Guest post from Jo Kaybryn, an international development consultant currently directing evaluation frameworks, evaluation quality assurance services, and leading evaluations for UN agencies and INGOs.
“Upping the Ex Ante” is a series of articles aimed at evaluators in international development exploring how our work is affected by – and affects – digital data and technology. I’ve been having lots of exciting conversations with people from all corners of the universe about our brave new world. But I’ve also been conscious that for those who have not engaged a lot with the rapid changes in technologies around us, it can be a bit daunting to know where to start. These articles explore a range of technologies and innovations against the backdrop of international development and the particular context of evaluation. For readers not yet well versed in technology there are lots of sources to do further research on areas of interest.
series is half way through, with 4 articles published.
in Part 1 the series has gone back to the olden days (1948!) to consider the
origin story of cybernetics and the influences that are present right now in
algorithms and big data. The philosophical and ethical dilemmas are a recurring
theme in later articles.
examines the problems of distance which is something that technology offers
huge strides forwards in, and yet it remains never fully solved, with a
discussion on what blockchains mean for the veracity of data.
considers qualitative data and shines a light on the gulf between our digital
data-centric and analogue-centric worlds and the need for data scientists and social
scientists to cooperate to make sense of it.
looks at quantitative data and the implications for better decision making, why
evaluators really don’t like an algorithmic “black box”; and reflections on how humans’
assumptions and biases leak into our technologies whether digital or analogue.
few articles will see a focus on ethics, psychology and bias; a case study on a
hypothetical machine learning intervention to identify children at risk of
maltreatment (lots more risk and ethical considerations), and some thoughts about putting it all
in perspective (i.e. Don’t
Enabling trust in an efficient manner is the primary innovation that the blockchain delivers through the use of cryptology and consensus algorithms. Trust is usually a painstaking relationship building effort that requires iterative interactions to build. The blockchain alleviates the need for much of the resources required to build this trust, but that does not mean that stakeholders will automatically trust the blockchain application. There will still need to be trust building mechanisms with any blockchain application and MEL practitioners are uniquely situated to inform how these trust relationships can mature.
Function of trust in the blockchain
Trust is expensive. You pay fees to banks who provide confidence to sellers who take your debit card as payment and trust that they will receive funds for the transaction. Agriculture buyers pay fees to third parties (who can certify that the produce is organic, etc.) to validate quality control on products coming through the value chain Often sellers do not see the money from debit card transaction in their accounts automatically and agriculture actors perpetually face the pressures resulting from being paid for goods and/or services they provided weeks previously. The blockchain could alleviate much of these harmful effects by substituting trust in humans by trust in math.
We pay these third parties because they are trusted agents, and these trusted agents can be destructive rent seekers at times; creating profits that do not add value to the goods and services they work with. End users in these transactions are used to using standard payment services for utility bills, school fees, etc. This history of iterative transactions has resulted in a level of trust in these processes. It may not be equitable but it is what many are used to and introducing an innovation like blockchain will require an understanding of how these processes are influencing stakeholders, their needs and how they might be nudged to trust something different like a blockchain application.
How MEL can help understand and build trust
Just as microfinance introduced new methods of sending/receiving money and access to new financial services that required piloting different possible solutions to build this understanding, so will blockchain applications. This is an area where MEL can add value to achieving mass impact, by designing the methods to iteratively build this understanding and test solutions.
MEL has done this before. Any project that requires relationship building should be based on understanding the mindset and incentives for relevant actions (behavior) amongst stakeholders to inform the design of the “nudge” (the treatment) intended to shift behavior.
Many of the programs we work on as MEL practitioners involve various forms and levels of relationship building, which is essentially “trust”. There have been many evaluations of relationship building whether it be in microfinance, agriculture value chains or policy reform. In each case, “trust” must be defined as a behavior change outcome that is “nudged” based on the framing (mindset) of the stakeholder. Meaning that each stakeholder, depending on their mindset and the required behavior to facilitate blockchain uptake, will require a customized nudge.
