Tag Archives: learning

What’s Happening with Tech and MERL?

by Linda Raftree, Independent Consultant and MERL Tech organizer

Back in 2014, the humanitarian and development sectors were in the heyday of excitement over innovation and Information and Communication Technologies for Development (ICT4D). The role of ICTs specifically for monitoring, evaluation, research and learning (aka “MERL Tech“) had not been systematized (as far as I know), and it was unclear whether there actually was “a field.” I had the privilege of writing a discussion paper with Michael Bamberger to explore how and why new technologies were being tested and used in the different steps of a traditional planning, monitoring and evaluation cycle. (See graphic 1 below, from our paper).

The approaches highlighted in 2014 focused on mobile phones, for example: text messages (SMS), mobile data gathering, use of mobiles for photos and recording, mapping with specific handheld global positioning systems (GPS) devices or GPS installed in mobile phones. Promising technologies included tablets, which were only beginning to be used for M&E; “the cloud,” which enabled easier updating of software and applications; remote sensing and satellite imagery, dashboards, and online software that helped evaluators do their work more easily. Social media was also really taking off in 2014. It was seen as a potential way to monitor discussions among program participants, gather feedback from program participants, and considered an underutilized tool for greater dissemination of evaluation results and learning. Real-time data and big data and feedback loops were emerging as ways that program monitoring could be improved, and quicker adaptation could happen.

In our paper, we outlined five main challenges for the use of ICTs for M&E: selectivity bias; technology- or tool-driven M&E processes; over-reliance on digital data and remotely collected data; low institutional capacity and resistance to change; and privacy and protection. We also suggested key areas to consider when integrating ICTs into M&E: quality M&E planning, design validity; value-add (or not) of ICTs; using the right combination of tools; adapting and testing new processes before role-out; technology access and inclusion; motivation to use ICTs, privacy and protection; unintended consequences; local capacity; measuring what matters (not just what the tech allows you to measure); and effectively using and sharing M&E information and learning.

We concluded that:

  • The field of ICTs in M&E is emerging and activity is happening at multiple levels and with a wide range of tools and approaches and actors. 
  • The field needs more documentation on the utility and impact of ICTs for M&E. 
  • Pressure to show impact may open up space for testing new M&E approaches. 
  • A number of pitfalls need to be avoided when designing an evaluation plan that involves ICTs. 
  • Investment in the development, application and evaluation of new M&E methods could help evaluators and organizations adapt their approaches throughout the entire program cycle, making them more flexible and adjusted to the complex environments in which development initiatives and M&E take place.

Where are we now:  MERL Tech in 2019

Much has happened globally over the past five years in the wider field of technology, communications, infrastructure, and society, and these changes have influenced the MERL Tech space. Our 2014 focus on basic mobile phones, SMS, mobile surveys, mapping, and crowdsourcing might now appear quaint, considering that worldwide access to smartphones and the Internet has expanded beyond the expectations of many. We know that access is not evenly distributed, but the fact that more and more people are getting online cannot be disputed. Some MERL practitioners are using advanced artificial intelligence, machine learning, biometrics, and sentiment analysis in their work. And as smartphone and Internet use continue to grow, more data will be produced by people around the world. The way that MERL practitioners access and use data will likely continue to shift, and the composition of MERL teams and their required skillsets will also change.

The excitement over innovation and new technologies seen in 2014 could also be seen as naive, however, considering some of the negative consequences that have emerged, for example social media inspired violence (such as that in Myanmar), election and political interference through the Internet, misinformation and disinformation, and the race to the bottom through the online “gig economy.”

In this changing context, a team of MERL Tech practitioners (both enthusiasts and skeptics) embarked on a second round of research in order to try to provide an updated “State of the Field” for MERL Tech that looks at changes in the space between 2014 and 2019.

