Tag Archives: evaluation

Blockchain as an M&E Tool

by Mike Cooper and Shailee Adinofi

This is the second in a series of blogs aimed at discussing and soliciting feedback on how the blockchain can benefit MEL practitioners in their work.  The series includes: What does Blockchain Offer to MERL, this post (Blockchain as an M&E Tool), and future posts on the use of MEL to inform Blockchain maturation, evaluating for trust in Blockchain applications, and integrating  blockchain into MEL Practices. The series leads into a MERL Tech Pre-Workshop on September 5th, 2018 in Washington D.C.  that will go into depth on possibilities and examples of MEL blockchain applications. Register here!

Introducing the Blockchain as an M&E Tool   

Blockchain is a technology that could transform many of the functions we now take for granted in our daily lives. It could change everything from supply chain management to trade to the Internet of Things (IOT), and possibly even serve as the backbone for the next evolution of the internet itself.  Within international development there have already been blockchain pilots for refugee assistance and financial inclusion (amongst others) with more varied pilots and scaled applications soon to come.

Technological solutions, however, need uptake in order for their effects to be truly known. This is no different for the blockchain. Technology solutions are not self-implementing — their uptake is dependent on social structures and human decision making.  Hence, while on paper the blockchain offers many benefits, the realization of these benefits in the monitoring, evaluation and learning (MEL) space requires close working with MEL practitioners to hear their concerns, excitement, and feedback on how the blockchain can best produce these benefits.

Blockchain removes intermediaries, thus increasing integrity

The blockchain is a data management tool for achieving data integrity, transparency, and addressing privacy concerns. It is a distributed software network of peer-to-peer transactions (data), which are validated through consensus, using pre-established rules. This can remove the need for a middleman or “intermediaries”, meaning that it can “disintermediate” the holders of a traditional MEL database, where data is stored and owned by a set of actors.  

Hence the blockchain solves two primary problems:

  1.   It reduces the need for “middlemen” (intermediaries) because it is peer-to-peer in nature.  For MEL, the blockchain may thus reduce the need for people to be involved in data management protocols, from data collection to dissemination, resulting in cost and time efficiencies.
  2.  The blockchain maintains data integrity (meaning that the data is immutable and is only shared in the intended manner) in a distributed peer-to-peer network where the reliability and trustworthiness of the network is inherent to the rules established in the consensus algorithms of the blockchain.  

So, what does this mean?  Simply put, a blockchain is a type of distributed immutable ledger or decentralized database that keeps continuously updated digital records of data ownership. Rather than having a central administrator manage a single database, a distributed ledger has a network of replicated databases, synchronized via the internet, and visible to anyone within the network (more on control of the network and who has access permissions below).

Advantages over Current Use of Centralized Data Management  

Distributed ledgers are much less vulnerable to loss of control over data integrity than current centralized data management systems. Loss of data integrity can happen in numerous ways, whether by hacking, manipulation or some other nefarious or accidental use.  Consider the multiple cases of political manipulation of census data as recorded in Poor Numbers: How We Are Misled by African Development Statistics and What to Do about It because census instruments are designed and census data analyzed/managed in a centralized fashion with little to no transparency.

Likewise, within the field of evaluation there has been increasing attention on p-hacking, where initial statistical results are manipulated on the back side to produce results more favorable to the original hypothesis.  Imagine if cleaned and anonymized data sets were put onto the blockchain where transparency, without sacrificing PII, makes p-hacking much more difficult (perhaps resulting in increased trust in data sets and their overall utility/uptake).

Centralized systems can have lost and/or compromised data (or loss of access) due to computer malfunctions or what we call “process malfunctions” where the bureaucratic control over the data builds artificially high barriers to access and subsequent use of the data by anyone outside the central sphere of control. This level of centralized control (as in the examples above regarding manipulation of census design/data and p-hacking) introduces the ability for data manipulation.

Computer malfunctions are mitigated by the blockchain because the data does not live in a central network hub but instead “lives’ in copies of the ledger that are distributed across every computer in the network. This lack of central control increases transparency. “Hashing” (a form of version control) ensures that any data manipulations in the blockchain are not included in the blockchain, meaning only a person with the necessary permissions can change the data on the chain. With the blockchain, access to information is as open, or closed, as is desired.  

How can we use this technology in MEL?

All MEL data must eventually find its way to a digital version of itself, whether it is entered from paper surveys or it goes through analytical software or straight into an Excel cell, with varying forms/rigor of quality control.  A benefit of blockchain is its compatibility with all digital data. It can include data files from all forms of data collection and analytical methods or software. Practitioners are free to collect data in whatever manner best suits their mandates with the blockchain becoming the data management tool at any point after collection, as the data can be uploaded to the blockchain at any point. Meaning data can be loaded directly by enumerators in the field or after additional cleaning/analysis.  