The role of trust in project selection and design: What does that mean for MEL
Defining “trust” should begin during project selection/design. Project selection and design criteria/due diligence are invaluable for MEL. Many of the dimensions of evaluability assessments refer back to the work that is done in the project selection/design phrase (which is why some argue evaluability assessments are essentially project design tools). When it comes to blockchain, the USAID Blockchain Primer provides some of the earliest thinking for how to select and design blockchain projects, hence it is a valuable resources for MEL practitioners who want to start thinking about how they will evaluate blockchain applications.
What should we be thinking about?
Relationship building and trust are behaviors, hence blockchain theories of change should have outcomes stated as behavior changes by specific stakeholders (hence the value add of tools like stakeholder analysis and outcome mapping). However, these Theories of Change (TOC) are only as good as what informs them, hence building a knowledge base of blockchain applications as well as previous lessons learned from evidence on relationship building/trust will be critical to developing a MEL Strategy for blockchain applications.
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 email@example.com or through the Emergence website.
Technology solutions in development contexts can be runaway trains of optimistic thinking. Remember the play pump, a low technology solution meant to provide communities with clean water as children play? Or the Soccket, the soccer ball that was going to help kids learn to read at night? I am not disparaging these good intentions, but the need to learn the evidence from past failure is widely recognized. When it comes to the blockchain, possibly the biggest technological innovation on the social horizon, the learning captured in guidance like the Principles for Digital Development or Blockchain Ethical Design Frameworks, needs to not only be integrated into the design of blockchain applications but also into how MEL practitioners will need to assess this integration and test solutions. Data driven feedback from MEL will help inform the maturation of human centered blockchain solutions that mitigate endless/pointless pilots which exhaust the political will of good natured partners and creates barriers to sustainable impact.
The Blockchain is new but we have a head start in thinking about it
The blockchain is an innovation, and it should be evaluated as such. True the blockchain could be revolutionary in its impact. And yes this potential could grease the wheels of the runaway train thinking referenced above, but this potential does not moot the evidence we have around evaluating innovations.
Keeping the risk of the runaway train at bay includes MERL practitioners working with stakeholders to ask : is blockchain the right approach for this at all? Only after determining the competitive advantage of the blockchain solutions over other possible solutions should MEL practitioners work with stakeholders to finalize design of the initial piloting. The USAID Blockchain Primer is the best early thinking about this process and the criteria involved.
Michael Quinn Patton and others have developed an expanded toolkit for MERL practitioners to best unpack the complexity of a project and design a MERL framework that responds to the decision making requirements on the scale up pathway. Because the blockchain is an innovation, which by definition means there is less evidence on its application but great potential, it will require MEL frameworks that iteratively test and modify applications to inform the scale up pathway.
The Principles for Digital Development highlight the need for iterative learning in technology driven solutions. The overlapping regulatory, organizational and technological spheres further assist in unpacking the complexity using tools like Problem Driven Iterative Adaptation (PDIA) or other adaptive management frameworks that are well suited to testing innovations in each sphere.
How Blockchain is different: Intended Impacts and Potential Spoilers
There will be intended and unintended outcomes from blockchain applications that MEL should account for. This includes general intended outcomes of increased access to services and overall costs savings while “un-intended” outcomes include the creation of winners and losers.
The primary intended outcomes that could be expected from blockchain applications are an increase in cost savings (by cutting out intermediaries) which results in increased access to whatever service/product (assuming any cost savings are re-invested in expanding access). Or a possible increase in access that results from creating a service where none existed before (for example creating access to banking services in rural populations). Hence methods for measuring the specific type of cost savings and increased access that are already used could be applied with modification.
However, the blockchain will be disruptive and when I say “un-intended” (using quotation marks) I do so because the cost savings from blockchain applications are the result of alleviating the need for some intermediaries or middlemen. These middlemen are third parties who could be some form of rent-seeker in providing a validation, accreditation, certification or other type of service meant to communicate trust. For example, with m-Pesa, banking loan and other services from banks were expanded to new populations. With a financial inclusion blockchain project these same services could be accessed by the same population but without the need for a bank, hence incurring a cost savings. However, as is well known in many a policy reform intervention, creating efficiencies usually means creating losers and in our example the losers are those previously offering the services that the blockchain makes more efficient.