Based on MERL Tech conferences and wider conversations in the MERL Tech space, we identified three general waves of technology emergence in MERL:

  • First wave: Tech for Traditional MERL: Use of technology (including mobile phones, satellites, and increasingly sophisticated data bases) to do ‘what we’ve always done,’ with a focus on digital data collection and management. For these uses of “MERL Tech” there is a growing evidence base. 
  • Second wave:  Big Data. Exploration of big data and data science for MERL purposes. While plenty has been written about big data for other sectors, the literature on the use of big data and data science for MERL is somewhat limited, and it is more focused on potential than actual use. 
  • Third wave:  Emerging approaches. Technologies and approaches that generate new sources and forms of data; offer different modalities of data collection; provide ways to store and organize data, and provide new techniques for data processing and analysis. The potential of these has been explored, but there seems to be little evidence base to be found on their actual use for MERL. 

We’ll be doing a few sessions at the American Evaluation Association conference this week to share what we’ve been finding in our research. Please join us if you’ll be attending the conference!

Session Details:

Thursday, Nov 14, 2.45-3.30pm: Room CC101D

Friday, Nov 15, 3.30-4.15pm: Room CC101D

Saturday, Nov 16, 10.15-11am. Room CC200DE

Join us for MERL Tech DC, Sept 5-6th!

MERL Tech DC: Taking Stock

September 5-6, 2019

FHI 360 Academy Hall, 8th Floor
1825 Connecticut Avenue NW
Washington, DC 20009

We gathered at the first MERL Tech Conference in 2014 to discuss how technology was enabling the field of monitoring, evaluation, research and learning (MERL). Since then, rapid advances in technology and data have altered how most MERL practitioners conceive of and carry out their work. New media and ICTs have permeated the field to the point where most of us can’t imagine conducting MERL without the aid of digital devices and digital data.

The rosy picture of the digital data revolution and an expanded capacity for decision-making based on digital data and ICTs has been clouded, however, with legitimate questions about how new technologies, devices, and platforms — and the data they generate — can lead to unintended negative consequences or be used to harm individuals, groups and societies.

Join us in Washington, DC, on September 5-6 for this year’s MERL Tech Conference where we’ll be taking stock of changes in the space since 2014; showcasing promising technologies, ideas and case studies; sharing learning and challenges; debating ideas and approaches; and sketching out a vision for an ideal MERL future and the steps we need to take to get there.

Conference strands:

Tech and traditional MERL:  How is digital technology enabling us to do what we’ve always done, but better (consultation, design, community engagement, data collection and analysis, databases, feedback, knowledge management)? What case studies can be shared to help the wider sector learn and grow? What kinks do we still need to work out? What evidence base exists that can support us to identify good practices? What lessons have we learned? How can we share these lessons and/or skills with the wider community?

Data, data, and more data: How are new forms and sources of data allowing MERL practitioners to enhance their work? How are MERL Practitioners using online platforms, big data, digitized administrative data, artificial intelligence, machine learning, sensors, drones? What does that mean for the ways that we conduct MERL and for who conducts MERL? What concerns are there about how these new forms and sources of data are being used and how can we address them? What evidence shows that these new forms and sources of data are improving MERL (or not improving MERL)? What good practices can inform how we use new forms and sources of data? What skills can be strengthened and shared with the wider MERL community to achieve more with data?

Emerging tools and approaches: What can we do now that we’ve never done before? What new tools and approaches are enabling MERL practitioners to go the extra mile? Is there a use case for blockchain? What about facial recognition and sentiment analysis in MERL? What are the capabilities of these tools and approaches? What early cases or evidence is there to indicate their promise? What ideas are taking shape that should be tried and tested in the sector? What skills can be shared to enable others to explore these tools and approaches? What are the ethical implications of some of these emerging technological capabilities?

