Tag Archives: evaluation

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.

You can’t have Aid…without AI: How artificial intelligence may reshape M&E

by Jacob Korenblum, CEO of Souktel Digital Solutions

Photo: wikipedia.org/

Potential—And Risk

The rapid growth of Artificial Intelligence—computers behaving like humans, and performing tasks which people usually carry out—promises to transform everything from car travel to personal finance. But how will it affect the equally vital field of M&E? As evaluators, most of us hate paper-based data collection—and we know that automation can help us process data more efficiently. At the same time, we’re afraid to remove the human element from monitoring and evaluation: What if the machines screw up?

Over the past year, Souktel has worked on three areas of AI-related M&E, to determine where new technology can best support project appraisals. Here are our key takeaways on what works, what doesn’t, and what might be possible down the road.

Natural Language Processing

For anyone who’s sifted through thousands of Excel entries, natural language processing sounds like a silver bullet: This application of AI interprets text responses rapidly, often matching them against existing data sets to find trends. No need for humans to review each entry by hand! But currently, it has two main limitations: First, natural language processing works best for sentences with simple syntax. Throw in more complex phrases, or longer text strings, and the power of AI to grasp open-ended responses goes downhill. Second, natural language processing only works for a limited number of (mostly European) languages—at least for now. English and Spanish AI applications? Yes. Chichewa or Pashto M&E bots? Not yet. Given these constraints, we’ve found that AI apps are strongest at interpreting basic misspelled answer text during mobile data collection campaigns (in languages like English or French). They’re less good at categorizing open-ended responses by qualitative category (positive, negative, neutral). Yet despite these limitations, AI can still help evaluators save time.

Object Differentiation

AI does a decent job of telling objects apart; we’ve leveraged this to build mobile applications which track supply delivery more quickly & cheaply. If a field staff member submits a photo of syringes and a photo of bandages from their mobile, we don’t need a human to check “syringes” and “bandages” off a list of delivered items. The AI-based app will do that automatically—saving huge amounts of time and expense, especially during crisis events. Still, there are limitations here too: While AI apps can distinguish between a needle and a BandAid, they can’t yet tell us whether the needle is broken, or whether the BandAid is the exact same one we shipped. These constraints need to be considered carefully when using AI for inventory monitoring.

Comparative Facial Recognition

This may be the most exciting—and controversial—application of AI. The potential is huge: “Qualitative evaluation” takes on a whole new meaning when facial expressions can be captured by cameras on mobile devices. On a more basic level, we’ve been focusing on solutions for better attendance tracking: AI is fairly good at determining whether the people in a photo at Time A are the same people in a photo at Time B. Snap a group pic at the end of each community meeting or training, and you can track longitudinal participation automatically. Take a photo of a larger crowd, and you can rapidly estimate the number of attendees at an event.

However, AI applications in this field have been notoriously bad at recognizing diversity—possibly because they draw on databases of existing images, and most of those images contain…white men. New MIT research has suggested that “since a majority of the photos used to train [AI applications] contain few minorities, [they] often have trouble picking out those minority faces”. For the communities where many of us work (and come from), that’s a major problem.

Do’s and Don’ts

So, how should M&E experts navigate this imperfect world? Our work has yielded a few “quick wins”—areas where Artificial Intelligence can definitely make our lives easier: Tagging and sorting quantitative data (or basic open-ended text), simple differentiation between images and objects, and broad-based identification of people and groups. These applications, by themselves, can be game-changers for our work as evaluators—despite their drawbacks. And as AI keeps evolving, its relevance to M&E will likely grow as well. We may never reach the era of robot focus group facilitators—but if robo-assistants help us process our focus group data more quickly, we won’t be complaining.

Visualizing what connects us: Social Network Analysis (SNA) in M&E

by Anne Laesecke (IREX) and Danielle de García (Social Impact). This post also appears on the Social Impact blog and  the IREX blog.