The blockchain can facilitate efficiencies, not elimination of all intermediary functions. With the introduction of any innovation, the need for new functions will emerge as old functions are mooted. For example mPesa experienced substantial barriers in its early development until they began working with kiosk owners who, after being trained up, could demonstrate and explain mPesa to customers. Hence careful iterative assessment of the ecosystem (similar to value chain mapping) to identify mooted functions (losers) and new functions (winners) is critical.
MERL practitioners have a value add in mitigating the negative effects from the creation of losers, who could become spoilers. MERL practitioners have many analytical tools/skills that can not only help in identifying the potential spoilers (perhaps through various outcome mapping and stakeholder analysis tools) but also in mitigating any negative effects (creating user personas of potential spoilers to better assess how to incentivize targeted behavior changes). Hence MEL might be uniquely placed to build a broader understanding amongst stakeholders on what the blockchain is, what it can offer and how to create a learning framework that builds trust in the solution.
Trust, the real innovation of blockchain
MERL is all about behavior change, because no matter the technology or process innovation, it requires uptake and uptake requires behavior. Trust is a behavior, you trust that when you put your money in a bank it will be available for when you want to use it. Without this behavior, stemming from a belief, there are runs on banks which in turn fail which further erodes trust in the banking system. The same could be said for paying money to a water or power utility and expecting that they will provide service, The more use, the more a relationship matures into a trustful one. But it does not take much to erode this trust even after the relationship is established, again think about how easy it is to cause a run on a bank or stop using a service provider.
The real innovation of the blockchain is that it replaces the need for trust in humans (whether it is an individual or system of organizations) with trust in math. Just as any entity needs to build a relationship of trust with its targeted patrons, so will the blockchain have to develop a relationship of trust not only with end users but with those within the ecosystem that could influence the impact of the blockchain solution to include beneficiaries and potential loser/spoilers. This brings us back to the importance of understanding who these stakeholders are, how they will interact with and influence the blockchain, and their perspectives, needs and capacities.
MERL practitioners who wish to use blockchain will need to pick up the latest thinking in behavioral sciences to understand this “trust” factor for each stakeholder and integrate it into an adaptive management framework. The next blog in this series will go into further detail about the role of “trust” when evaluating a blockchain application.
The Blockchain is different — don’t throw the baby out with the bath water
There will inevitably be mountains of pressure go to “full steam ahead” (part of me wants to add “and damn the consequences”) without sufficient data driven due diligence and ethical review, since blockchain is the next new shiny thing. MERL practitioners should not only be aware of this unfortunate certainty, but they also need to pro-actively consider their own informed strategy on how they will respond to this pressure. MERL practitioners are uniquely positioned to advocate for data driven decision making and provide the data necessary to steer clear of misapplication of blockchain solutions. There are already great resources for MEL practitioners on the ethical criteria and design implications for blockchain solutions.
The potential impact of blockchain is still unknown but if current thinking is to be believed, the impact could be paradigm shifting. Given this potential, getting the initial testing right to maximize learning will be critical to cultivating the political will, the buy-in, and the knowledge base to kick start something much bigger.
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 firstname.lastname@example.org or through the Emergence website.
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.
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:
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.
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.
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 email@example.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
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.
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.
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.
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?
by Christopher Robert, CEO of Dobility (Survey CTO). This post was originally published on March 15, 2018, on the Survey CTO blog.
Needs, markets, and innovation combine to produce technological change. This is as true in the international development sector as it is anywhere else. And within that sector, it’s as true in the broad category of MERL (monitoring and evaluation, research, and learning) technologies as it is in the narrower sub-category of digital data collection technologies. Here, I’ll consider the recent history of digital data collection technology as an example of MERL technology maturation – and as an example, more broadly, of the importance of market structure in shaping the evolution of a technology.
My basic observation is that, as digital data collection technology has matured, the same stakeholders have been involved – but the market structure has changed their relative power and influence over time. And it has been these very changes in power and influence that have changed the cost and nature of the technology itself.