The Future of MERL: Where should we be going and what should the future of MERL look like? What does the state of the sector, of digital data, of technology, and of the world in which we live mean for an ideal future for the MERL sector? Where do we need to build stronger bridges for improved MERL? How should we partner and with whom? Where should investments be taking place to enhance MERL practices, skills and capacities? How will we continue to improve local ownership, diversity, inclusion and ethics in technology-enabled MERL? What wider changes need to happen in the sector to enable responsible, effective, inclusive and modern MERL?

Cross-cutting themes include diversity, inclusion, ethics and responsible data, and bridge-building across disciplines.

Submit your session ideas, register to attend the conference, or reserve a demo table for MERL Tech DC now!

You’ll join some of the brightest minds working on MERL across a wide range of disciplines – evaluators, development and humanitarian MERL practitioners, small and large non-profit organizations, government and foundations, data scientists and analysts, consulting firms and contractors, technology developers, and data ethicists – for 2 days of in-depth sharing and exploration of what’s been happening across this multidisciplinary field and where we should be heading.

Report back on MERL Tech DC

Day 1, MERL Tech DC 2018. Photo by Christopher Neu.

The MERL Tech Conference explores the intersection of Monitoring, Evaluation, Research and Learning (MERL) and technology. The main goals of “MERL Tech” as an initiative are to:

  • Transform and modernize MERL in an intentionally responsible and inclusive way
  • Promote ethical and appropriate use of tech (for MERL and more broadly)
  • Encourage diversity & inclusion in the sector & its approaches
  • Improve development, tech, data & MERL literacy
  • Build/strengthen community, convene, help people talk to each other
  • Help people find and use evidence & good practices
  • Provide a platform for hard and honest talks about MERL and tech and the wider sector
  • Spot trends and future-scope for the sector

Our fifth MERL Tech DC conference took place on September 6-7, 2018, with a day of pre-workshops on September 5th. Some 300 people from 160 organizations joined us for the 2-days, and another 70 people attended the pre-workshops.

Attendees came from a wide diversity of professions and disciplines:

What professional backgrounds did we see at MERL Tech DC in 2018?

An unofficial estimate on speaker racial and gender diversity is here.

Gender balance on panels

At this year’s conference, we focused on 5 themes (See the full agenda here):

  1. Building bridges, connections, community, and capacity
  2. Sharing experiences, examples, challenges, and good practice
  3. Strengthening the evidence base on MERL Tech and ICT4D approaches
  4. Facing our challenges and shortcomings
  5. Exploring the future of MERL

As always, sessions were related to: technology for MERL, MERL of ICT4D and Digital Development programs, MERL of MERL Tech, digital data for adaptive decisions/management, ethical and responsible data approaches and cross-disciplinary community building.

Big Data and Evaluation Session. Photo by Christopher Neu.

Sessions included plenaries, lightning talks and breakout sessions. You can find a list of sessions here, including any presentations that have been shared by speakers and session leads. (Go to the agenda and click on the session of interest. If we have received a copy of the presentation, there will be a link to it in the session description).

One topic that we explored more in-depth over the two days was the need to get better at measuring ourselves and understanding both the impact of technology on MERL (the MERL of MERL Tech) and the impact of technology overall on development and societies.

As Anahi Ayala Iacucci said in her opening talk — “let’s think less about what technology can do for development, and more about what technology does to development.” As another person put it, “We assume that access to tech is a good thing and immediately helps development outcomes — but do we have evidence of that?”

Feedback from participants

Some 17.5% of participants filled out our post-conference feedback survey, and 70% of them rated their experience either “awesome” or “good”. Another 7% of participants rated individual sessions through the “Sched” app, with an average session satisfaction rating of 8.8 out of 10.

Topics that survey respondents suggested for next time include: more basic tracks and more advanced tracks, more sessions relating to ethics and responsible data and a greater focus on accountability in the sector.  Read the full Feedback Report here!

What’s next? State of the Field Research!

In order to arrive at an updated sense of where the field of technology-enabled MERL is, a small team of us is planning to conduct some research over the next year. At our opening session, we did a little crowdsourcing to gather input and ideas about what the most pressing questions are for the “MERL Tech” sector.

