Tag Archives: feedback

Feedback Report from MERL Tech London 2018

MERL Tech London happened on March 19-20, 2018. Here are some highlights from session level feedback and the post-conference survey on the overall MERL Tech London experience.

If you attended MERL Tech London, please get in touch if you have any further questions about the feedback report or if you would like us to send you detailed (anonymized) feedback about a session you led. Please also be sure to send us your blog posts & session summaries so that we can post them on MERL Tech News!

Background on the data

  • 54 participants (~27%) filled out the post-conference survey via Google Forms.
  • 59 (~30%) rated and/or commented on individual sessions via the Sched app. Participants chose from three ’emoji’ options: a happy face 🙂 , a neutral face 😐 , and a sad face 🙁 . Participants could also leave their comments on individual sessions.
  • We received 616 session ratings/comments via Sched. Some people rated the majority of sessions they attended; others only rated 1-2 sessions.
  • Some reported that they did not feel comfortable rating sessions in the Sched app because they were unclear about whether session leads and the public could see the rating. In future, we will let participants know that only Sched administrators can see the identity of commenters and the ratings given to sessions.
  • We do not know if there is an overlap between those who filled out Sched and those that fed back via Google Forms because the Google Forms survey is anonymous.

Overall feedback

Here’s how survey participants rated the overall experience:

Breakout sessions– 137 ratings: 69% 🙂 30% 😐 and 13% 🙁

Responses were fairly consistent across both Sched ratings and Google Forms (the form asked people to identify their favorite session). Big data and data science sessions stand out with the highest number of favorable ratings and comments. General Data Protection Regulation (GDPR) and responsible data made an important showing, as did the session on participatory video in evaluation.

Sessions with favorable comments tended to include or combine elements of:

  • an engaging format
  • good planning and facilitation
  • strong levels of expertise
  • clear and understandable language and examples
  • and strategic use of case studies to point at a bigger picture that is replicable to other situations.

Below are the breakout sessions that received the most favorable ratings and comments overall. (Plenty of other sessions were also rated well but did not make the “top-top.”)

Session title

Comments

Be it resolved: In the near future, conventional evaluation and big data will be successfully integrated Brilliant session! Loved the format! Fantastic to have such experts taking part. Really appreciated the facilitation and that there was a time at the end for more open questions/discussion.
Innovative Use of Theory-Based and Data Science Evaluation Approaches Most interesting talk of the day (maybe more for the dedicated evaluation practitioners), very practical and easy to understand and I’m really looking forward to hearing more about the results as the work progresses!
Unpacking How Change Happened (or Didn’t): Participatory Video and Most Significant Change Right amout of explanation and using case studies to illustrate points and respond to questions rather than just stand alone case studies.
GDPR – What Is It and What Do We Do About It? GDPR and what we do about it – Great presentation starting off with some historical background, explaining with clarity how this new legislation is a rights-based approach and concluding on how for Oxfam this is not a compliance project but a modification in data culture. Amazing, innovative and the speaker knew his area very well.
The Best of Both Worlds? Combining Data Science and Traditional M&E to Understand Impact I learned so much from this session and was completely inspired by the presenter and the content. Clear – well paced – honest – open – collaborative and packed with really good insight. Amazing.
Big Data, Adaptive Management, and the Future of MERL Quite a mixed bag of presenters, with focus on different pieces on the overall topic. Speakers from Novometrics was particularly engaging and stimulated some good discussion.
Blockchain: Getting Past the Hype and Considering its Value for MERL Great group with good facilitation. Open ended question left lots of room for discussion without bias towards particular outcome. Learned lots and not just about blockchain.
LEAP, and How to Bring Data to Life in Your Organization Really great session, highly interactive, rich in concepts clearly and convincingly explained. No questions were left unanswered. Very insightful suggestions shared between the presenters/facilitators and the audience. Should be on the agenda of next MERL Tech Conference as well.
Who Watches the Watchers? Good Practice for Ethical MERL(Tech)

 

I came out with some really helpful material. Collaborative session and good workshop participants willing to share and mind map. Perhaps the lead facilitator could have been a bit more contextual. Not always clear. But, our table session was really helpful and output useful.
The GDPR is coming! Now what?! Practical Steps to Help You Get Ready Good session. Appreciated the handouts….

What could session leads improve on?

We also had a few sessions that were ranked closer to 😐 (somewhere around a 6 or 6.5 on a scale of 1-10). Why did participants rate some sessions lower?