First, when it comes to digital data collection in the development context, who are the stakeholders?
Donors. These are the primary actors who fund development work, evaluation of development policies and programs, and related research. There are mega-actors like USAID, Gates, and the UN agencies, but also many other charities, philanthropies, and public or nonprofit actors, from Catholic Charities to the U.S. Centers for Disease Control and Prevention.
Developers. These are the designers and software engineers involved in producing technology in the space. Some are students or university faculty, some are consultants, many work full-time for nonprofits or businesses in the space. (While some work on open-source initiatives in a voluntary capacity, that seems quite uncommon in practice. The vast majority of developers working on open-source projects in the space get paid for that work.)
Consultants and consulting agencies.These are the technologists and other specialists who help research and program teams use technology in the space. For example, they might help to set up servers and program digital survey instruments.
Researchers. These are the folks who do the more rigorous research or impact evaluations, generally applying social-science training in public health, economics, agriculture, or other related fields.
M&E professionals.These are the people responsible for program monitoring and evaluation. They are most often part of an implementing program team, but it’s also not uncommon to share more centralized (and specialized) M&E teams across programs or conduct outside evaluations that more fully separate some M&E activities from the implementing program team.
IT professionals.These are the people responsible for information technology within those organizations implementing international development programs and/or carrying out MERL activities.
Program beneficiaries. These are the end beneficiaries meant to be aided by international development policies and programs. The vast majority of MERL activities are ultimately concerned with learning about these beneficiaries.
These different stakeholders have different needs and preferences, and the market for digital data collection technologies has changed over time – privileging different stakeholders in different ways. Two distinct stages seem clear, and a third is coming into focus:
The early days of donor-driven pilots and open source. These were the days of one-offs, building-your-own, and “pilotitis,” where donors and developers were effectively in charge and there was a costly additional layer of technical consultants between the donors/developers and the researchers and M&E professionals who had actual needs in the field. Costs were high, and some combination of donor and developer preferences reigned supreme.
Intensifying competition in program-adopted professional products.Over time, professional products emerged that began to directly market to – and serve – researchers and M&E professionals. Costs fell with economies of scale, and the preferences of actual users in the field suddenly started to matter in a more direct, tangible, and meaningful way.
Intensifying competition in IT-adopted professional products.Now that use of affordable, accessible, and effective data-collection technology has become ubiquitous, it’s natural for IT organizations to begin viewing it as a kind of core organizational infrastructure, to be adopted, supported, and managed by IT. This means that IT’s particular preferences and needs – like scale, standardization, integration, and compliance – start to become more central, and costs unfortunately rise.
While I still consider us to be in the glory days of the middle stage, where costs are low and end-users matter most, there are still plenty of projects and organizations living in that first stage of more costly pilots, open source projects, and one-offs. And I think that the writing’s very much on the wall when it comes to our progression toward the third stage, where IT comes to drive the space, innovation slows, and end-user needs are no longer dominant.
Full disclosure: I myself have long been a proponent of the middle phase, and I am proud that my social enterprise has been able to help graduate thousands of users from that costly first phase. So my enthusiasm for the middle phase began many years ago and in fact helped to launch Dobility.
THE EARLY DAYS OF DONOR-DRIVEN PILOTS AND OPEN SOURCE
In the beginning, there were pioneering developers, patient donors, and program or research teams all willing to take risks and invest in a better way to collect data from the field. They took cutting-edge technologies and found ways to fit them into some of the world’s most difficult, least-cutting-edge settings.
In these early days, it mattered a lot what could excite donors enough to open their checkbooks – and what would keep them excited enough to keep the checks coming. So the vital need for large and ongoing capital injections gave donors a lot of influence over what got done.
Developers also had a lot of sway. Donors couldn’t do anything without them, and they also didn’t really know how to actively manage them. If a developer said “no, that would be too hard or expensive” or even “that wouldn’t work,” what could the donor really say or do? They could cut off funding, but that kind of leverage only worked for the big stuff, the major milestones and the primary objectives. For that stuff, donors were definitely in charge. But for the hundreds or thousands of day-to-day decisions that go into any technology solution, it was the developers effectively in charge.