We’ll be keeping you informed here on the blog about this research and welcome any further input or support! We’ll also be sharing more about individual sessions here.

MERL Tech London 2018 Agenda is out!

We’ve been working hard over the past several weeks to finish up the agenda for MERL Tech London 2018, and it’s now ready!

We’ve got workshops, panels, discussions, case studies, lightning talks, demos, community building, socializing, and an evening reception with a Fail Fest!

Topics range from mobile data collection, to organizational capacity, to learning and good practice for information systems, to data science approaches, to qualitative methods using mobile ethnography and video, to biometrics and blockchain, to data ethics and privacy and more.

You can search the agenda to find the topics, themes and tools that are most interesting, identify sessions that are most relevant to your organization’s size and approach, pick the session methodologies that you prefer (some of us like participatory and some of us like listening), and to learn more about the different speakers and facilitators and their work.

Tickets are going fast, so be sure to snap yours up before it’s too late! (Register here!)

View the MERL Tech London schedule & directory.

 

MERL Tech 101: Google forms

by Daniel Ramirez-Raftree, MERL Tech volunteer

In his MERL Tech DC session on Google Forms, Samhir Vesdev from IREX led a hands-on workshop on Google Forms and laid out some of the software’s capabilities and limitations. Much of the session focused on Google Forms’ central concepts and the practicality of building a form.

At its most fundamental level, a form is made up of several sections, and each section is designed to contain a question or prompt. The centerpiece of a section is the question cell, which is, as one would imagine, the cell dedicated to the question. Next to the question cell there is a drop down menu that allows one to select the format of the question, which ranges from multiple-choice to short answer.


At the bottom right hand corner of the section you will find three dots arranged vertically. When you click this toggle, a drop-down menu will appear. The options in this menu vary depending on the format of the question. One common option is to include a few lines of description, which is useful in case the question needs further elaboration or instruction. Another is the data validation option, which restricts the kinds of text that a respondent can input. This is useful in the case that, for example, the question is in a short answer format but the form administrators need the responses to be limited numerals for the sake of analysis.

The session also covered functions available in the “response” tab, which sits at the top of the page. Here one can find a toggle labeled “accepting responses” that can be turned off or on depending on the needs for the form.

Additionally, in the top right corner this tab, there are three dots arranged vertically, and this is the options menu for this tab. Here you will find options such as enabling email notifications for each new response, which can be used in case you want to be alerted when someone responds to the form. Also in this drop down, you can click “select response destination” to link the Google Form with Google Sheets, which simplifies later analysis. The green sheets icon next to the options drop-down will take you to the sheet that contains the collected data.

Other capabilities in Google Forms include the option for changing the color scheme, which you can access by clicking the palette icon at the top of the screen. Also, by clicking the settings button at the top of the screen you can limit the response amount to restrict people’s ability to skew the data by submitting multiple responses, or you can enable response editing after submission to allow respondents to go in and correct their response after submitting it.

Branching is another important tool in Google Forms. It can be used in the case that you want a particular response to a question (say, a multiple choice question) to lead the respondent to another related question only if they respond in a certain way.

For example, if in one section you ask “did you like the workshop?” with the answer options being “yes” and “no,” and if you want to know what they didn’t like about the workshop only if they answer “no,” you can design the sheet to take the respondent to a section with the question “what didn’t you like about the workshop?” only in the case that they answer “no,” and then you can design the sheet to bring the respondent back to the main workflow after they’ve answered this additional question.

To do this, create at least two new sections (by clicking “add section” in the small menu to the right of the sections), one for each path that a person’s response will lead them down. Then, in the options menu on the lower right hand side select “go to section based on answer” and using the menu that appears, set the path that you desire.

These are just some of the tools that Google Forms offers, but with just these it is possible to build an effective form to collect the data you need. Samhir ended with a word of caution that Google has been known to shut down popular apps, so you should be wary about building an organization strategy around Google Forms.