  • “Felt like a product pitch”
  • “Title was misleading”
  • Participatory activity was unclear
  • Poor time management
  • “Case studies did not expand to learning for the sector – too much ‘this is what we did’ and not enough ‘this is what it means.””
  • Poor facilitation/moderation
  • “Too unstructured, meandering”
  • Low energy
  • “Only a chat among panelists, very little time for Q&A. No space to engage”

Additionally, some sessions had participants with very diverse levels of expertise and varied backgrounds and expectations, which seemed to affect session ratings.

Lightning Talks– 182 ratings: 74% 🙂 22% 😐 4% 🙁

Lightning talks consistently get the highest ratings at MERL Tech, and this year was no exception. As one participant said, “my favorite sessions were the lightning talks because they gave a really quick overview of really concrete uses of technology in M&E work. This really helped in getting an overview of the type of activities and projects that were going on.”  Participants rated almost all the lightning talks positively.

Plenary sessions– 192 ratings: 77% 🙂 21% 😐 and 2% 🙁

Here we include:  the welcome, discussions on MERL Tech priorities on Day 1, opening talks on both days, summary on Day 1, panel with donors, the closing ‘fishbowl’, and the Fail Fest.

Opening Talks:

  • People appreciated André’ Clarke’s stage setting talk on Day 1. “Clear, accessible and thoughtful.” “Nice deck!”
  • Anahi Ayala Iacucci’s opening talk on Day 2 was a hit: “Great keynote! Anahi is very engaging but also her content was really rich. Useful that she used a lot of examples and provided a lot of history.” And “Like Anahi says ‘The question to ask is what does technology do _to_ development, rather than what can technology do _for_ development.'”

Deep Dive into Priorities for the Sector:

  • Most respondents enjoyed the self-directed conversations around the various topics.
  • “Great way to set the tone for the following sessions….” “Some really valuable and practical insights shared.” “Great group, very interesting discussion, good way to get to know a few people.”

Fail Fest:

  • The Fail Fest was enjoyed by virtually everyone. “Brilliantly honest! Well done for having created the space and thank you to those who shared so openly.” “Awesome! Anahi definitely stole the show. What an amzing way to share learning, so memorable. Again, one to steal….” “I thought this was fun way to end the first day. All the presenters were really good and the session was well framed and facilitated by Wayan.”

Fishbowl:

  • There were mixed reactions to the “Fish Bowl”
  • “Great session and way to close the event!” “Fascinating – especially insights from Michael and Veronica.” “Not enough people volunteered to speak.” “Some speakers went on too long.”

Lunchtime Demos – 23 ratings: 52% 🙂 34% 😐 and 13% 🙁

We know that many MERL Tech participants are wary of being “sold” to. Feedback from past conferences has been that participants don’t like sales pitches disguised as breakout sessions and lightning talks. So, this year we experimented with the idea of lunchtime demo sessions. The idea was that these optional sessions would allow people with a specific interest in a tool or product to have dedicated time with the tool creators for a demo or Q&A. We hoped that doing demo sessions separately from breakout sessions would make the nature of the sessions clear. Judging from the feedback, we didn’t hit the mark. We’ll try to do better next time!

What went wrong?

  • Timing: “The schedule was too tight.” “Give more time to the lunch time demo sessions or change the format. I missed the Impact Mapper session on day 1 as there was insufficient time to eat, go for a comfort break and network. This is really disappointing. I suggest a dedicated hour in the programme on the first day to visit all the ICT provider stalls.”
  • Content: “Demo sessions were more like advertising sessions by respective companies, while nicely titled as if they were to explore topical issues. Demo sessions were all promising the world to us while we know how much challenge technology application faces in real-world. Overall so many demo sessions within a short 2-day conference compromised the agenda”
  • Framing and intent: “I don’t know that framing the lunch sessions as ‘product demos’ makes a ton of sense. Maybe force people to have real case studies or practical (hands-on) sessions, and make them regular sessions? Not sure.” “I think more care needs to be taken to frame the sessions run by the software companies with a proper declarations of interests…. Sessions led by software reps were a little less transparent in that they pitched their product, but through some other topic that people would be interested in. I think that it would be wise to make it a DOI [declaration of intent] that is scripted when people who have an interest declare their interest up front for every panel discussion at the beginning, even if they did a previous one. I think that way the rules would be a little clearer.” 

General Comments

Because we bring such a diverse group together in terms of field, experience, focus and interest, expectations are varied, and we often see conflicting suggestions. Whereas some would like more MERL content, others want more Tech content. Where as some learn a lot, others feel they have heard much of this before. Here are a few of the overall comments from Sched and the Google Form.

Who else should be at MERL Tech?