Actual end-users in the field – the researchers and M&E professionals who were piloting or even trying to use these solutions – might have had some solid ideas about how to guide the technology development, but they had essentially no levers of control. In practice, the solutions being built by the developers were often so technically-complex to configure and use that there was an additional layer of consultants (technical specialists) sitting between the developers and the end-users. But even if there wasn’t, the developers’ inevitable “no, sorry, that’s not feasible,” “we can’t realistically fit that into this release,” or simple silence was typically the end of the story for users in the field. What could they do?
Unfortunately, without meaning any harm, most developers react by pushing back on whatever is contrary to their own preferences (I say this as a lifelong developer myself). Something might seem like a hassle, or architecturally unclean, and so a developer will push back, say it’s a bad idea, drag their heels, even play out the clock. In the past five years of Dobility, there have been hundreds of cases where a developer has said something to the effect of “no, that’s too hard” or “that’s a bad idea” to things that have turned out to (a) take as little as an hour to actually complete and (b) provide massive amounts of benefit to end-users. There’s absolutely no malice involved, it’s just the way most of them/us are.
This stage lasted a long time – too long, in my view! – and an entire industry of technical consultants and paid open-source contributors grew up around an approach to digital data collection that didn’t quite embrace economies of scale and never quite privileged the needs or preferences of actual users in the field. Costs were high and complaints about “pilotitis” grew louder.
INTENSIFYING COMPETITION IN PROGRAM-ADOPTED PROFESSIONAL PRODUCTS
But ultimately, the protagonists of the early days succeeded in establishing and honing the core technologies, and in the process they helped to reveal just how much was common across projects of different kinds, even across sectors. Some of those protagonists also had the foresight and courage to release their technologies with the kinds of permissive open-source licenses that would allow professionalization and experimentation in service and support models. A new breed of professional products directly serving research, program, and M&E teams was born – in no small part out of a single, tremendously-successful open-source project, Open Data Kit (ODK).
These products tended to be sold directly to end-users, and were increasingly intended for those end-users to be able to use themselves, without the help of technical staff or consultants. For traditionalists of the first stage, this was a kind of heresy: it was considered gauche at best and morally wrong at worst to charge money for technology, and it was seen as some combination of impossible and naive to think that end-users could effectively deploy and manage these technologies without technical assistance.
In fact, the new class of professional products were not designed to be used entirely without assistance. But they were designed to require as little assistance as possible, and the assistance came with the product instead of being provided by a separate (and separately-compensated) internal or external team.
A particularly successful breed of products came to use a “Software as a Service” (SaaS) model that streamlined both product delivery and support, ramping up economies of scale and driving down costs in the process (like SurveyCTO). When such products offered technical support free-of-charge as part of the purchase or subscription price, there was a built-in incentive to improve the product: since tech support was so costly to deliver, improving the product such that it required less support became one of the strongest incentives driving product development. Those who adopted the SaaS model not only had to earn every dollar of revenue from end-users, but they had to keep earning that revenue month in, month out, year in, year out, in order to retain business and therefore the revenue needed to pay the bills. (Read about other SaaS benefits for M&E in this recent DevResults post.)
It would be difficult to overstate the importance of these incentives to improve the product and earn revenue from end-users. They are nothing short of transformative. Particularly once there is active competition among vendors, users are squarely in charge. They control the money, their decisions make or break vendors, and so their preferences and needs are finally at the center.
Now, in addition to the “it’s heresy to charge money or think that end-users can wield this kind of technology” complaints that used to be more common, there started to be a different kind of complaint: there are too many solutions! It’s too overwhelming, how many digital data collection solutions there are now. Some go so far as to decry the duplication of effort, to claim that the free market is inefficient or failing; they suggest that donors, consultants, or experts be put back in charge of resource allocation, to re-impose some semblance of sanity to the space.
