

The organization felt it was also important to change the image of the IT team. ‘We had to show that we were not going to tie people up with formal committees and approvals if they wanted to try something new and innovative. Being more approachable is necessary or staff will bypass the IT team and go to consultants, and then we open ourselves up to data privacy risks and we also lose institutional knowledge.’

Salon participants agreed that it was important to know how to “sell” an ICT4D-related idea to frontline staff, management and leadership. Some ways to do this include demonstrating the value-add of ICTs in terms of longer-term cost and time efficiencies, showing the benefit of real-time data for decision-making, and demonstrating what peer organizations are doing. Organizations often also need someone at the top who is pushing for change and modernization.

Our third discussant said that her company has been shifting from a commercial product developer to a full-fledged technology company. She outlined the need for strategic thinking along that journey. Initially, the company outsourced activities such as research and data collection. With time, it started to pull key functions in house since systems maintenance and technology has become a core part of the business.

“As a small company, we can be flexible and change easily,” she said. ‘ICT is embedded into our culture and everyone thinks about it.’ One challenge that many ICT4D initiatives face – whether they are happening in a non-profit or a for-profit — is sustainability. ‘People are often fine with paying for a physical product, but when it comes to the web, they are accustomed to getting everything for free, which makes long-term sustainability difficult.’

In order to continuously evolve their strategies, organizations need to have time and space to constantly step back and think about their underlying values and where they see themselves in 5 or 10 years. A more pro-active relationship with donors is also important. Although Salon participants felt that the ICT4D Principles and related processes were promising, they also felt that donors do not have a clear idea of what they are looking for, what exists already, what needs to be created, and what evidence base exists for different tools or kinds of ICT4D. One Salon participant felt that ‘donor agencies don’t know what kinds of tech are effective, so it’s up to you as an implementer to bring the evidence to the table. It’s critical to have the ITC4D support staff at the table with you, because if not these more detailed conversations about the tech don’t happen with donors and you’ll find all kinds of duplication of efforts.’

Another challenge with thinking about ICT4D in a strategic way is that donors normally don’t want to fund capacity building, said another Salon participant. They prefer to fund concrete projects or innovation challenges rather than supporting organizations to create an environment that gives rise to innovation. In addition, funding beyond the program cycle is a big challenge. ‘We need to be thinking about enterprise systems, layered on systems, national systems,’ said one person. ‘And systems really struggle here to scale and grow if you can’t claim ownership for the whole.’

Salon participants highlighted hiring and human resources departments as a big barrier when it comes to ICT4D. It is often not clear what kinds of skills are needed to implement ICT4D programs, and human resources teams often screen for the wrong skill sets because they do not understand the nature of ICT4D. ‘I always make them give me all the CVs and screen them myself,’ said one person. ‘If not, some of the best people will not make it to the short list.’ Engaging with human resources and sharing the ICT4D strategy is one way to help with better hiring and matching of job needs with skill sets that are out there and potentially difficult to find.

In conclusion, whether the ICT4D strategy is to mainstream, to isolate and create a ‘lab,’ or to combine approaches, it seems that most organizations are struggling a bit to develop and/or implement ICT4D strategies due to the multiple pain points of slow organizational change and the need for more capacity and resources. Some are making headway, however, and developing clearer thinking and action plans that are paying off in the short term, and that may set the organizations up for eventual ICT4D success.

Thanks to Population Council 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. No attribution has been made in this post.

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Traditional development evaluation has been characterized as ‘backward looking’ rather than forward looking and too focused on proving over improving. Some believe applying an ‘agile’ approach in development would be more useful — the assumption being that if you design a program properly and iterate rapidly and constantly based on user feedback and data analytics, you are more likely achieve your goal or outcome without requiring expensive evaluations. The idea is that big data could eventually allow development agencies to collect enough passive data about program participants that there would no longer be a need to actively survey people or conduct a final evaluation, because there would be obvious patterns that would allow implementers to understand behaviors and improve programs along the way.

The above factors have made some evaluators and data scientists question whether big data and real-time availability of multiple big data sets, along with the technology that enables their collection and analysis, will make evaluation as we know it obsolete. Others have argued that it’s not the end of evaluation, but rather we will see a blending of real-time monitoring, predictive modeling, and impact evaluation, depending on the situation. Big questions remain, however, about the feasibility of big data in some contexts. For example, are big data approaches useful when it comes to people who are not producing very much digital data? How will the biases in big data be addressed to ensure that the poorest, least connected, and/or most marginalized are represented?