MEL has  specific data management challenges that the blockchain seems uniquely suited to overcome including 1. protection of Personally Identifiable Information (PII)/data integrity, 2. mitigating data management resource requirements, and 3. lowering barriers to end use through timely dissemination and increased access to reliable data.  

Let’s explore each of these below:

1. Increasing Protection and Integrity of Data: There might be a knee jerk reaction against increasing transparency in evaluation data management, given the prevalence of personally identifiable information (PII) and other sensitive data. Meeting internal quality control procedures for developing and sharing draft results is usually a long arduous process — even more so if delivering cleaned data sets.  Hence there might be hesitation in introducing new data management techniques given the priority given to the protection of PII balanced against the pressure to deliver data sets in a timely fashion.

However, we should learn a lesson from our counterparts in healthcare records management, one of the more PII and sensitive data laden data management fields in the world.  The blockchain has seen piloting in healthcare records management precisely because it is able to secure the integrity of sensitive data in such an efficient manner.

Imagine an evaluator completes a round of household surveys, the data is entered, cleaned and anonymized and the data files are ready to be sent to whomever the receiver is (funder, public data catalog, etc.)  The funder requires that the data uploaded to the blockchain is done using a Smart Contract. Essentially a Smart Contract is a set of “if……then” protocols on the Ethereum network (a specific type of blockchain) which can say “if all data has been cleaned of PII and is appropriately formatted….etc….etc…, it can be accepted onto the blockchain.”  If the requirements written into the Smart Contract are not met, the data is rejected and not uploaded to the blockchain (see point 2 below). So, in the case where proper procedures or best or preferred practices are not met, the data is not shared and remains safe within the confines of a (hopefully) secure and reliable centralized database.

This example demonstrates one of the unsung values of the blockchain. When correctly done (meaning the Smart Contract is properly developed) it can ensure that only the data that is appropriate is shared and is in fact shared only with those meant to have it in a manner where the data cannot be manipulated.  This is an advantage over current practice where human error can result in PII being released or unuseable or incompatible data files being shared.

The blockchain also has inherent quality control protocols around version control that mitigate against manipulation of the data for whatever reason. Hashing is partly a summary labelling of different encrypted data sets on the blockchain where any modification to the data set results in a different hash for that data set.  Hence version control is automatic and easily tracked through the different hashes which are one way only (meaning that once the data is hashed it cannot be reverse engineered to change the original data). Thus, all data on the blockchain is immutable.

2. Decreasing Data Management Resources: Current data management practice is very resource intensive for MEL practitioners.  Data entry, creation of data files, etc. requires ample amounts of time, mostly spent guarding against error, which introduces timeliness issues where processes take so long the data uses its utility by the time it is “ready” for decision makers.  A future post in this series will cover how the blockchain can introduce efficiencies at various points in the data management process (from collection to dissemination). There are many unknowns in this space that require further thinking about the ability to embed automated cleaning and/or analytical functions into the blockchain or compatibility issues around data files and software applications (like STATA or NIVIVO).  This series of posts will highlight broad areas where the blockchain can introduce the benefits of an innovation as well as finer points that still need to be “unpacked” for the benefits to materialize.

3. Distributed ledger enables timely dissemination in a flexible manner:  With the increased focus on the use of evaluation data, there has been a correlated increase in discussion in how evaluation data is shared.

Current data dissemination practices include:

  • depositing them with a data center, data archive, or data bank
  • submitting them to a journal to support a publication
  • depositing them in an institutional repository
  • making them available online via a project or institutional website
  • making them available informally between researchers on a peer-to-peer basis

All these avenues of dissemination are very resource intensive. Each avenue has its own procedures, protocols, and other characteristics that may not be conducive to timely learning. Timelines for publishing in journals is long with incentives towards only publishing positive results, contributing to a dismal utilization rates of results.  Likewise, many institutional evaluation catalogs are difficult to navigate, often incomplete, and generally not user friendly. (We will look at query capabilities on the blockchain later in the blog series).

Using the blockchain to manage and disseminate data could result in more timely and transparent sharing.  Practitioners could upload data to the chain at any point after collection, and with the use of Smart Contracts, data can be widely distributed in a controlled manner.  Data sets can be easily searchable and available in much timelier and user-friendly fashion to a much larger population. This creates the ability to share specific data with specific partners (funders, stakeholders, the general public) in a more automated fashion and on a timelier basis.  Different Smart Contracts can be developed so that funders can see all data as soon as it is collected in the field, while a different Smart Contract with local officials allows them to see data relevant to their locality only after it is entered, cleaned, etc.).