But meanwhile, we’ve experienced a kind of golden age in terms of who can afford digital data collection technology, who can wield it effectively, and in what kinds of settings. There are a dizzying number of solutions – but most of them cater to a particular type of need, or have optimized their business model in a particular sort of way. Some, like us, rely nearly 100% on subscription revenues, others fund themselves more primarily from service provision, others are trying interesting ways to cross-subsidize from bigger, richer users so that they can offer free or low-cost options to smaller, poorer ones. We’ve overcome pilotitis, economies of scale are finally kicking in, and I think that the social benefits have been tremendous.
INTENSIFYING COMPETITION IN IT-ADOPTED PROFESSIONAL PRODUCTS
It was the success of the first stage that laid the foundation for the second stage, and so too it has been the success of the second stage that has laid the foundation for the third: precisely because digital data collection technology has become so affordable, accessible, and ubiquitous, organizations are increasingly thinking that it should be IT departments that procure and manage that technology.
Part of the motivation is the very proliferation of options that I mentioned above. While economics and the historical success of capitalism has taught us that a marketplace thriving with competition is most often a very good thing, it’s less clear that a wide variety of options is good within any single organization. At the very least, there are very good reasons to want to standardize some software and processes, so that different people and teams can more effortlessly share knowledge and collaborate, and so that there can be some economies of scale in training, support, and compliance.
Imagine if every team used its own product and file format for writing documents, for example. It would be a total disaster! The frictions across and between teams would be enormous. And as data becomes more and more core to the operations of more organizations – the way that digital documents became core many years ago – it makes sense to want to standardize and scale data systems, to streamline integrations, just for efficiency purposes.
Growing compliance needs only up the ante. The arrival of the EU’s General Data Protection Regulation (GDPR) this year, for example, raises the stakes for EU-based (or even EU-touching) organizations considerably, imposing stiff new data privacy requirements and steep penalties for violations. Coming into compliance with GDPR and other data-security regulations will be effectively impossible if IT can’t play a more active role in the procurement, configuration, and ongoing management of data systems; and it will be impractical for IT to play such a role for a vast array of constantly-shifting technologies. After all, IT will require some degree of stability and scale.
But if IT takes over digital data collection technology, what changes? Does the golden age come to an end?
Potentially. And there are certainly very good reasons to worry.
First, changing who controls the dollars – who’s in charge of procurement – threatens to entirely up-end the current regime, where end-users are directly in charge and their needs and preferences are catered to by a growing body of vendors eager to earn their business.
It starts with the procurement process itself. When IT is in charge, procurement processes are long, intensive, and tend to result in a “winner take all” contract. After all, it makes sense that IT departments would want to take their time and choose carefully; they tend to be choosing solutions for the organization as a whole (or at least for some large class of users within the organization), and they most often intend to choose a solution, invest heavily in it, and have it work for as long as possible.
This very natural and appropriate method that IT uses to procure is radically different from the method used by research, program, and M&E teams. And it creates a radically different dynamic for vendors.
Vendors first have to buy into the idea of investing heavily in these procurement processes – which some may simply choose not to do. Then they have to ask themselves, “what do these IT folks care most about?” In order to win these procurements, they need to understand the core concerns driving the purchasing decision. As in the old saying “nobody ever got fired for choosing IBM,” safety, stability, and reputation are likely to be very important. Compliance issues are likely to matter a lot too, including the vendor’s established ability to meet new and evolving standards. Integrations with corporate systems are likely to count for a lot too (e.g., integrating with internal data and identity-management systems).
Does it still matter how well the vendor meets the needs of end-users within the organization? Of course. But note the very important shift in the dynamic: vendors now have to get the IT folks to “yes” and so would be quite right to prioritize meeting their particular needs. Nobody will disagree that end-users ultimately matter, but meanwhile the focus will be on the decision-makers. The vendors that meet the decision-makers’ needs will live, the others will die. That’s simply one aspect of how a free market works.
Note also the subtle change in dynamic once a vendor wins a contract: the SaaS model where vendors had to re-earn every customer’s revenue month in, month out, is largely gone now. Even if the contract is formally structured as a subscription or has lots of exit options, the IT model for technology adoption is inherently stickier. There is a lot more lock-in in practice. Solutions are adopted, they’re invested in at large scale, and nobody wants to walk away from that investment. Innovation can easily slow, and nobody wants to repeat the pain of procurement and adoption in order to switch solutions.