M&E Squared: Evaluating M&E Technologies

by Roger Nathanial Ashby, Co-Founder & Principal Consultant, OpenWise

The universe of MERL Tech solutions has grown exponentially. In 2008 monitoring and evaluating tech within global development could mostly be confined to mobile data collection tools like Open Data Kit (ODK), and Excel spreadsheets to analyze and visualize survey data. In the intervening decade a myriad of tools, companies and NGOs have been created to advance the efficiency and effectiveness of monitoring, evaluation, research and learning (MERL) through the use of technology. Whether it’s M&E platforms or suites, satellite imagery, remote sensors, or chatbots, new innovations are being deployed every day in the field.

However, how do we evaluate the impact when MERL Tech is the intervention itself? That was the question and task put to participants of the “M&E Squared” workshop at MERL Tech 2017.

Workshop participants were separated into three groups that were each given a case study to discuss and analyze. One group was given a case about improving the learning efficiency of health workers in Liberia through the mHero Health Information System (HIS). The system was deployed as a possible remedy to some of the information communication challenges identified during the 2014 West African Ebola outbreak. A second group was given a case about the use of RapidPro to remind women to attend antenatal care (ANC) for preventive malaria medicine in Guinea. The USAID StopPalu project goal was to improve the health of infants by increasing the percent of women attending ANC visits. The final group was given a case about using remote images to assist East African pastoralists. The Satellite Assisted Pastoral Resource Management System (SAPARM) informs pastoralists of vegetation through remote sensing imagery so they can make better decisions about migrating their livestock.

After familiarizing ourselves with the particulars of the case studies, each group was tasked to present their findings to all participants after pondering a series of questions. Some of the issues under discussion included

(1) “How would you assess your MERL Tech’s relevance?”

(2) “How would you evaluate the effectiveness of your MERL Tech?”

(3) “How would you measure efficiency?” and

(4) “How will you access sustainability?”.

Each group came up with some innovative answers to the questions posed and our facilitators and session leads (Alexandra Robinson & Sutyajeet Soneja from USAID and Molly Chen from RTI) will soon synthesize the workshop findings and notes into a concise written brief for the MERL Tech community.

Before the workshop closed we were all introduced to the great work done by SIMLab (Social Impact Lab) in this area through their SIMLab Monitoring and Evaluation Framework. The framework identifies key criteria for evaluating M&E including:

  1. Relevance – The extent to which the technology choice is appropriately suited to the priorities and capacities of the context of the target group or organization.
  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.
  3. Efficiency – Measure of the outputs (qualitative and quantitative) in relation to the inputs.
  4. Impact – The positive and negative changed produced by technology introduction, change in a technology tool, or platform on the overall development intervention (directly or indirectly; intended or unintended).
  5. Sustainability – Measure of whether the benefits of a technology tool or platform are likely to continue after donor funding has been withdrawn.
  6. Coherence – How related is the technology to the broader policy context (development, market, communication networks, data standards & interoperability mandates, and national & international law) within which the technology was developed and implemented.

While it’s unfortunate that SIMLab stopped most operations in early September 2017, their exceptional work in this and other areas lives on and you can access the full framework here.

I learned a great deal in this session from the facilitators and my colleagues attending the workshop. I would encourage everyone in the MERL Tech community to take the ideas generated during this workshop and the great work done by SIMLab into their development practice. We certainly intend to integrate much of these insights into our work at OpenWise. Read more about “The Evidence Agenda” here on SIMLab’s blog. 

 

 

 

You can’t have Aid…without AI: How artificial intelligence may reshape M&E

by Jacob Korenblum, CEO of Souktel Digital Solutions

Photo: wikipedia.org/

Potential—And Risk

The rapid growth of Artificial Intelligence—computers behaving like humans, and performing tasks which people usually carry out—promises to transform everything from car travel to personal finance. But how will it affect the equally vital field of M&E? As evaluators, most of us hate paper-based data collection—and we know that automation can help us process data more efficiently. At the same time, we’re afraid to remove the human element from monitoring and evaluation: What if the machines screw up?