SNA, or Social Network Analysis, continues to gain momentum in the M&E space. This year at MERL Tech, we held an SNA 101 session, giving a quick-and-dirty overview of what it is, how it can contribute to M&E, and useful tips and tools for conducting an SNA. If you missed it, here’s what you need to know:

What is SNA?

SNA is a way to analyze social systems through relationships. Analyzing and visualizing networks can reveal critical insights for understanding relationships between organizations, supply chains; social movements; and/or between individuals. It’s a very versatile tool which can be used throughout the program cycle to measure things like trust and social capital, information flows, resources, collaboration, and disease spread, among other things.

SNA uses a different vocabulary than other types of analyses. For example, the relationships we measure are called ties or links, and the entities that make up a network are called nodes or actors. These can be organizations, people, or even whole networks themselves. We can study nodes more closely by looking at their attributes – things that characterize them (like demographic information), and we can learn more about how nodes interact and cluster by studying communities or modalities within networks. Various measures of the roles nodes play in a network, as well as measures that characterize the networks themselves, can reveal a lot about the systems and hidden relationships at play. For example, we can determine who has the most ties with other actors; who is relatively cut off from the network, or who is connected to the most well-connected actors.

Why would you use SNA for M&E?

The term “social network analysis” often triggers associations with social media, but SNA uses data from a variety of platforms (including but not limited to social media!). For instance, SNA can identify key influencers in systems – important for programs that rely on thinking politically. SNA can also be a useful tool in processing big data with applications for cybersecurity as well as creating biological and epidemiological projections. Beyond looking at networks of individuals, SNA can explore relationships with concepts through analysis of qualitative data and concept mapping. It can also look at organizational risks and processes (think about comparing an organizational chart with who people actually go to within an organization for information).

How do you do SNA?

Conducting SNA mostly follows the same procedure as other analysis.

  1. Determine your purpose and questions. What decisions do you need to make based on the data you get? Who is your audience and what do they need to know? Answering these questions can help you decided what you are trying to measure and how.
  2. Collect your data. SNA can incorporate lots of different data forms, including customized surveys and interviews asking actors in your network about the links they have, external data such as census information or other public records to further inform attributes or triangulate your custom data; and mapping of key locations or concepts. One thing to consider while conducting an SNA – data cleaning is usually a heavier lift than for other types of analysis.
  3. Crunch those numbers. SNA uses matrices to calculate various measures – from types of centrality to network density and beyond. Lucky for us, there are plenty of tools that take on both the analysis and visualization portions of SNA. However, another consideration as you analyze your data is that network data is often not generalizable in the same way as some statistical analysis. If you miss a key node in the network, you may miss an entire portion that is only linked through that node.
  4. Visualize the network. Network visualizations are one of the most distinctive features of SNA and can be incredibly useful as tools to engage partners about your findings. There is a wealth of analysis and visualization tools that can help you do this. We created a worksheet that outlines several, but a few of the most popular are UCINet, Gephi, and NodeXL.
  5. Interpret your results. You now have a beautiful graph that shows what nodes are important in your network. So what? How does it relate to your program? Your interpretation should answer the questions around the purpose of your analysis, but beyond interpretation can serve to improve your programming. Often, SNA results can help make projections for program sustainability based on who key players are and who can continue championing work, or projecting where trends seem to be going and anticipating activities around those areas.

Conclusions and resources

We barely scratched the surface of what SNA can do and there are so many more applications! Some great resources to learn more are the SNA TIG of the American Evaluation Association, Stephen Borgatti’s course website on SNA, and a site of his dedicated completely to designing surveys for SNA.

MERL Tech Round Up | November 1, 2017

It’s time for our second MERL Tech Round Up, a monthly compilation of MERL Tech News!