And speaking of the pain of the procurement process: costs have been rising. After all, the procurement process itself is extremely costly to the vendor – especially when it loses, but even when it wins. So that’s got to get priced in somewhere. And then all of the compliance requirements, all of the integrations with corporate systems, all of that stuff’s really expensive too. What had been an inexpensive, flexible, off-the-shelf product can easily become far more expensive and far less flexible as it works itself through IT and compliance processes.
What had started out on a very positive note (“let’s standardize and scale, and comply with evolving data regulations”) has turned in a decidedly dystopian direction. It’s sounding pretty bad now, and you wouldn’t be wrong to think “wait, is this why a bunch of the products I use for work are so much more frustrating than the products I use as a consumer?” or “if Microsoft had to re-earn every user’s revenue for Excel, every month, how much better would it be?”
While I don’t think there’s anything wrong with the instinct for IT to take increasing control over digital data collection technologies, I do think that there’s plenty of reason to worry. There’s considerable risk that we lose the deep user orientation that has just been picking up momentum in the space.
WHERE WE’RE HEADED: STRIKING A BALANCE
If we don’t want to lose the benefits of a deep user orientation in this particular technology space, we will need to work pretty hard – and be awfully clever – to avoid it. People will say “oh, but IT just needs to consult research, program, and M&E teams, include them in the process,” but that’s hogwash. Or rather, it’s woefully inadequate. The natural power of those controlling resources to bend the world to their preferences and needs is just too powerful for mere consultation or inclusion to overcome.
And the thing is: what IT wants and needs is good. So the solution isn’t just “let’s not let them anywhere near this, let’s keep the end-users in charge.” No, that approach collapses under its own weight eventually, and certainly it can’t meet rising compliance requirements. It has its own weaknesses and inefficiencies.
What we need is an approach – a market structure – that allows the needs of IT and the needs of end-users both to matter to appropriate degrees.
With SurveyCTO, we’re currently in an interesting place: we’re becoming split between serving end-users and serving IT organizations. And I suppose as long as we’re split, with large parts of our revenue coming from each type of decision-maker, we remain incentivized to keep meeting everybody’s needs. But I see trouble on the horizon: the IT organizations can pay more, and more organizations are shifting in that direction… so once a large-enough proportion of our revenue starts coming from big, winner-take-all IT contracts, I fear that our incentives will be forever changed. In the language of economics, I think that we’re currently living in an unstable equilibrium. And I really want the next equilibrium to serve end-users as well as the last one!
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.
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.
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.
Efficiency – Measure of the outputs (qualitative and quantitative) in relation to the inputs.
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).
Sustainability – Measure of whether the benefits of a technology tool or platform are likely to continue after donor funding has been withdrawn.
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.
by Daniel Ramirez-Raftree, MERL Tech support team.
Evolving data collection methods call for evolving quality assurance methods. In their session titled Data Quality in the Age of Lean Data, Sam Schueth of Intermedia, Woubedle Alemayehu of Oxford Policy Management, Julie Peachey of the Progress out of Poverty Index, and Christina Villella of MEASURE Evaluation discussed problems, solutions, and ethics related to digital data collection methods. [Bios and background materials here]
Sam opened the conversation by comparing the quality assurance and control challenges in paper assisted personal interviewing (PAPI) to those in digital assisted personal interviewing (DAPI). Across both methods, the fundamental problem is that the data that is delivered is a black box. It comes in, it’s turned into numbers and it’s disseminated, but in this process alone there is no easily apparent information about what actually happened on the ground.
During the age of PAPI, this was dealt with by sending independent quality control teams to the field to review the paper questionnaire that was administered and perform spot checks by visiting random homes to validate data accuracy. Under DAPI, the quality control process becomes remote. Survey administrators can now schedule survey sessions to be recorded automatically and without the interviewer’s knowledge, thus effectively gathering a random sample of interviews that can give them a sense of how well the sessions were conducted. Additionally, it is now possible to use GPS to track the interviewers’ movements and verify the range of households visited. The key point here is that with some creativity, new technological capacities can be used to ensure higher data quality.
