Tag Archives: MERL Tech

MERL Tech DC: Session ideas due by May 12th!

Don’t forget to sign up to present, register to attend, or reserve a demo table for MERL Tech DC on September 7-8, 2017 at FHI 360 in Washington, DC.

Submit Your Session Ideas by Friday, May 12th!

Like previous conferences, MERL Tech DC 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:

  • Discussions around good practice and evidence-based review
  • Workshops with practical, hands-on exercises
  • Discussion and sharing on how to address methodological aspects such as rigor, bias, and construct validity in MERL Tech approaches
  • Future-focused thought provoking ideas and examples
  • Conversations about ethics, inclusion and responsible policy and practice in MERL Tech

Session leads receive priority for the available seats at MERL Tech and a discounted registration fee. You will hear back from us in early June and, if selected, you will be asked to submit the final session title, summary and outline by June 30.

If you have questions or are unsure about a submission idea, please get in touch with Linda Raftree.

Submit your ideas here! 

Six priorities for the MERL Tech community

by Linda Raftree, MERL Tech Co-organizer

IMG_4636Participants at the London MERL Tech conference in February 2017 crowdsourced a MERL Tech History timeline (which I’ve shared in this post). Building on that, we projected out our hopes for a bright MERL Tech Future. Then we prioritized our top goals as a group (see below). We’ll aim to continue building on these as a sector going forward and would love more thoughts on them.

  1. Figure out how to be responsible with digital data and not put people, communities, vulnerable groups at risk. Subtopics included: share data with others responsibly without harming anyone; agree minimum ethical standard for MERL and data collection; agree principles for minimizing data we collect so that only essential data is captured, develop duty of care principles for MERL Tech and digital data; develop ethical data practices and policies at organization levels; shift the power balance so that digital data convenience costs are paid by orgs, not affected populations; develop a set of quality standards for evaluation using tech
  2. Increase data literacy across the sector, at individual level and within the various communities where we are working.
  3. Overcome the extraction challenge and move towards true downward accountability. Do good user/human centered design and planning together, be ‘leaner’ and more user-focused at all stages of planning and MERL. Subtopics included: development of more participatory MERL methods; bringing consensus decision-making to participatory MERL; realizing the potential of tech to shift power and knowledge hierarchies; greater use of appreciative inquiry in participatory MERL; more relevant use of tech in MERL — less data, more empowering, less extractive, more used.
  4. Integrate MERL into our daily opfor blogerations to avoid the thinking that it is something ‘separate;’ move it to the core of operations management and make sure we have the necessary funds to do so; demystify it and make it normal! Subtopics included that: we’ve stopped calling “MERL” a “thing” and the norm is to talk about monitoring as part of operations; data use is enabling real-time coordination; no more paper based surveys.
  5. Improve coordination and interoperability as related to data and tools, both between organizations and within organizations. Subtopics included: more interoperability; more data-sharing platforms; all data with suitable anonymization is open; universal exchange of machine readable M&E Data (e.g., standards? IATI? a platform?); sector-wide IATI compliance; tech solutions that enable sharing of qualitative and quantitative data; systems of use across agencies; e.g., to refer feedback; coordination; organizations sharing more data; interoperability of tools. It was emphasized that donors should incentivize this and ensure that there are resources to manage it.
  6. Enhance user-driven and accessible tech that supports impact and increases efficiency, that is open source and can be built on, and that allows for interoperability and consistent systems of measurement and evaluation approaches.

In order to move on these priorities, participants felt we needed better coordination and sharing of tools and lessons among the NGO community. This could be through a platform where different innovations and tools are appropriately documented so that donors and organizations can more easily find good practice, useful tools and get a sense of ‘what’s out there’ and what it’s being used for. This might help us to focus on implementing what is working where, when, why and how in M&E (based on a particular kind of context) rather than re-inventing the wheel and endlessly pushing for new tools.

Participants also wanted to see MERL Tech as a community that is collaborating to shape the field and to ensure that we are a sector that listens, learns, and adopts good practices. They suggested hosting MERL Tech events and conferences in ‘the South;’ and building out the MERL Tech community to include greater representation of users and developers in order to achieve optimal tools and management processes.

What do you think – have we covered it all? What’s missing?

Technology in MERL: an approximate history

by Linda Raftree, MERL Tech co-organizer.

At MERL Tech London, Maliha Khan led us in an exercise to map out our shared history of MERL Tech. Following that we did some prioritizing around potential next steps for the sector (which I’ll cover in a next post).

She had us each write down 1) When we first got involved in something related to MERL Tech, and 2) What would we identify as a defining moment or key event in the wider field or in terms of our own experiences with MERL Tech.

The results were a crowdsourced MERL Tech Timeline on the wall.

 

An approximate history of tech in MERL 

We discussed the general flow of how technology had come to merge with MERL in humanitarian and development work over the past 20 years. The purpose was not to debate about exact dates, but to get a sense of how the field and community had emerged and how participants had experienced its ebbs and flows over time.

Some highlights:

  • 1996 digital photos being used in community-led research
  • 1998 mobile phones start to creep more and more into our work
  • 2000 the rise of SMS
  • 2001 spread of mobile phone use among development/aid workers, especially when disasters hit
  • 2003 Mobile Money comes onto the scene
  • 2004 enter smart phones; Asian tsunami happens and illustrates need for greater collaboration
  • 2005 increased focus on smartphones; enter Google maps
  • 2008 IATI, Hans Rosling interactive data talk/data visualization
  • 2009 ODK, FrontlineSMS, more and more Mobile Money and smart phones, open data; global ICT4D conference
  • 2010 Haiti earthquakes – health, GIS and infrastructure data collected at large scale, SMS reporting and mapping
  • 2011 FrontlineSMS’ data integrity guide
  • 2012 introduction and spread of cloud services in our work; more and more mapping/GIS in humanitarian and development work
  • 2013 more focus and funding from donors for tech-enabled work, more awareness and work on data standards and protocols, more use of tablets for data collection, bitcoin and blockchain enter the humanitarian/development scene; big data
  • 2014 landscape report on use of ICTs for M&E; MERL Tech conference starts to come together; Responsible Data Forum; U-Report and feedback loops; thinking about SDGs and Data revolution
  • 2015 Ebola crisis leads to different approach to data, big data concerns and ‘big data disasters’, awareness of the need for much improved coordination on tech and digital data; World Bank Digital Dividends report; Oxfam Responsible Data policy
  • 2016 real-time data and feedback loops are better unpacked and starting to be more integrated, adaptive management focus, greater awareness of need of interoperability, concerns about digital data privacy and security
  • 2017 MERL Tech London and the coming-together of the related community

What do you think? What’s missing? We’d love to have a more complete and accurate timeline at some point…. 

 

We have a data problem

by Emily Tomkys, ICT in Programmes at Oxfam GB

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

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

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

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

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

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

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

Dropping down your ignorance ratio: Campaigns meet KNIME

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

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

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

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

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

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

Watch Rodrigo’s MERL Tech Lightning Talk here!

 

5 Insights from MERL Tech 2016

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

MT1MT2

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

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

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

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

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

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

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

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

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

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

3) Humans > computers.

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

4) Sharing is caring.

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

5) Social impact investment is crucial.

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