On the MERL Tech Blog:

We’ve been posting session summaries from MERL Tech DC. Here are some posts you may have missed in October:

Stuff we’re reading/watching/bookmarking:

There’s quite a bit to learn both in our “MERL / Tech” sector and in related sectors whose experiences are relatable to MERL Tech. Some thought-provoking pieces here:

Events:

Jobs

Head over to ICT4DJobs for a ton of tech related jobs. Here are some interesting ones for folks in the MERL Tech space:

If you’re not already signed up to the Pelican Initiative: Platform for Evidence-based Learning & Communication for Social Change, we recommend doing that. You will find all kinds of MERL and MERLTech related jobs and MERL-related advice. (Note: the Platform is an extremely active forum, so you may want to adjust your settings to receive weekly compilations).

Tag us on Twitter using #MERLTech if you have resources, events, or other news you’d like us to include here!

Don’t forget to submit your session ideas for MERL Tech London by November 10th!

Big data, big problems, big solutions

by Alvaro Cobo-Santillan, Catholic Relief Services (CRS); Jeff Lundberg, CRS; Paul Perrin, University of Notre Dame; and Gillian Kerr, LogicalOutcomes Canada. 

In the year 2017, with all of us holding a mini-computer at all hours of the day and night, it’s probably not too hard to imagine that “A teenager in Africa today has access to more information than the President of United States had 15 years ago”. So it also stands to reason that the ability to appropriately and ethically grapple with the use of that immense amount information has grown proportionately.

At the September MERL Tech event in Washington D.C. a panel that included folks from University of Notre Dame, Catholic Relief Services, and LogicalOutcomes spoke at length about three angles of this opportunity involving big data.

The Murky Waters of Development Data

What do we mean when we say that the world of development—particularly evaluation—data is murky? A major factor in this sentiment is the ambiguous polarity between research and evaluation data.

  • “Research seeks to prove; evaluation seeks to improve.” – CDC
  • “Research studies involving human subjects require IRB review. Evaluative studies and activities do not.”
Source: Patricia Rogers (2014), Ways of Framing the difference between research and evaluation, Better Evaluation Network.

This has led to debates as to the actual relationship between research and evaluation. Some see them as related, but separate activities, others see evaluation as a subset of research, and still others might posit that research is a specific case of evaluation.

But regardless, though motivations of the two may differ, research and evaluation look the same due to their stakeholders, participants, and methods.

If that statement is true, then we must hold both to similar protections!

What are some ways to make the waters less murky?

  • Deeper commitment to informed consent
  • Reasoned use of identifiers
  • Need to know vs. nice to know
  • Data security and privacy protocols
  • Data use agreements and protocols for outside parties
  • Revisit NGO primary and secondary data IRB requirements

Alright then, what can we practically do within our individual agencies to move the needle on data protection?

  • In short, governance. Responsible data is absolutely a crosscutting responsibility, but can be primarily championed through close partnerships between the M&E and IT Departments
  • Think about ways to increase usage of digital M&E – this can ease the implementation of R&D
  • Can existing agency processes and resources be leveraged?
  • Plan and expect to implement gradual behavior change and capacity building as a pre-requisite for a sustainable implementation of responsible data protections
  • Think in an iterative approach. Gradually introduce guidelines, tools and training materials
  • Plan for business and technical support structures to support protections

Is anyone doing any of the practical things you’ve mentioned?

Yes! Gillian Kerr from LogicalOutcomes spoke about highlights from an M&E system her company is launching to provide examples of the type of privacy and security protections they are doing in practice.

As a basis for the mindset behind their work, she notably presented a pretty fascinating and simple comparison of high risk vs. low risk personal information – year of birth, gender, and 3 digit zip code is unique for .04% of US residents, but if we instead include a 5 digit zip code over 50% of US residents could be uniquely identified. Yikes.

In that vein, they are not collecting names or identification and only year of birth (not month or day) and seek for minimal sensitive data defining data elements by level of risk to the client (i.e. city of residence – low, glucose level – medium, and HIV status – high).