  • More donors “EU, DFID, MCC, ADB, WB, SIDA, DANIDA, MFA Finland”
  • “People working with governments in developing countries”
  • “People from the ‘field’. It was mentioned in one of the closing comments that the term ‘field’ is outdated and we are not sure what we mean anymore. Wrong. There couldn’t be a more striking difference in discussions during those two days between those with solid field experience and those lacking in it.”
  • “More Brits? There were a lot of Americans that came in from DC…”

Content that participants would like to see in the future

  • More framing: “An opening session that explains what MERL Tech is and all the different ways it’s being or can be used”
  • More specifics on how to integrate technology for specific purposes and for new purposes: “besides just allowing quicker and faster data collection and analysis”
  • More big data/data science: “Anything which combines data science, stats and qualitative research is really interesting for me and seems to be the direction a lot of organisations are going in.”
  • Less big data/data science: “The big data stuff was less relevant to me”
  • More MERL-related sessions: “I have a tech background, so I would personally have liked to have seen more MERL-related sessions.”
  • More tech-related sessions: “It was my first MERLTech, so enjoyed it. I felt that many of the presentations could have been more on-point with respect to the Tech side, rather than the MERL end (or better focus on the integration of the two).”
  • More “R” (Research): Institutional learning and research (evaluations as a subset of research).
  • More “L” Learning treated as topic of it’s own. By this I mean, the capture of tacit knowledge and good practice, use of this learning for adaptive management. Compared to my last MERL Tech, I felt this meeting better featured evaluation, or at least spoke of ‘E’ as its own independent letter. I would like to see this for ‘L.’”
  • More opportunities for smaller organisations to get best practice lessons.
  • More ethics discussions: “Consequences/whether or not we should be using personal data held by privately owned companies (like call details records from telecomms companies)” “The conceptual issues around the power dynamics and biases in data and tech ownership, collection, analysis and use and what it means for the development sector.”
  • Hands-on tutorials “for applying some of the methods people have used would be amazing, although may be beyond the remit of this conference.”
  • Coaching sessions: “one-on-ones to discuss challenges in setting up good M&E systems in smaller organisations – the questions we had, and the challenges we face did not feel like they would have been of relevance to the INGOs in the room.”

Some “Ah ha! Moments”

  • “The whole tech discussion needs to be framed around evaluation practice and theory – it seems to me that people come at this being somewhat data obsessed and driven but not starting from what we want to know and why that might be useful.”
  • “There is still quite a large gap between the data scientist and the M&E world – we really need to think more on how to bridge that gap. Despite the fact that it is recognized I do feel that much of the tech stuff was ‘ because we can’ and not because it is useful and answers to a concrete problem. On the other hand some of the tech was so complex that I also couldn’t assess whether it was really useful and what possible risks could be”
  • “I was surprised to see the scale of the impact GDPR is apparently making. Before the conference, I usually felt that most people didn’t have much of an interest in data privacy and responsible data.”
  • “That people were being honest and critical about tech!”
  • “That the tech world remains data hungry and data obsessed!”
  • “That this group is seriously confused about how tech and MERL can be used effectively as a general business practice.”
  • “This community is learning fast!”
  • “Hot topics like Big Data and Block Chain are only new tools, not silver bullets. Like RCTs a few years ago, we are starting to understand their best use and specific added value.”

Kudos

  • “A v useful conference for a growing sector. Well done!”
  • “Great opportunity for bringing together different sectors – sometimes it felt we were talking across tech, merl, and programming without much clarity of focus or common language but I suppose that shows the value of this space to discuss and work towards a common understanding and debate.”
  • “Small but meaningful to me – watching session leads attend other sessions and actively participating was great. We have such an overlap in purpose and in some cases almost no overlap in skillsets. Really felt like MERLTech was a community taking turns to learn from each other, which is pretty different from the other conferences I’ve been to, where the same people often present the same idea to a slightly different audience each year.”
  • “I loved the vibe. I’m coming to this late in my career but was made to feel welcome. I did not feel like an idiot. I found it so informative and some sessions were really inspiring. It will probably become an annual must go to event for me.”
  • “I was fully blown away. I haven’t learnt so much in a long time during a conference. The mix of types of sessions helps massively make the most of the knowledge in room, so keep up that format in the future.”
  • “I absolutely loved it. It felt so good to be with like minded people who have similar concerns and values….”

Thanks again to everyone who filled out the feedback forms and rated their sessions. This really does help us to adapt and improve. We take your ideas and opinions seriously!

If you’d like to experience MERL Tech, please join us in Johannesburg August 1-2, 2018, or Washington, DC, September 6-7, 2018!  The call for session ideas for MERL Tech DC is open through April 30th – please submit yours now!

Buckets of data for MERL

by Linda Raftree, Independent Consultant and MERL Tech Organizer

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

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

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

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

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

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

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

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

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