The Technology Salon on Big Data and Evaluation hosted during November’s  American Evaluation Association Conference in Chicago opened these questions up for consideration by a roomful of evaluators and a few data scientists. We discussed the potential role of new kinds and quantities of data. We asked how to incorporate static and dynamic big data sources into development evaluation. We shared ideas on what tools, skills, and partnerships we might require if we aim to incorporate big data into evaluation practice. This rich and well-informed conversation was catalyzed by our lead discussants: Andrew Means, Associate Director of the Center for Data Science & Public Policy at the University of Chicago and Founder of Data Analysts for Social Good and The Impact Lab; Michael Bamberger, Independent Evaluator and co-author of Real World Evaluation; and Veronica Olazabal from The Rockefeller Foundation. The Salon was supported by ITAD via a Rockefeller Foundation grant.

What do we mean by ‘big data’?

The first task was to come up with a general working definition of what was understood by ‘big data.’ Very few of the organizations present at the Salon were actually using ‘big data’ and definitions varied. Some talked about ‘big data sets’ as those that could not be collected or analyzed by a human on a standard computer. Others mentioned that big data could include ‘static’ data sets (like government census data – if digitized — or cellphone record data) and ‘dynamic’ data sets that are being constantly generated in real time (such as streaming data input from sensors or ‘cookies’ and ‘crumbs’ generated through use of the Internet and social media). Others considered big data to be real time, socially-created and socially-driven data that could be harvested without having to purposely collect it or budget for its collection. ‘It’s data that has a life of its own. Data that just exists out there.’ Yet others felt that for something to be ‘big data’ multiple big data sets needed to be involved, for example, genetic molecular data crossed with clinical trial data and other large data sets, regardless of static or dynamic nature. Big data, most agreed, is data that doesn’t easily fit on a laptop and that requires a specialized skill set that most social scientists don’t have. ‘What is big data? It’s hard to define exactly, but I know it when I see it,’ concluded one discussant.

Why is big data a ‘thing’?

As one discussant outlined, recent changes in technology have given rise to big data. Data collection, data storage and analytical power are becoming cheaper and cheaper. ‘We live digitally now and we produce data all the time. A UPS truck has anywhere from 50-75 sensors on it to do everything from optimize routes to indicate how often it visits a mechanic,’ he said. ‘The analytic and computational power in my iPhone is greater than what the space shuttle had.’ In addition, we have ‘seamless data collection’ in the case of Internet-enabled products and services, meaning that a person creates data as they access products or services, and this can then be monetized, which is how companies like Google make their money. ‘There is not someone sitting at Google going — OK, Joe just searched for the nearest pizza place, let me enter that data into the system — Joe is creating the data about his search while he is searching, and this data is a constant stream.’

What does big data mean for development evaluation?

Evaluators are normally tasked with making a judgment about the merit of something, usually for accountability, learning and/or to improve service delivery, and usually looking back at what has already happened. In the wider sense, the learning from evaluation contributes to program theory, needs assessment, and many other parts of the program cycle.

This approach differs in some key ways from big data work, because most of the new analytical methods used by data scientists are good at prediction but not very good at understanding causality, which is what social scientists (and evaluators) are most often interested in. ‘We don’t just look at giant data sets and find random correlations,’ however, explained one discussant. ‘That’s not practical at all. Rather, we start with a hypothesis and make a mental model of how different things might be working together. We create regression models and see which performs better. This helps us to know if we are building the right hypothesis. And then we chisel away at that hypothesis.’

Some challenges come up when we think about big data for development evaluation because the social sector lacks the resources of the private sector. In addition, data collection in the world of international development is not often seamless because ‘we care about people who do not live in the digital world,’ as one person put it. Populations we work with often do not leave a digital trail. Moreover, we only have complete data about the entire population in some cases (for example, when it comes to education in the US), meaning that development evaluators need to figure out how to deal with bias and sampling.

Satellite imagery can bring in some data that was unavailable in the past, and this is useful for climate and environmental work, but we still do not have a lot of big data for other types of programming, one person said. What’s more, wholly machine-based learning, and the kind of ‘deep learning’ made possible by today’s computational power is currently not very useful for development evaluation.

Evaluators often develop counterfactuals so that they can determine what would have happened without an intervention. They may use randomized controlled trials (RCTs), differentiation models, statistics and economics research approaches to do this. One area where data science may provide some support is in helping to answer questions about counterfactuals.