Over the past year, Souktel has worked on three areas of AI-related M&E, to determine where new technology can best support project appraisals. Here are our key takeaways on what works, what doesn’t, and what might be possible down the road.

Natural Language Processing

For anyone who’s sifted through thousands of Excel entries, natural language processing sounds like a silver bullet: This application of AI interprets text responses rapidly, often matching them against existing data sets to find trends. No need for humans to review each entry by hand! But currently, it has two main limitations: First, natural language processing works best for sentences with simple syntax. Throw in more complex phrases, or longer text strings, and the power of AI to grasp open-ended responses goes downhill. Second, natural language processing only works for a limited number of (mostly European) languages—at least for now. English and Spanish AI applications? Yes. Chichewa or Pashto M&E bots? Not yet. Given these constraints, we’ve found that AI apps are strongest at interpreting basic misspelled answer text during mobile data collection campaigns (in languages like English or French). They’re less good at categorizing open-ended responses by qualitative category (positive, negative, neutral). Yet despite these limitations, AI can still help evaluators save time.

Object Differentiation

AI does a decent job of telling objects apart; we’ve leveraged this to build mobile applications which track supply delivery more quickly & cheaply. If a field staff member submits a photo of syringes and a photo of bandages from their mobile, we don’t need a human to check “syringes” and “bandages” off a list of delivered items. The AI-based app will do that automatically—saving huge amounts of time and expense, especially during crisis events. Still, there are limitations here too: While AI apps can distinguish between a needle and a BandAid, they can’t yet tell us whether the needle is broken, or whether the BandAid is the exact same one we shipped. These constraints need to be considered carefully when using AI for inventory monitoring.

Comparative Facial Recognition

This may be the most exciting—and controversial—application of AI. The potential is huge: “Qualitative evaluation” takes on a whole new meaning when facial expressions can be captured by cameras on mobile devices. On a more basic level, we’ve been focusing on solutions for better attendance tracking: AI is fairly good at determining whether the people in a photo at Time A are the same people in a photo at Time B. Snap a group pic at the end of each community meeting or training, and you can track longitudinal participation automatically. Take a photo of a larger crowd, and you can rapidly estimate the number of attendees at an event.

However, AI applications in this field have been notoriously bad at recognizing diversity—possibly because they draw on databases of existing images, and most of those images contain…white men. New MIT research has suggested that “since a majority of the photos used to train [AI applications] contain few minorities, [they] often have trouble picking out those minority faces”. For the communities where many of us work (and come from), that’s a major problem.

Do’s and Don’ts

So, how should M&E experts navigate this imperfect world? Our work has yielded a few “quick wins”—areas where Artificial Intelligence can definitely make our lives easier: Tagging and sorting quantitative data (or basic open-ended text), simple differentiation between images and objects, and broad-based identification of people and groups. These applications, by themselves, can be game-changers for our work as evaluators—despite their drawbacks. And as AI keeps evolving, its relevance to M&E will likely grow as well. We may never reach the era of robot focus group facilitators—but if robo-assistants help us process our focus group data more quickly, we won’t be complaining.

MERL Tech Round Up | November 1, 2017

It’s time for our second MERL Tech Round Up, a monthly compilation of MERL Tech News!

On the MERL Tech Blog:

We’ve been posting session summaries from MERL Tech DC. Here are some posts you may have missed in October:

Stuff we’re reading/watching/bookmarking:

There’s quite a bit to learn both in our “MERL / Tech” sector and in related sectors whose experiences are relatable to MERL Tech. Some thought-provoking pieces here:

Events:

Jobs

Head over to ICT4DJobs for a ton of tech related jobs. Here are some interesting ones for folks in the MERL Tech space:

If you’re not already signed up to the Pelican Initiative: Platform for Evidence-based Learning & Communication for Social Change, we recommend doing that. You will find all kinds of MERL and MERLTech related jobs and MERL-related advice. (Note: the Platform is an extremely active forum, so you may want to adjust your settings to receive weekly compilations).

