Tag Archives: M&E

Maturity Models: Visualizing Progress Towards Next-Generation Transparency and Accountability

photo-sep-08-768x953By Alison Miranda (TAI) and Megan Colnar (Open Society Foundation). This is a cross-post of a piece published on September 17th on the Transparency and Accountability Initiative’s blog.

How can we assess progress on a second-generation way of working in the transparency, accountability and participation (TAP) field? Monitoring, evaluation, research, and learning (MERL) maturity models can provide some inspiration. The 2017 MERL Tech conference in Washington, DC was a two-day bonanza of lightening talks, plenary sessions, and hands-on workshops among participants who use technology for MERL.

Here are key conference takeaways from two MEL practitioners in the TAP field.

1. Making open data useful

Several MERL Tech sessions resonated deeply with the TAP field’s efforts to transition from fighting for transparent and open data towards linking open data to accountability and governance outcomes. Development Gateway and InterAction drew on findings from “Avoiding Data Graveyards” as we explored progress and challenges for International Aid Transparency Initiative (IATI) data use cases. While transparency is an important value, what is gained (or lost) in data use for collaboration when there are many different potential data consumers?

A partnership between Freedom House and DataKind is moving the Freedom in the World study towards a more transparent display of index sub-indicators, and building a more robust – and usable! – data set by reformatting and integrating their data and other secondary big data sets. What could such an initiative yield for the Extractive Industry Transparency Initiative (EITI), for example, if equivalent data sets were available?

And finally, as TAP practitioners are keenly aware, power and politics can overshadow evidence in decision making. Another Development Gateway presentation reminded us that it is important to work with data producers and users to identify decisions that are (or can be) data-driven, and to recognize when other factors are driving decisions. (The incentives to supply open data is whole other can of worms!)

Drawing on our second-generation TAP approach, more work is needed for the TAP and MERL fields to move from “open data everywhere, all of the time” to planning for, and encouraging more effective data use.

2. Tech for MERL for improved policy, practice, and outcomes

Among our favorite moments at MERL Tech was when Dobility Founder and CEO Christopher Robert remarked that “the most interesting innovations at MERL Tech aren’t the new, cutting-edge technology developments, but generic technology applied in innovative ways.” Unsurprising for a tech company focused on using affordable technology to enable quality data collection for social impact, but a refreshing reminder amidst the talk of ‘AI’, ‘chatbots’, and ‘blockchains’ for development coursing through the conference.

The TAP field is certainly not a stranger to employing technology from apps to curb trade corruption in Nigeria to Citizen Helpdesks in Nepal, Liberia, and Mali to crowdsourced political campaign expenditure monitoring in Bolivia, but our second-generation TAP insights remind us technology tools are not an end in themselves. MERL and technology are our means for collecting effective data, generating important insights and learning, building larger movements, and gathering context-specific evidence on transparency and accountability.

We are undoubtedly on the precipice of revolutionary technological advancements that can be readily (and maybe even affordably) deployed[1] to solve complex global challenges, but they will still be tools and not solutions.

3. Balancing inclusion and participation with efficiency and accuracy

We explored a constant conundrum for MERL: how to balance inclusion and participation with efficiency and accuracy. Girl Effect and Praekelt Foundation took “mixed methods” research to another level, combining online and offline efforts to understand user needs of adolescent girls and to support user-developed digital media content. Their iterative process showcased an effective way to integrate tech into the balancing act of inclusive – and holistic – design, paired with real-time data use.

This session on technology in citizen generated data brought to light two case studies of how tech can both help and hinder this balancing act. The World Café discussions underscored the importance of planning for – and recognizing the constraints on – feedback loops. And provided us a helpful reminder that MERL and tech professionals are often considering different “end users” in their design work!

So, which is it – balancing act or zero-sum game between inclusion and efficiency? The MERL community has long applied participatory methods. And tech solutions abound that can help with efficiency, accuracy, and inclusion. Indeed, the second-generation TAP focus on learning and collaboration is grounded in effective data use – but there are many potential “end users” to consider. These principles and practices can force uncomfortable compromises – particularly in the face of finite resources and limited data availability – but they are not at odds with each other. Perhaps the MERL and TAP communities can draw lessons from each other in striking the right balance.

4. Tech sees no development sector silos

One of the things that makes MERL Tech such an exciting conference, is the deliberate mixing of tech nerds with MERL nerds. It’s pretty unique in its dual targeting of both types of professionals who share a common purpose of social impact (where as conferences like ICT4D cast a wider net looking at application of technology to broader development issues). And, though we MERL professionals like to think of design and iteration as squarely within our wheelhouse, being in a room full of tech experts can quickly remind you that our adaptation game has a lot of room to grow. We talk about user-centered design in TAP, but when the tech crowd was asked in plenary “would you even think of designing software or an app without knowing who was going to use it?” they responded with a loud and exuberant laugh.