More access to big data (and open data) could also mean that development and humanitarian organizations stop duplicating data collection functions. Perhaps most interestingly, big data’s predictive capabilities could in the future be used in the planning phase to inform the kinds of programs that agencies run, where they should be run, and who should be let into them to achieve the greatest impact, said one discussant. Computer scientists and social scientists need to break down language barriers and come together more often so they can better learn from one another and determine where their approaches can overlap and be mutually supportive.

Are we all going to be using big data?

Not everyone needs to use big data. Not everyone has the capacity to use it, and it doesn’t exist for offline populations, so we need to be careful that we are not forcing it where it’s not the best approach. As one discussant emphasized, big data is not magic, and it’s not universally applicable. It’s good for some questions and not others, and it should be considered as another tool in the toolbox rather than the only tool. Big data can provide clues to what needs further examination using other methods, and thus most often it should be part of a mixed methods approach. Some participants felt that the discussion about big data was similar to the one 20 years ago on electronic medical records or to the debate in the evaluation community about quantitative versus qualitative methods.

What about groups of people who are digitally invisible?

There are serious limitations when it comes to the data we have access to in the poorest communities, where there are no tablets and fewer cellphones. We also need to be aware of ‘micro-exclusion’ (who within a community or household is left out of the digital revolution?) and intersectionality (how do different factors of exclusion combine to limit certain people’s digital access?) and consider how these affect the generation and interpretation of big data. There is also a question about the intensity of the digital footprint: How much data and at what frequency is it required for big data to be useful?

Some Salon participants felt that over time, everyone would have a digital presence and/or data trail, but others were skeptical. Some data scientists are experimenting with calibrating small amounts of data and comparing them to human-collected data in an attempt to make big data less biased, a discussant explained. Another person said that by digitizing and validating government data on thousands (in the case of India, millions) of villages, big data sets could be created for those that are not using mobiles or data.

Another person pointed out that generating digital data is a process that involves much more than simple access to technology. ‘Joining the digital discussion’ also requires access to networks, local language content, and all kinds of other precursors, she said. We also need to be very aware that these kinds of data collection processes impact on people’s participation and input into data collection and analysis. ‘There’s a difference between a collective evaluation activity where people are sitting around together discussing things and someone sitting in an office far from the community getting sound bites from a large source of data.’

Where is big data most applicable in evaluation?

One discussant laid out areas where big data would likely be the most applicable to development evaluation:

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It would appear that big data has huge potential in the evaluation of complex programs, he continued. ‘It’s fairly widely accepted that conventional designs don’t work well with multiple causality, multiple actors, multiple contextual variables, etc. People chug on valiantly, but it’s expected that you may get very misleading results. This is an interesting area because there are almost no evaluation designs for complexity, and big data might be a possibility here.’

In what scenarios might we use big data for development evaluation?

This discussant suggested that big data might be considered useful for evaluation in three areas:

  1. Supporting conventional evaluation design by adding new big data generated variables. For example, one could add transaction data from ATMs to conventional survey generated poverty indicators
  2. Increasing the power of a conventional evaluation design by using big data to strengthen the sample selection methodology. For example, satellite images were combined with data collected on the ground and propensity score matching was used to strengthen comparison group selection for an evaluation of the effects of interventions on protecting forest cover in Mexico.
  3. Replacing a conventional design with a big data analytics design by replacing regression based models with systems analysis. For example, one could use systems analysis to compare the effectiveness of 30 ongoing interventions that may reduce stunting in a sample of villages. Real-time observations could generate a time-series that could help to estimate the effectiveness of each intervention in different contexts.

It is important to remember construct validity too. ‘If big data is available, but it’s not quite answering the question that you want to ask, it might be easy to decide to do something with it, to run some correlations, and to think that maybe something will come out. But we should avoid this temptation,’ he cautioned. ‘We need to remember and respect construct validity and focus on measuring what we think we are measuring and what we want to measure, not get distracted by what a data set might offer us.’

What about bias in data sets?

We also need to be very aware that big data carries with it certain biases that need to be accounted for, commented several participants; notably, when working with low connectivity populations and geographies or when using data from social media sites that cater to a particular segment of the population. One discussant shared an example where Twitter was used to identify patterns in food poisoning, and suddenly the upscale, hipster restaurants in the city seemed to be the problem. Obviously these restaurants were not the sole source of the food poisoning, but rather there was a particular kind of person that tended to use Twitter.