Tag us on Twitter using #MERLTech if you have resources, events, or other news you’d like us to include here!

Don’t forget to submit your session ideas for MERL Tech London by November 10th!

Big data, big problems, big solutions

by Alvaro Cobo-Santillan, Catholic Relief Services (CRS); Jeff Lundberg, CRS; Paul Perrin, University of Notre Dame; and Gillian Kerr, LogicalOutcomes Canada. 

In the year 2017, with all of us holding a mini-computer at all hours of the day and night, it’s probably not too hard to imagine that “A teenager in Africa today has access to more information than the President of United States had 15 years ago”. So it also stands to reason that the ability to appropriately and ethically grapple with the use of that immense amount information has grown proportionately.

At the September MERL Tech event in Washington D.C. a panel that included folks from University of Notre Dame, Catholic Relief Services, and LogicalOutcomes spoke at length about three angles of this opportunity involving big data.

The Murky Waters of Development Data

What do we mean when we say that the world of development—particularly evaluation—data is murky? A major factor in this sentiment is the ambiguous polarity between research and evaluation data.

  • “Research seeks to prove; evaluation seeks to improve.” – CDC
  • “Research studies involving human subjects require IRB review. Evaluative studies and activities do not.”

Source: Patricia Rogers (2014), Ways of Framing the difference between research and evaluation, Better Evaluation Network.

This has led to debates as to the actual relationship between research and evaluation. Some see them as related, but separate activities, others see evaluation as a subset of research, and still others might posit that research is a specific case of evaluation.

But regardless, though motivations of the two may differ, research and evaluation look the same due to their stakeholders, participants, and methods.

If that statement is true, then we must hold both to similar protections!

What are some ways to make the waters less murky?

  • Deeper commitment to informed consent
  • Reasoned use of identifiers
  • Need to know vs. nice to know
  • Data security and privacy protocols
  • Data use agreements and protocols for outside parties
  • Revisit NGO primary and secondary data IRB requirements

Alright then, what can we practically do within our individual agencies to move the needle on data protection?

  • In short, governance. Responsible data is absolutely a crosscutting responsibility, but can be primarily championed through close partnerships between the M&E and IT Departments
  • Think about ways to increase usage of digital M&E – this can ease the implementation of R&D
  • Can existing agency processes and resources be leveraged?
  • Plan and expect to implement gradual behavior change and capacity building as a pre-requisite for a sustainable implementation of responsible data protections
  • Think in an iterative approach. Gradually introduce guidelines, tools and training materials
  • Plan for business and technical support structures to support protections

Is anyone doing any of the practical things you’ve mentioned?

Yes! Gillian Kerr from LogicalOutcomes spoke about highlights from an M&E system her company is launching to provide examples of the type of privacy and security protections they are doing in practice.

As a basis for the mindset behind their work, she notably presented a pretty fascinating and simple comparison of high risk vs. low risk personal information – year of birth, gender, and 3 digit zip code is unique for .04% of US residents, but if we instead include a 5 digit zip code over 50% of US residents could be uniquely identified. Yikes.

In that vein, they are not collecting names or identification and only year of birth (not month or day) and seek for minimal sensitive data defining data elements by level of risk to the client (i.e. city of residence – low, glucose level – medium, and HIV status – high).

In addition, asking for permission not only in the original agency permission form, but also in each survey. Their technical system maintains two instances – one containing individual level personal information with tight permission even for administrators and another with aggregated data with small cell sizes. Other security measures such as multi-factor authentication, encryption, and critical governance; such as regular audits are also in place.