With the help of read/write protocols, anyone can control the extent to which data is shared. Use of the data is immutable, meaning it cannot be changed (in contrast to current practice where we hope the PDF is “good enough” to guard against modification but most times data are pushed out in excel sheets, or something similar, with no way to determine what the “real” data when different versions appear).   

Where are we?

We are in the early stages of understanding, developing and exploring the blockchain in general and with MEL in particular. On September 5th, we’ll be leading a day-long Pre-Conference Workshop on What Blockchain Can Do For MERL. The Pre-Conference Workshop and additions to this blog series will focus on how:

  • The blockchain can introduce efficiencies in MEL data management
  • The blockchain can facilitate “end use” whether it is accountability, developmental, formative, etc.
  • To work with MEL practitioners and other stakeholders to improve the uptake of the blockchain as an innovation by overcoming regulatory, organizational and cultural barriers.  

This process is meant to be collaborative so we invite others to help inform us on what issues they think warrant further exploration.  We look forward to collaborating with others to unpack these issues to help develop thinking that leads to appropriate uptake of blockchain solutions to MEL problems.  

Where are we going?

As it becomes increasingly possible that blockchain will be a disruptive technology, it is critical that we think about how it will affect the work of MEL practitioners.  To this end, stay tuned for a few more posts, including:

  • How can MEL inform Blockchain maturation?
  • Evaluating for Trust in Blockchain applications
  • How can we integrate blockchain into MEL Practices?

We would greatly benefit from feedback on this series to help craft topics that the series can cover.  Please comment below or contact the authors with any feedback, which would be greatly appreciated.

Register here for the September 5th workshop on Blockchain and MERL!

Michael Cooper is a former Associate Director at Millennium Challenge Corporation and the U.S. State Dept in Policy and Evaluation.  He now heads Emergence, a firm that specializes in MEL and Blockchain services. He can be reached at emergence.cooper@gmail.com or through the Emergence website.

Shailee Adinolfi is an international development professional with over 15 years of experience working at the intersection of financial services, technology, and global development. Recently, she performed business development, marketing, account management, and solution design as Vice President at BanQu, a Blockchain-based identity platform. She held a variety of leadership roles on projects related to mobile banking, financial inclusion, and the development of emerging markets. More about Shailee 

What does Blockchain offer to MERL?

by Shailee Adinolfi

By now you’ve read at least one article on the potential of blockchain, as well as the challenges in its current form. USAID recently published a Primer on Blockchain: How to assess the relevance of distributed ledger technology to international development, which explains that distributed ledgers are “a type of shared computer database that enables participants to agree on the state of a set of facts or events (frequently described as an “authoritative shared truth”) in a peer-to-peer fashion without needing to rely on a single, centralized, or fully trusted party”.

Explained differently, the blockchain introduces cost savings and resource efficiencies by allowing data to be entered, stored and shared in an immutable fashion by substituting the need for a trusted third party with algorithms and cryptography.

The blockchain/Distributed Ledger Technology (DLT) industry is evolving quickly, as are the definitions and terminology. Blockchain may not solve world hunger, but the core promises are agreed upon by many – transparency, auditability, resiliency, and streamlining. The challenges, which companies are racing to be the first to address, include scale (speed of transactions), security, and governance.

It’s not time to sit back wait and see what happens. It’s time to deepen our understanding. Many have already begun pilots across sectors. As this McKinsey article points out, early data from pilots shows strong potential in the Agriculture and Government sectors, amongst others. The article indicates that scale may be as little as 3-5 years away, and that’s not far out.

The Center for Global Development’s Michael Pisa argues that the potential benefits of blockchain do not outweigh the associated costs and complexities right now. He suggests that the development community focus its energies and resources on bringing down barriers to actual implementation, such as standards, interoperability, de-siloing data, and legal and regulatory rules around data storage, privacy and protection.

One area where blockchain may be useful is Monitoring, Evaluation, Research and Learning (MERL). But we need to dig in and understand better what the potentials and pitfalls are.

Join us on September 5th for a one-day workshop on Blockchain and MERL at Chemonics International where we will discuss what blockchain offers to MERL.

This is the first in a series of blogs at discussing and soliciting feedback on how the blockchain can benefit MEL practitioners in their work.  The series includes this post (What does Blockchain Offer to MERL),  Blockchain as an M&E Tool, and future posts on the use of MEL to inform Blockchain maturation, evaluating for trust in Blockchain applications, and integrating  blockchain into MEL Practices. 