Woubedle presented next and elaborated on the theme of quality control for DAPI. She brought up the point that data quality checks can be automated, but that this requires pre-survey-implementation decisions about what indicators to monitor and how to manage the data. The amount of work that is put into programming this upfront design has a direct relationship on the ultimate data quality.
One useful tool is a progress indicator. Here, one collects information on trends such as the number of surveys attempted compared to those completed. Processing this data could lead to further questions about whether there is a pattern in the populations that did or did not complete the survey, thus alerting researchers to potential bias. Additionally, one can calculate the average time taken to complete a survey and use it to identify outliers that took too little or too long to finish. Another good practice is to embed consistency checks in the survey itself; for example, making certain questions required or including two questions that, if answered in a particular way, would be logically contradictory, thus signaling a problem in either the question design or the survey responses. One more practice could be to apply constraints to the survey, depending on the households one is working with.
After this discussion, Julie spoke about research that was done to assess the quality of different methods for measuring the Progress out of Poverty Index (PPI). She began by explaining that the PPI is a household level poverty measurement tool unique to each country. To create it, the answers to 10 questions about a household’s characteristics and asset ownership are scored to compute the likelihood that the household is living below the poverty line. It is a simple, yet effective method to evaluate household level poverty. The research project Julie described set out to determine if the process of collecting data to create the PPI could be made less expensive by using SMS, IVR or phone calls.
Grameen Foundation conducted the study and tested four survey methods for gathering data: 1) in-person and at home, 2) in-person and away from home, 3) in-person and over the phone, and 4) automated and over the phone. Further, it randomized key aspects of the study, including the interview method and the enumerator.
Ultimately, Grameen Foundation determined that the interview method does affect completion rates, responses to questions, and the resulting estimated poverty rates. However, the differences in estimated poverty rates was likely not due to the method itself, but rather to completion rates (which were affected by the method). Thus, as long as completion rates don’t differ significantly, neither will the results. Given that the in-person at home and in-person away from home surveys had similar completion rates (84% and 91% respectively), either could be feasibly used with little deviation in output. On the other hand, in-person over the phone surveys had a 60% completion rate and automated over the phone surveys had a 12% completion rate, making both methods fairly problematic. And with this understanding, developers of the PPI have an evidence-based sense of the quality of their data.
This case study illustrates the the possibility of testing data quality before any changes are made to collection methods, which is a powerful strategy for minimizing the use of low quality data.
Christina closed the session with a presentation on ethics in data collection. She spoke about digital health data ethics in particular, which is the intersection of public health ethics, clinical ethics, and information systems security. She grounded her discussion in MEASURE Evaluation’s experience thinking through ethical problems, which include: the vulnerability of devices where data is collected and stored, the privacy and confidentiality of the data on these devices, the effect of interoperability on privacy, data loss if the device is damaged, and the possibility of wastefully collecting unnecessary data.
To explore these issues, MEASURE conducted a landscape assessment in Kenya and Tanzania and analyzed peer reviewed research to identify key themes for ethics. Five themes emerged: 1) legal frameworks and the need for laws, 2) institutional structures to oversee implementation and enforcement, 3) information systems security knowledge (especially for countries that may not have the expertise), 4) knowledge of the context and users (are clients comfortable with their data being used?), and 5) incorporating tools and standard operating procedures.
Based in this framework, MEASURE has made progress towards rolling out tools that can help institute a stronger ethics infrastructure. They’ve been developing guidelines that countries can use to develop policies, building health informatic capacity through a university course, and working with countries to strengthen their health information systems governance structures.
Finally, Christina explained her take on how ethics are related to data quality. In her view, it comes down to trust. If a device is lost, this may lead to incomplete data. If the clients are mistrustful, this could lead to inaccurate data. If a health worker is unable to check or clean data, this could create a lack of confidence. Each of these risks can lead to the erosion of data integrity.
Participants 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.
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
Increase data literacy across the sector, at individual level and within the various communities where we are working.
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.
Integrate MERL into our daily operations 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.
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.
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?