‘People are often unclear about what’s magical and what’s really possible when it comes to big data. We want it to tell us impossible things and it can’t. We really need to engage human minds in this process; it’s not a question of everything being automated. We need to use our capacity for critical thinking and ask: Who’s creating the data? How’s it created? Where’s it coming from? Who might be left out? What could go wrong?’ emphasized one discussant. ‘Some of this information can come from the metadata, but that’s not always enough to make certain big data is a reliable source.’ Bias may also be introduced through the viewpoints and unconscious positions, values and frameworks of the data scientists themselves as they are developing algorithms and looking for/finding patterns in data.

What about the ethical and privacy implications?

Big Data has a great deal of ethical and privacy implications. Issues of consent and potential risk are critical considerations, especially when working with populations that are newly online and/or who may not have a good understanding of data privacy and how their data may be used by third parties who are collecting and/or selling it. However, one participant felt that a protectionist mentality is misguided. ‘We are pushing back and saying that social media and data tracking are bad. Instead, we should realize that having a digital life and being counted in the world is a right and it’s going to be inevitable in the future. We should be working with the people we serve to better understand digital privacy and help them to be more savvy digital citizens.’ It’s also imperative that aid and development agencies abandon our slow and antiquated data collection systems, she said, and to use the new digital tools that are available to us.

How can we be more responsible with the data we gather and use?

Development and humanitarian agencies do need be more responsible with data policies and practices, however. Big data approaches may contribute to negative data extraction tendencies if we mine data and deliver it to decision-makers far away from the source. It will be critical for evaluators and big data practitioners to find ways to engage people ‘on the ground’ and involve more communities in interpreting and querying their own big data. (For more on responsible data use, see the Responsible Development Data Book. Oxfam also has a responsible data policy that could serve as a reference. The author of this blog is working on a policy and practice guide for protecting girls digital safety, security and privacy as well.)

Who should be paying for big data sets to be made available?

One participant asked about costs and who should bear the expense of creating big data sets and/or opening them up to evaluators and/or data scientists. Others asked for examples of the private sector providing data to the social sector. This highlighted additional ethical and privacy issues. One participant gave an example from the healthcare space where there is lots of experience in accessing big data sets generated by government and the private sector. In this case, public and private data sets needed to be combined. There were strict requirements around anonymization and the effort ended up being very expensive, which made it difficult to build a business case for the work.

This can be a problem for the development sector, because it is difficult to generate resources for resolving social problems; there is normally only investment if there is some kind of commercial gain to be had. Some organizations are now hiring ‘data philanthropist’ positions that help to negotiate these kinds of data relationships with the private sector. (Global Pulse has developed a set of big data privacy principles to guide these cases.)

So, is big data going to replace evaluation or not?

In conclusion, big data will not eliminate the need for evaluation. Rather, it’s likely that it will be integrated as another source of information for strengthening conventional evaluation design. ‘Big Data and the underlying methods of data science are opening up new opportunities to answer old questions in new ways, and ask new kinds of questions. But that doesn’t mean that we should turn to big data and its methods for everything,’ said one discussant. ‘We need to get past a blind faith in big data and get more practical about what it is, how to use it, and where it adds value to evaluation processes,’ said another.

Thanks again to all who participated in the discussion! If you’d like to join (or read about) conversations like this one, visit Technology Salon. Salons run under Chatham House Rule, so no attribution has been made in this summary post.

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Last month I joined a panel hosted by the Guardian on the contribution of innovation and technology to the Sustainable Development Goals (SDGs). Luckily they said that it was fine to come from a position of ‘skeptical realism.’

To drum up some good skeptical realist thoughts, I did what every innovative person does – posted a question on Facebook. A great discussion among friends who work in development, innovation and technology ensued. (Some might accuse me of ‘crowdsourcing’ ideas for the panel, but I think of it as more of a group discussion enabled by the Internet.) In the end, I didn’t get to say most of what we discussed on Facebook while on the panel, so I’m summarizing here.

To start off, I tend to think that the most interesting thing about the SDGs is that they are not written for ‘those developing countries over there.’ Rather, all countries are supposed to meet them. (I’m still not sure how many people or politicians in the US are aware of this.)

Framing them as global goals forces recognition that we have global issues to deal with — inequality and exclusion happen within countries and among countries everywhere. This opens doors for a shift in the narrative and framing of  ‘development.’ (See Goal 10: Reduce inequality within and among countries; and Goal 16: Promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable and inclusive institutions at all levels.)