 

Integrating Big Data into Evaluation: a conversation with Michael Bamberger and Rick Davies

At MERL Tech London, 2018, we invited Michael Bamberger and Rick Davies to debate the question of whether the enthusiasm for Big Data in Evaluation is warranted. At their session, through a formal debate (skillfully managed by Shawna Hoffman from The Rockefeller Foundation) they discussed whether Big Data and Evaluation would eventually converge, whether one would dominate the other, how can and should they relate to each other, and what risks and opportunities there are in this relationship.

Following the debate, Michael and Risk wanted to continue the discussion — this time exploring the issues in a more conversational mode on the MERL Tech Blog, because in practice both of them see more than one side to the issue.

So, what do Rick and Michael think — will big data integrate with evaluation — or is it all just hype?

Rick: In the MERL Tech debate I put a lot of emphasis on the possibility that evaluation, as a field, would be overwhelmed by big data / data science rhetoric. But since then I have been thinking about a countervailing development, which is that evaluative thinking is pushing back against unthinking enthusiasm for the use of data science algorithms. I emphasise “evaluative thinking” rather than “evaluators” as a category of people, because a lot of this pushback is coming from people who would not identify themselves as evaluators. There are different strands to this evaluative response.

One is a social justice perspective, reflected in recent books such as “Weapons of Math Destruction”, “Automated Inequality”, and “Algorithms of Oppression” which emphasise the human cost of poorly designed and or poorly supervised use of algorithms using large amounts of data to improve welfare and justice administration. Another strand is more like a form of exploratory philosophy, and has focused on how it might be possible to define “fairness” when designing and evaluating algorithms that have consequences for human welfare[ See 1, 2, 3, 4]. Another strand is perhaps more technical in focus, but still has a value concern. This is the literature on algorithmic transparency. Without transparency it is difficult to assess fairness [See 5, 6, ] Neural networks are often seen as a particular challenge. Associated with this are discussions about “the right to explanation” and what this means in practice[1,]

In parallel there is also some infiltration of data science thinking into mainstream evaluation practice. DFID is funding the World Bank’s Strategic Impact Evaluation Fund (SIEF) latest call for “nimble evaluations” [7]. These are described as rapid and low cost and likely to take the form of an RCT but ones which are focused on improving implementation rather than assessing overall impact [8]. This type of RCT is directly equivalent to A/B testing used by the internet giants to improve the way their platforms engage with their users. Hopefully these nimble approaches may bring a more immediate benefit to the people’s lives than RCTs which have tried to assess the impact of a whole project and then inform the design of subsequent projects.

Another recent development is the World Bank’s Data Science competition [9], where participants are being challenged to develop predictive models of household poverty status, based on World Bank Household Survey data.  The intention is that they should provide a cheaper means of identifying poor households than simply relying on what can be very expensive and time consuming nationwide household surveys. At present the focus on the supporting website is very technical. As far as I can see there is no discussion of how the winning prediction model will be used and an how any risks of adverse effects might be monitored and managed.  Yet as I suggested at MERLTech London, most algorithms used for prediction modelling will have errors. The propensity to generate False Positives and False Negatives is machine learning’s equivalent of original sin. It is to be expected, so it should be planned for. Plans should include systematic monitoring of errors and a public policy for correction, redress and compensation.

Michael:  These are both important points, and it is interesting to think what conclusions we can draw for the question before us.  Concerning the important issue of algorithmic transparency (AT), Rick points out that a number of widely discussed books and articles have pointed out the risk that the lack of AT poses for democracy and particularly for poor and vulnerable groups. Virginia Eubanks, one of the authors cited by Rick, talks about the “digital poorhouse” and how unregulated algorithms can help perpetuate an underclass.  However, I think we should examine more carefully how evaluators are contributing to this discussion. My impression, based on very limited evidence is that evaluators are not at the center — or even perhaps the periphery — of this discussion. Much of the concern about these issues is being generated by journalists, public administration specialists or legal specialists.  I argued in an earlier MERL Tech post that many evaluators are not very familiar with big data and data analytics and are often not very involved in these debates.  This is a hypothesis that we hope readers can help us to test.

Rick’s second point, about the infiltration of data science into evaluation is obviously very central to our discussion.  I would agree that the World Bank is one of the leaders in the promotion of data science, and the example of “nimble evaluation” may be a good example of convergence between data science and evaluation.  However, there are other examples where the Bank is on the cutting edge of promoting new information technology, but where potential opportunities to integrate technology and evaluation do not seem to have been taken up.  An example would be the Bank’s very interesting Big Data Innovation Challenge, which produced many exciting new applications of big data to development (e.g. climate smart agriculture, promoting financial inclusion, securing property rights through geospatial data, and mapping poverty through satellites). The use of data science to strengthen evaluation of the effectiveness of these interventions, however, was not mentioned as one of the objectives or outputs of this very exciting program.  