Tech has long employed systematic approaches to user-centered design, prototyping, iteration, and adaptation, all of which can offer compelling lessons to guide MERL practices and methods. Though we know Context is King, it is exhilarating to know that the tech specialists assembled at the conference work across traditional silos of development work (from health to corruption, and everything in between). End users are, of course, crucial to the final product but the life cycle process and steps follow a regular pattern, regardless of the topic area or users.

The second-generation wave in TAP similarly moved away from project-specific, fragmented, or siloed planning and learning towards a focus on collective approaches and long-term, more organic engagement.

American Evaluation Association President, Kathy Newcomer, quipped that maybe an ‘Academy Awards for Adaptation’ could inspire better informed and more adept evolutions to strategy as circumstances and context shift around us. Adding to this, and borrowing from the tech community, we wonder where we can build more room to beta test, follow real demand, and fail fast. Are we looking towards other sectors and industries enough or continuing to reinvent the wheel?

Alison left thinking:

  • Concepts and practices are colliding across the overlapping MERL, tech, and TAP worlds! In leading the Transparency and Accountability Initiative’s learning strategy, and supporting our work on data use for accountability, I often find myself toggling between different meanings of ‘data’, ‘data users’, and tech applications that can enable both of these themes in our strategy. These worlds don’t have to be compatible all the time, and concepts don’t have to compute immediately (I am personally still working out hypothetical blockchain applications for my MERL work!). But this collision of worlds is a helpful reminder that there are many perspectives to draw from in tackling accountable governance outcomes.
  • Maturity models come in all shapes and sizes, as we saw in the creative depictions created at MERL Tech that included, steps, arrows, paths, circles, cycles, and carrots! And the transparency and accountability field is collectively pursuing a next generation of more effective practice that will take unique turns for different accountability actors and outcomes. Regardless of what our organizational or programmatic models look like, MERL Tech reminded me that champions of continuous improvement are needed at all stages of the model – in MERL, in tech for development, and in the TAP field.

Megan left thinking:

  • That I am beginning to feel like I’m a Dr. Seuss book. We talked ‘big data’, ‘small data’, ‘lean data’, and ‘thick data’. Such jargon-filled conversations can be useful for communicating complex concepts simply with others. Ah, but this is also the problem. This shorthand glosses over the nuances that explain what we actually mean. Jargon is also exclusive—it clearly defines the limits of your community and makes it difficult for newcomers. In TAP, I can’t help but see missed opportunities for connecting our work to other development sectors. How can health outcomes improve without holding governments and service providers accountable for delivering quality healthcare? How can smallholder farmers expect better prices without governments budgeting for and building better roads? Jargon is helpful until it divides us up. We have collective, global problems and we need to figure out how to talk to each other if we’re going to solve them.
  • In general, I’m observing a trend towards organic, participatory, and inclusive processes—in MERL, in TAP, and across the board in development and governance work. This is, almost universally speaking, a good thing. In MERL, a lot of this movement is a backlash to randomistas and imposing The RCT Gold Standard to social impact work. And, while I confess to being overjoyed that the “RCT-or-bust” mindset is fading out, I can’t help but think we’re on a slippery slope. We need scientific rigor, validation, and objective evidence. There has to be a line between ‘asking some good questions’ and ‘conducting an evaluation’. Collectively, we are working to eradicate unjust systems and eliminate poverty, and these issues require not just our best efforts and intentions, but workable solutions. Listen to Freakonomic’s recent podcast When Helping Hurts and commit with me to find ways to keep participatory and inclusive evaluation techniques rigorous and scientific, too.

[1] https://channels.theinnovationenterprise.com/articles/ai-in-developing-countries

Using R to produce innovative, quick and reproducible evidence

By Claire Benard, formerly of Crisis UK and now with National Council for Voluntary Organizations (NCVO). 

Most people who work with data in MERL will have heard of R. Some people will have been properly introduced to it, but only a few will invest the necessary time in learning how to use it. Being a relatively late convert, I wanted to share my experience of moving from a traditional data analysis software package to a language based one, so I did a Lightning Talk at MERL Tech London. (You can watch the video below.)

First things first, what is R?

Aside from being the 18th letter of the alphabet, R is also a language and environment for statistical computing and graphics. 

But wait, you say… why should I use it?