These core elements of the SDGs — exclusion and inequality – are two things that we also need to be aware of when we talk about innovation and technology. And while innovation and technology can contribute to development and inclusion…by connecting people and providing more access to information; helping improve access to services; creating space for new voices to speak their minds; contributing in some ways to improved government and international agency accountability; improving income generation; and so on… it’s important to be aware of who is excluded from creating, accessing, using and benefiting from tech and tech-enabled processes and advances.

Who creates and/or controls the tech? Who is pushed off platforms because of abuse or violence? Who is taken advantage of through tech? Who is using tech to control others? Who is seen as ‘innovative’ and who is ignored? For whom are most systems and services designed? Who is an entrepreneur by choice vs. an informal worker by necessity? There are so many questions to ask at both macro and micro levels.

But that’s not the whole of it. Even if all the issues of access and use were resolved, there are still problems with framing innovation and technology as one of the main solutions to the world’s problems. A core weakness of the Millennium Development Goals (MDGs) was that they were heavy on quantifiable goals and weak on reaching the most vulnerable and on improving governance. Many innovation and technology solutions suffer the same problem.

Sometimes we try to solve the wrong problems with tech, or we try to solve the wrong problems altogether, without listening to and involving the people who best understand the nature of those problems, without looking at the structural changes needed for sustainable impact, and without addressing exclusion at the micro-level (within and among districts, communities, neighborhoods or households).

Often a technological solution is brought in for questionable reasons. There is too little analysis of the political economy in development work as DE noted on the discussion thread. Too few people are asking who is pushing for a technology solution. Why technology? Who gains? What is the motivation? As Ory Okollah asked recently, Why are Africans expected to innovate and entrepreneur our way out of our problems? We need to get past our collective fascination with invention of products and move onward to a more holistic understanding of innovation that involves sustainable implementation, change, and improvement over the longer term.

Innovation is a process, not a product. As MBC said on the discussion thread, “Don’t confuse doing it first with doing it best.” Innovation is not an event, a moment, a one-time challenge, a product, a simple solution. Innovation is technology agnostic, noted LS. So we need to get past the goal of creating and distributing more products. We need to think more about innovating and tweaking processes, developing new paradigms and adjusting and improving on ways of doing things that we already know work. Sometimes technology helps, but that is not always the case.

We need more practical innovation. We should be looking at old ideas in a new context (citing from Stephen Johnson’s Where Good Ideas Come From) said AM. “The problem is that we need systems change and no one wants to talk about that or do it because it’s boring and slow.”

The heretical IT dared suggest that there’s too much attention to high profile innovation. “We could do with more continual small innovation and improvements and adaptations with a strong focus on participants/end users. This doesn’t make big headlines but it does help us get to actual results,” he said.

Along with that, IW suggested we need more innovative thinking and listening, and less innovative technology. “This might mean senior aid officials spending a half a day per week engaging with the people they are supposed to be helping.”

One innovative behavior change might be that of overcoming the ‘expert knowledge’ problem said DE. We need to ensure that the intended users or participants in an innovation or a technology or technological approach are involved and supported to frame the problem, and to define and shape the innovation over time. This means we also need to rely on existing knowledge – immediate and documented – on what has worked, how and when and where and why and what hasn’t, and to make the effort to examine how this knowledge might be relevant and useful for the current context and situation. As Robert Chambers said many years ago: the links of modern scientific knowledge with wealth, power, and prestige condition outsiders to despise and ignore rural peoples’ own knowledge. Rural people’s knowledge and modern scientific knowledge are complementary in their strengths and weaknesses.

Several people asked whether the most innovative thing in the current context is simply political will and seeing past an election cycle, a point that Kentaro Toyama often makes. We need renewed focus on political will and capacity and a focus on people rather than generic tech solutions.

In addition, we need paradigm shifts and more work to make the current system inclusive and fit for purpose. Most of our existing institutions and systems, including that of ‘development’ carry all of the old prejudices and ‘isms’. We need more questioning of these systems and more thinking about realistic alternatives – led and designed by people who have been traditionally excluded and pushed out. As a sector, we’ve focused a LOT on technocratic approaches over the past several years, and we’ve stopped being afraid to get technical. Now we need to stop being afraid to get political.

In summary, there is certainly a place for technology and for innovation in the SDGs, but the innovation narrative needs an overhaul. Just as we’ve seen with terms like ‘social good’ and ‘user centered design’ – we’ve collectively imbued these ideas and methods with properties that they don’t actually have and we’ve fetishized them. Re-claiming the term innovation, said HL, and taking it back to a real process with more realistic expectations might do us a lot of good.

 

 

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