It would also be interesting to explore to what extent the World Bank Data Science competition that Rick mentions resulted in the convergence of data science and evaluation, or whether it was simply testing new applications of data science.

Finally, I would like to mention two interesting chapters in Cybersociety, Big Data and Evaluation edited by Petersson and Breul (2017, Transaction Publications).  One chapter (by Hojlund et al) reports on a survey which found that only 50% of professional evaluators claimed to be familiar with the basic concepts of big data, and only about 10% reported having used big data in an evaluation.  In another chapter, Forss and Noren reviewed a sample of Terms of Reference (TOR) for evaluations conducted by different development agencies, where they found that none of the 25 TOR specifically required the evaluators to incorporate big data into their evaluation design.

It is difficult to find hard evidence on the extent to which evaluators are familiar with, sympathetic to, or using big data into their evaluations, but the examples mentioned above show that there are important questions about the progress made towards the convergence of evaluation and big data.  

We invite readers to share their experiences both on how the two professions are starting to converge, or on the challenges that slow down, or even constrain the process of convergence.

Take our survey on Big Data and Evaluation!

Or sign up for Michael’s full-day workshop on Big Data and Evaluation in Washington, DC, on September 5th, 2018! 

Big data or big hype: a MERL Tech debate

by Shawna Hoffman, Specialist, Measurement, Evaluation and Organizational Performance at the Rockefeller Foundation.

Both the volume of data available at our fingertips and the speed with which it can be accessed and processed have increased exponentially over the past decade.  The potential applications of this to support monitoring and evaluation (M&E) of complex development programs has generated great excitement.  But is all the enthusiasm warranted?  Will big data integrate with evaluation — or is this all just hype?

A recent debate that I chaired at MERL Tech London explored these very questions. Alongside two skilled debaters (who also happen to be seasoned evaluators!) – Michael Bamberger and Rick Davies – we sought to unpack whether integration of big data and evaluation is beneficial – or even possible.

Before we began, we used Mentimeter to see where the audience  stood on the topic:

Once the votes were in, we started.

Both Michael and Rick have fairly balanced and pragmatic viewpoints; however, for the sake of a good debate, and to help unearth the nuances and complexity surrounding the topic, they embraced the challenge of representing divergent and polarized perspectives – with Michael arguing in favor of integration, and Rick arguing against.

“Evaluation is in a state of crisis,” Michael argued, “but help is on the way.” Arguments in favor of the integration of big data and evaluation centered on a few key ideas:

  • There are strong use cases for integration. Data science tools and techniques can complement conventional evaluation methodology, providing cheap, quick, complexity-sensitive, longitudinal, and easily analyzable data.
  • Integration is possible. Incentives for cross-collaboration are strong, and barriers to working together are reducing. Traditionally these fields have been siloed, and their relationship has been characterized by a mutual lack of understanding of the other (or even questioning of the other’s motivations or professional rigor).  However, data scientists are increasingly recognizing the benefits of mixed methods, and evaluators are seeing the potential to use big data to increase the number of types of evaluation that can be conducted within real-world budget, time and data constraints. There are some compelling examples (explored in this UN Global Pulse Report) of where integration has been successful.
  • Integration is the right thing to do.  New approaches that leverage the strengths of data science and evaluation are potentially powerful instruments for giving voice to vulnerable groups and promoting participatory development and social justice.   Without big data, evaluation could miss opportunities to reach the most rural and remote people.  Without evaluation (which emphasizes transparency of arguments and evidence), big data algorithms can be opaque “black boxes.”

While this may paint a hopeful picture, Rick cautioned the audience to temper its enthusiasm. He warned of the risk of domination of evaluation by data science discourse, and surfaced some significant practical, technical, and ethical considerations that would make integration challenging.

First, big data are often non-representative, and the algorithms underpinning them are non-transparent. Second, “the mechanistic approaches offered by data science, are antithetical to the very notion of evaluation being about people’s values and necessarily involving their participation and consent,” he argued. It is – and will always be – critical to pay attention to the human element that evaluation brings to bear. Finally, big data are helpful for pattern recognition, but the ability to identify a pattern should not be confused with true explanation or understanding (correlation ≠ causation). Overall, there are many problems that integration would not solve for, and some that it could create or exacerbate.

The debate confirmed that this question is complex, nuanced, and multi-faceted. It helped to remind that there is cause for enthusiasm and optimism, at the same time as a healthy dose of skepticism. What was made very clear is that the future should leverage the respective strengths of these two fields in order to maximize good and minimize potential risks.