This is what the five-minute video below is about, but in short, here are a few reasons:

  • There is nothing your current software package does that R doesn’t do.
  • R is free.
  • Using a programming language makes the analysis easy to reproduce, whether it’s because you need to produce similar analysis year on year or because you have a team of analysts who need to collaborate and understand each other’s work.
  • R is an open source technology. People from all backgrounds contribute to it and make new tools available for free regularly. This is you’re insurance to stay at the cutting edge of what is being developed.

Well, then, how do I get started? you wonder… 

If you’re more MERL than Tech, learning a new programming language can be daunting. There is a time and money cost to it and it’s hard to know where to start if you’re on your own.

In the video, I give a few tips. It’s also worth checking out free/cheap training online (for example here or here) ; looking out for a user group near you and getting advice from blogs, forums and newsletters.

Check out Claire’s presentation too if you want more info!


Tips for solar charging your data collection

Post by Julia Connors of Voltaicsystems. Email Julia with questions: julia@voltaicsystems.com

What is solar for M&E?

Solar technology can be extremely useful for M&E projects in areas with minimal or inconsistent access to power. Portable solar chargers can eliminate power constraints and keep phones and tablets reliably charged up in the field.

In this post we’ll discuss:

  • How to decide if solar is right for your project
  • How to properly size a solar charging system to meet your needs

Do you really need solar?

In many cases solar is not necessary and will simply add complexity and costs to your project. If your team can return every day to a central location with access to power, then the battery power of the tablet is sufficient in most scenarios. If not, we recommend implementing standard power saving tips to reduce power consumption during time out collecting data.



If you do have daily access to the grid but find that users need to recharge at least once while out or need to spend more than one day without power, then add an external battery pack. This cost-effective option allows your team to have extra power without carrying a full solar charging system. To size a battery for your needs, skip down to ‘Step 3’ below.

If you don’t have reliable access to grid power, the next section will help you determine which size solar charging system is best for you.

Sizing your solar charger system

The key to making solar successful in your project is finding the best system for your needs. If a system is underpowered then your team can still run out of power when they’re collecting data. On the other hand, if your system is too powerful it will be heavier and more expensive than needed. We recommend the following three steps for sizing your solar needs:

  1. Estimate your daily power consumption
  2. Determine your minimum solar panel size
  3. Determine your minimum battery size

Step 1: Estimate your daily power consumption

Once you have chosen the device you will be using in the field, it’s easy to determine your daily power consumption. First you’ll need to figure out the size of your device’s battery (in Watt hours). This can often be found by looking on the back of the battery itself or doing a quick Google search to find your device’s technical specifications.

Next, you’ll need to determine your battery usage per day. For example, if you use half of your device’s battery on a typical day of data collection, then your usage is 50%. If you need to recharge twice in one day, then your usage is 200%.

Once you have those numbers, use the formula below to find your daily power consumption:

Size of Device’s Battery (Wh) x Battery Usage (per day) =

Daily Power Consumption (Wh/day)

Step 2: Determine your minimum solar panel size

The larger your device, the bigger the solar panel (measured in Watts) you’ll need. This is because larger solar panels can generate more power from the sun than smaller panels. To determine the best solar panel size for your needs, use our formula below:

Daily Power Consumption (from Step 1) / Expected Hours of Good Sun*

x 2 (Standard Power Loss Variable) =

Solar Panel Minimum (Watts)

*We typically use 5 hours as a baseline for good sun and then adjust up or down depending on the conditions. High temperatures, clouds, or shading will reduce the power produced by the panel.

Since solar conditions change frequently throughout the day, we recommend choosing a panel that is 2-4 times the minimum size required.


Step 3: Determine minimum battery size

External batteries offer extra power storage so that your device will be charged when you need it. The battery acts as a perfect backup on cloudy and rainy days so it’s important to choose the right size for your device.

It can vary, but typically about 30% of power is lost in the transfer from the external battery to your device. Therefore, to determine the battery capacity needed for one day of use, we’ll use our power consumption data from Step 1 and divide by 0.7 (100% – 30% power loss).

Watt hours per day / 0.7 hours =

Watt battery capacity needed for 1 day of use


Picking the right system for your project

Now that you’ve done the math, you’re one step closer to choosing a solar charging system for your project. Since solar chargers come in many different forms, the last step to determining your perfect system is to think about how your team will be using the solar chargers in their work. It’s important to factor in storage for device/cables and how the user will be carrying the system.

Most users aren’t that technical, so having a pack that stores the battery and the device can simplify their experience (rather than handing over a battery and a panel that they need to figure how to organize during their day). By simply finding the right style and size, you’ll experience higher usage rates and make your team’s solar-powered data collection go more smoothly.