In addition, asking for permission not only in the original agency permission form, but also in each survey. Their technical system maintains two instances – one containing individual level personal information with tight permission even for administrators and another with aggregated data with small cell sizes. Other security measures such as multi-factor authentication, encryption, and critical governance; such as regular audits are also in place.

It goes without saying that we collectively have ethical responsibilities to protect personal information about vulnerable people – here are final takeaways:

  • If you can’t protect sensitive information, don’t collect it.
  • If you can’t keep up with current security practices, outsource your M&E systems to someone who can.
  • Your technology roadmap should aspire to give control of personal information to the people who provide it (a substantial undertaking).
  • In the meantime, be more transparent about how data is being stored and shared
  • Continue the conversation by visiting https://responsibledata.io/blog
Register for MERL Tech London, March 19-20th 2018! Session ideas due November 10th.

MERL Tech and the World of ICT Social Entrepreneurs (WISE)

by Dale Hill, an economist/evaluator with over 35 years experience in development and humanitarian work. Dale led the session on “The growing world of ICT Social Entrepreneurs (WISE): Is social Impact significant?” at MERL Tech DC 2018.

Roger Nathanial Ashby of OpenWise and Christopher Robert of Dobility share experiences at MERL Tech.
Roger Nathanial Ashby of OpenWise and Christopher Robert of Dobility share experiences at MERL Tech.

What happens when evaluators trying to build bridges with new private sector actors meet real social entrepreneurs? A new appreciation for the dynamic “World of ICT Social Entrepreneurs (WISE)” and the challenges they face in marketing, pricing, and financing (not to mention measurement of social impact.)

During this MERL Tech session on WISE, Dale Hill, evaluation consultant, presented grant funded research on measurement of social impact of social entrepreneurship ventures (SEVs) from three perspectives. She then invited five ICT company CEOs to comment.

The three perspectives are:

  • the public: How to hold companies accountable, particularly if they have chosen to be legal or certified “benefit corporations”?
  • the social entrepreneurs, who are plenty occupied trying to reach financial sustainability or profit goals, while also serving the public good; and
  • evaluators, who see the important influence of these new actors, but know their professional tools need adaptation to capture their impact.

Dale’s introduction covered overlapping definitions of various categories of SEVs, including legally defined “benefit corporations”, and “B Corps”, which are intertwined with the options of certification available to social entrepreneurs. The “new middle” of SEVs are on a spectrum between for-profit companies on one end and not-for profit organizations on the other. Various types of funders, including social impact investors, new certification agencies, and monitoring and evaluation (M&E) professionals, are now interested in measuring the growing social impact of these enterprises. A show of hands revealed that representatives of most of these types of actors were present at the session.

The five social entrepreneur panelists all had ICT businesses with global reach, but they varied in legal and certification status and the number of years operating (1 to 11). All aimed to deploy new technologies to non-profit organizations or social sector agencies on high value, low price terms. Some had worked in non-profits in the past and hoped that venture capital rather than grant funding would prove easier to obtain. Others had worked for Government and observed the need for customized solutions, which required market incentives to fully develop.

The evaluator and CEO panelists’ identification of challenges converged in some cases:

  • maintaining affordability and quality when using market pricing
  • obtaining venture capital or other financing
  • worry over “mission drift” – if financial sustainability imperatives or shareholder profit maximization preferences prevail over founders’ social impact goals; and
  • the still present digital divide, when serving global customers (insufficient bandwidth, affordability issues, limited small business capital in some client countries.

New issues raised by the CEOs (and some social entrepreneurs in the audience) included:

  • the need to provide incentives to customers to use quality assurance or security features of software, to avoid falling short of achieving the SEV’s “public good” goals;
  • the possibility of hostile takeover, given high value of technological innovations;
  • the fact that mention of a “social impact goal” was a red flag to some funders who then went elsewhere to seek profit maximization.

There was also a rich discussion on the benefits and costs of obtaining certification: it was a useful “branding and market signal” to some consumers, but a negative one to some funders; also, it posed an added burden on managers to document and report social impact, sometimes according to guidelines not in line with their preferences.