In the end, the side in favor of integration of big data and evaluation won the debate by a considerable margin.

The future of integration looks promising, but it’ll be interesting to see how this conversation unfolds as the number of examples of integration continues to grow.

Interested in learning more and exploring this further? Stay tuned for a follow-up post from Michael and Rick. You can also attend MERL Tech DC in September 2018 if you’d like to join in the discussions in person!

Evaluating ICT4D projects against the Digital Principles

By Laura Walker McDonald,  This post was originally published on the Digital Impact Alliance’s Blog on March 29, 2018.

As I have written about elsewhere, we need more evidence of what works and what doesn’t in the ICT4D and tech for social change spaces – and we need to hold ourselves to account more thoroughly and share what we know so that all of our work improves. We should be examining how well a particular channel, tool or platform works in a given scenario or domain; how it contributes to development goals in combination with other channels and tools; how the team selected and deployed it; whether it is a better choice than not using technology or using a different sort of technology; and whether or not it is sustainable.

At SIMLab, we developed our Framework for Monitoring and Evaluation of Technology in Social Change projects to help implementers to better measure the impact of their work. It offers resources towards a minimum standard of best practice which implementers can use or work toward, including on how to design and conduct evaluations. With the support of the Digital Impact Alliance (DIAL), the resource is now finalized and we have added new evaluation criteria based on the Principles for Digital Development.

Last week at MERL Tech London, DIAL was able to formally launch this product by sharing a 2-page summary available at the event and engaging attendees in a conversation about how it could be used. At the event, we joined over 100 organizations to discuss Monitoring, Evaluation, Research and Learning related to technology used for social good.

Why evaluate?

Evaluations provide snapshots of the ongoing activity and the progress of a project at a specific point in time, based on systematic and objective review against certain criteria. They may inform future funding and program design; adjust current program design; or to gather evidence to establish whether a particular approach is useful. They can be used to examine how, and how far, technology contributes to wider programmatic goals. If set up well, your program should already have evaluation criteria and research questions defined, well before it’s time to commission the evaluation.

Evaluation criteria provide a useful frame for an evaluation, bringing in an external logic that might go beyond the questions that implementers and their management have about the project (such as ‘did our partnerships on the ground work effectively?’ or ‘how did this specific event in the host country affect operations?’) to incorporate policy and best practice questions about, for example, protection of target populations, risk management, and sustainability. The criteria for an evaluation could be any set of questions that draw on an organization’s mission, values, principles for action; industry standards or other best practice guidance; or other thoughtful ideas of what ‘good’ looks like for that project or organization. Efforts like the Principles for Digital Development can set useful standards for good practice, and could be used as evaluation criteria.

Evaluating our work, and sharing learning, is radical – and critically important

While the potential for technology to improve the lives of vulnerable people around the world is clear, it is also evident that these improvements are not keeping pace with the advances in the sector. Understanding why requires looking critically at our work and holding ourselves to account. There is still insufficient evidence of the contribution technology makes to social change work. What evidence there is often is not shared or the analysis doesn’t get to the core issues. Even more important, the learnings from what has not worked and why have not been documented and absorbed.

Technology-enabled interventions succeed or fail based on their sustainability, business models, data practices, choice of communications channel and technology platform; organizational change, risk models, and user support – among many other factors. We need to build and examine evidence that considers these issues and that tells us what has been successful, what has failed, and why. Holding ourselves to account against standards like the Principles is a great way to improve our practice, and honor our commitment to the people we seek to help through our work.

Using the Digital Principles as evaluation criteria

The Principles for Digital Development are a set of living guidance intended to help practitioners succeed in applying technology to development programs. They were developed, based on some pre-existing frameworks, by a working group of practitioners and are now hosted by the Digital Impact Alliance.

These nine principles could also form a useful set of evaluation criteria, not unlike OECD evaluation criteria, or Sphere standards. Principles overlap, so data can be used to examine more than one criterion, and ot every evaluation would need to consider all of the Digital Principles.

Below are some examples of Digital Principles and sample questions that could initiate, or contribute to, an evaluation.

Design with the User: Great projects are designed with input from the stakeholders and users who are central to the intended change. How far did the team design the project with its users, based on their current tools, workflows, needs and habits, and work from clear theories of change and adaptive processes?

Understand the Existing Ecosystem: Great projects and programs are built, managed, and owned with consideration given to the local ecosystem. How far did the project work to understand the local, technology and broader global ecosystem in which the project is situated? Did it build on existing projects and platforms rather than duplicating effort? Did the project work sensitively within its ecosystem, being conscious of its potential influence and sharing information and learning?