It goes without saying that we collectively have ethical responsibilities to protect personal information about vulnerable people – here are final takeaways:

  • If you can’t protect sensitive information, don’t collect it.
  • If you can’t keep up with current security practices, outsource your M&E systems to someone who can.
  • Your technology roadmap should aspire to give control of personal information to the people who provide it (a substantial undertaking).
  • In the meantime, be more transparent about how data is being stored and shared
  • Continue the conversation by visiting https://responsibledata.io/blog
Register for MERL Tech London, March 19-20th 2018! Session ideas due November 10th.

Submit your session ideas for MERL Tech London by Nov 10th!

MERL Tech London

Please submit a session idea, register to attend, or reserve a demo table for MERL Tech London, on March 19-20, 2018, for in-depth sharing and exploration of what’s happening across the multidisciplinary monitoring, evaluation, research and learning field.

Building on MERL Tech London 2017, we will engage 200 practitioners from across the development and technology ecosystems for a two-day conference seeking to turn the theories of MERL technology into effective practice that delivers real insight and learning in our sector.

MERL Tech London 2018

Digital data and new media and information technologies are changing MERL practices. The past five years have seen technology-enabled MERL growing by leaps and bounds, including:

  • Adaptive management and ‘developmental evaluation’
  • Faster, higher quality data collection.
  • Remote data gathering through sensors and self-reporting by mobile.
  • Big Data and social media analytics
  • Story-triggered methodologies

Alongside these new initiatives, we are seeing increasing documentation and assessment of technology-enabled MERL initiatives. Good practice guidelines and new frameworks are emerging and agency-level efforts are making new initiatives easier to start, build on and improve.

The swarm of ethical questions related to these new methods and approaches has spurred greater attention to areas such as responsible data practice and the development of policies, guidelines and minimum ethical frameworks and standards for digital data.

Please submit a session idea, register to attend, or reserve a demo table for MERL Tech London to discuss all this and more! You’ll have the chance to meet, learn from, debate with 150-200 of your MERL Tech peers and to see live demos of new tools and approaches to MERL.

Submit Your Session Ideas Now!

Like previous conferences, MERL Tech London will be a highly participatory, community-driven event and we’re actively seeking practitioners in monitoring, evaluation, research, learning, data science and technology to facilitate every session.

Please submit your session ideas now. We are particularly interested in:

  • Case studies: Sharing end-to-end experiences/learning from a MERL Tech process
  • MERL Tech 101: How-to use a MERL Tech tool or approach
  • Methods & Frameworks: Sharing/developing/discussing methods and frameworks for MERL Tech
  • Data: Big, large, small, quant, qual, real-time, online-offline, approaches, quality, etc.
  • Innovations: Brand new, untested technologies or approaches and their application to MERL(Tech)
  • Debates: Lively discussions, big picture conundrums, thorny questions, contentious topics related to MERL Tech
  • Management: People, organizations, partners, capacity strengthening, adaptive management, change processes related to MERL Tech
  • Evaluating MERL Tech: comparisons or learnings about MERL Tech tools/approaches and technology in development processes
  • Failures: What hasn’t worked and why, and what can be learned from this?
  • Demo Tables: to share MERL Tech approaches, tools, and technologies
  • Other topics we may have missed!

Session Submission Deadline: Friday, November 10, 2017.

Session leads receive priority for the available seats at MERL Tech and a discounted registration fee. You will hear back from us in early December and, if selected, you will be asked to submit an updated and final session title, summary and outline by Friday, January 19th, 2018.

Register Now!

Please register to attend, or reserve a demo table for MERL Tech London 2018 to examine these trends with an exciting mix of educational keynotes, lightning talks, and group breakouts, including an evening Fail Festival reception to foster needed networking across sectors.

We are charging a modest fee to better allocate seats and we expect to sell out quickly again this year, so buy your tickets or demo tables now. Event proceeds will be used to cover event costs and to offer travel stipends for select participants implementing MERL Tech activities in developing countries.