Build for Sustainability: Great projects factor in the physical, human, and financial resources that will be necessary for long-term sustainability. How far did the project: 1) think through the business model, ensuring that the value for money and incentives are in place not only during the funded period but afterwards, and 2) ensure that long-term financial investments in critical elements like system maintenance and support, capacity building, and monitoring and evaluation are in place? Did the team consider whether there was an appropriate local partner to work through, hand over to, or support the development of, such as a local business or government department?

Be Data Driven: Great projects fully leverage data, where appropriate, to support project planning and decision-making. How far did the project use real-time data to make decisions, use open data standards wherever possible, and collect and use data responsibly according to international norms and standards?

Use Open Standards, Open Data, Open Source, and Open Innovation: Great projects make appropriate choices, based on the circumstances and the sensitivity of their project and its data, about how far to use open standards, open the project’s data, use open source tools and share new innovations openly. How far did the project: 1) take an informed and thoughtful approach to openness, thinking it through in the context of the theory of change and considering risk and reward, 2) communicate about what being open means for the project, and 3) use and manage data responsibly according to international norms and standards?

For a more complete set of guidance, see the complete Framework for Monitoring and Evaluating Technology, and the more nuanced and in-depth guidance on the Principles, available on the Digital Principles website.

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.

 

Moving from “evaluation” to “impact management”

by Richa Verma, Resident Entrepreneur at Social Cops. This post originally appeared on the Social Cops blog on August 28, 2017.

When I say that Impact Evaluation is history, I mean it. Some people will question this. After all, Impact Evaluation just became mainstream in the last decade, driven by great improvements in experimental design methods like randomized control trials (RCTs). So how can I say that it’s already a thing of the past? It’s not Impact Evaluation’s fault. The world changed.

Methodologies like RCTs came from medical science, where you can give patients a pill and assess its impact with randomized trials. However, development is not a space where one pill will work for everyone. In development, the patients change faster, the illness evolves faster, and the pill needs to keep pace with both the patients and the illness. That’s where Impact Management comes in.

What Is Impact Management?

New Philanthropy Capital‘s 2017 Global Innovation in Measurement and Evaluation Report counts Impact Management as one of the top 7 innovations of 2017.

So what is Impact Management? Let me first explain what it is not. It’s not a one-time evaluation. It’s not collecting data for answering a limited set of questions. It’s not a separate activity from your program. It’s not just monitoring and evaluation.

It’s a way of making data-driven decisions at every step of your program. It’s about keeping a pulse on your program every day and finding new questions to answer, rather than just focusing on specific questions predetermined by your monitoring and evaluation team or funders.

“The question that’s being asked more and more is, ‘How does evaluation feed into better management decisions?’ That’s a shift from measurement of impact, to measurement for impact.”
– Megan Campbell (Feedback Labs)

How Does Impact Management Work?

Impact Management uses the basic components of monitoring and evaluation, but with an outlook shift. It involves frequent data collection, regular reporting and monitoring of your data, and iteratively updating your program indicators and metrics as data comes in and the program changes.

Impact Management differs from Impact Assessment in that it promotes course correction on a daily basis. Organizations collect data on their programs as they conduct activities, analyze that information on a regular basis, and make changes to the program.

With an outlook that encourages frequent changes, as if you were trading in stocks, organizations will have the ability to A/B test their programs with real-time data to make decisions immediately; rather than wait to compare and contrast two different surveys. They can test out new things and make changes as they receive data in servers, even at the end of the day rather than waiting for the official year-end review. It becomes a way of deciding how they should execute a program daily rather than only seeing strategic changes through.

“[Data collection] should be ongoing — it’s a value driver not a compliance requirement.”
– Tom Adams (Acumen)

In many ways, this is how decisions are made on Wall Street or Dalal Street in India. Analysts don’t wait until the end of the year to make investments by reviewing annual reports. They watch daily as the market fluctuates and strike as soon as they see new potential.

Impact Management works exactly the same. You should strive to increase your impact as soon as opportunity arrives, rather than waiting for a year-end external evaluation or approval.

How Can You Implement Impact Management?

To make Impact Management possible, switch from static data files to a flexible data system.

Today, most of your program officers and even your beneficiaries are armed with mini-computers in their pockets (read: smartphones). Leverage these to create a network of data ingestion devices, continuously tracking and measuring the impact of your programs. Use mobile data collection apps to add forms, deploy them to the field, and reach out not just to your field force but also your beneficiaries — not just at the end of the month or quarter, but as frequently as possible.

Then don’t let this data sit in Excel files. Use today’s technologies to create your own data management system, one that will link your beneficiaries, connect your programs, and answer queries. Have someone with an analytical bent look at this data regularly, or draw on machine power to analyze this data and generate meaningful insights or reports in real time.

“We’re moving away from a static data world, where you work on datasets, and you write reports, to a dynamic data world where data is always being generated and created and it helps you do your job better.”
– Andrew Means (beyond.uptake)

Lastly, it’s crucial to tie this flexible data system back to your decisions. Make real-time data — rather than guesses or last year’s data — the basis of every program decision and the foundation of even weekly catch-ups. And don’t hesitate to test out new things. Data will tell you whether something worked or not.

Many of our partners are using our platform to make Impact Management possible and track their programs in real time. The platform lets them create and tweak data collection forms, and monitor incoming data in real time on their computer, in regular reports, or even on map-based dashboards. They are asking new questions about how their programs are doing and answering them with data.

If we really want to create the best development programs, we’ll have to think differently and use evidence not just once every month or year, but as we make crucial decisions every day. All backed by the tenets of Impact Management: test, fail, improve, repeat.

Join us at MERL Tech London on March 19-20 – where we’ll be debating this topic!

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. 

 

 

 

Making (some) sense of data storage and presentation in Excel

By Anna Vasylytsya. Anna is in the process of completing her Master’s in Public Policy with an emphasis on research methods. She is excited about the role that data can play in improving people’s lives!

At the MERL Tech Conference, I attended a session called “The 20 skills that solve 80% of M&E problems” presented by Dr. Leslie Sage of DevResults. I was struck by the practical recommendations Leslie shared that can benefit anyone that uses Excel to store and/or present data.

I boiled down the 20 skills presented in the session into three key takeaways, below.

1. Discerning between data storage and data presentation

Data storage and data presentation serve two different functions and never the two shall meet. In other words, data storage is never data presentation.

Proper data storage should not contain merged cells, subheadings, color used to denote information, different data types within cells (numbers and letters), more than one piece of data in a cell (such as disaggregations). Additionally, in proper data storage, columns should be the variables and rows as the observations or vice versa. Poor data storage practices need to be avoided because they mean that you cannot use Excel’s features to present the data.

A common example of poor data storage:

Excel 1

 

One of the reasons that this is not good data storage is because you are not able to manipulate this data using Excel’s features. If you needed this data in a different format or you wanted to visualize it, you would have to do this manually, which would be time consuming.

Here is the same data presented in a “good” storage format:

2Good_Data_Storage

 

Data stored this way may not look as pretty, but it is not meant to be presented or read in within the sheet. This is an example of good data storage because each unique observation gets a new row in the spreadsheet. When you properly store data, it is easy for Excel to aggregate the data and summarize it in a pivot table, for example.

2. Use Excel’s features to organize and clean data

You do not have to use precious time to organize or clean data manually. Here are a few recommendations on Excel’s data organization and cleaning features:

  • To join to cells that have text into one cell, use the concatenate function.
  • To split text from one cell into different cells, use the text to columns
  • To clean text data, use Excel’s functions: trim, lower, upper, proper, right, left, and len.
  • To move data from rows into columns or columns into rows, use Excel’s transpose feature.
  • There is a feature to remove duplicates from the data.
  • Create a macro to automate simple repetitive steps in Excel.
  • Insert data validation in an excel spreadsheet if you are sending a data spreadsheet to implementers or partners to fill out.
    • This restricts the type of data or values that can be entered in certain parts of the spreadsheet.
    • It also saves you time from having to clean the data after you receive it.
  • Use the vlookup function in Excel in your offline version to look up a Unique ID
    • Funders or donors normally require that data is anonymized if it is made public. While not the best option for anonymizing data, you can use Excel if you haven’t been provided with specific tools or processes.
    • You can create an “online” anonymized version that contains a Unique ID and an “offline version” (not public) containing the ID and Personally Identifiable Information (PII). Then, if you needed to answer a question about a Unique ID, for example, your survey was missing data and you needed to go back and collect it, you can use vlookup to find a particular record.

3. Use Excel’s features to visualize data

One of the reasons to organize data properly so that you can use Excel’s Pivot Table feature.

Here is an example of a pivot table made from the data in the good data storage example above (which took about a minute to make):

3Pivot_Table

Using the pivot table, you can then use Excel’s Create a Chart Feature to quickly make a bar graph:

4BarGraph

In the Future

I have fallen prey to poor data storage practices in the past. Now that I have learned these best practices and features of Excel, I know I will improve my data storage and presentation practices. Also, now that I have shared them with you; I hope that you will too!

Please note that in this post I did not discuss how Excel’s functions or features work or how to use them. There are plenty of resources online to help you discover and explore them. Some helpful links have been included as a start. Additionally, the data presented here are fictional and created purely for demonstration purposes.