Surprises?

a) Despite the “hype”, social impact investment funding proved elusive to the panelists. Options for them included: sliding scale pricing; establishment of a complementary for-profit arm; or debt financing;

b) Many firms were not yet implementing planned monitoring and evaluation (M&E) programs, despite M&E being one of their service offerings; and

c) The legislation on reporting social impact of benefit corporations among the 31 states varies considerably, and the degree of enforcement is not clear.

A conclusion for evaluators: Social entrepreneurs’ use of market solutions indeed provides an evolving, dynamic environment which poses more complex challenges for measuring social impact, and requires new criteria and tools, ideally timed with an understanding of market ups and downs, and developed with full participation of the business managers.

How to buy M&E software and not get bamboozled

by Josh Mandell, a Director at DevResults where he leads strategy and business development. Josh can be reached at josh@devresults.com.

While there is no way to guarantee that M&E software will solve all of your problems or make all of your colleagues happy, there absolutely are things you can do during the discovery, procurement, and contracts stages to mitigate against the risk of getting bamboozled.

#1 – Trust no one. Test everything.

Most development practitioners I speak with are balancing a heavy load of client work, internal programmatic and BD support, and other organization initiatives. I can appreciate that time is scarce and testing software you may not buy could feel like a giant waste of time.

However, when it comes to reducing uncertainty and building confidence in your decision, the single most productive use of your time is spent testing. When you don’t test, what evidence do you have to base your decision on? The vendor’s marketing and proposal materials. Don’t take the BD guy’s word for it and whatever you do, don’t trust screenshots, brochures, or proposals. Like a well-curated social media profile, marketing collateral gives you a sense for what’s possible, but probably isn’t the most accurate reflection of reality. If you really want to understand usability, performance, and culture fit, you simply need to see for yourself.

We have found that the organizations that take the time to identify and test what they’ll actually be doing in DevResults are much better off than those who buy based on what they see in documentation and presentations, or based on someone else’s recommendation.

And it makes our lives easier too! We may have to spend a little more time upfront in the discovery and procurement phases, but by properly setting expectations early on, we have to provide far less support over the long-term. This makes for smoother, lower-cost implementations and happier customers.

#2 – Document what success looks like in plain language.

We obviously need contracts for defining the scope of work, payment terms, SLA, and other legalese, but the reality is that the people leading procurement and contracts are often not the people leading the day to day data operations.

Contracts are also typically dense and hard to use as a point of reference for frequent, human communication. So, it’s incredibly important that the implementation leads themselves define what success looks like in their own words and that is what drives the implementation.

It took us years to figure this out, but we’ve taken the lesson to heart. What we do now with each of our engagements is create an Implementation Charter that documents, in the words of the implementation leads, things like a summary baseline, roles and responsibilities, and a list of desired outcomes, i.e. ‘what success looks like.’ We then use the charter as the primary point of reference for determining whether or not we’re doing a good job and we evaluate ourselves against the charter quarterly.

Similar to the point about testing above, we have found this practice to dramatically increase transparency, properly set expectations, and establish more effective channels for communication, all of which are crucial in enterprise software implementations.

#3 – Plan for the long-haul and create the right financial incentives. Spread out the payments.

Whether at the project or organizational levels, M&E software implementations are long-term efforts. Unlike custom, external-facing websites where the bulk of work is done up front and the rest is mostly maintenance, enterprise software is constantly evolving. Rapidly changing technology and industry trends, shifting user requirements, and quality user experience all require persistent attention and ongoing development.

Your contract and payment structure should reflect that reality.

The easiest way to achieve this alignment is to spread the payments out over time. I’m not going to get into the merits of a software as a service (SaaS) business model here (we’ll be putting another post out on that in the coming weeks), but suffice to say that you get better service when your technology partner needs to continuously earn your money month after month and year after year.

This not only shifts the focus from checking boxes in a contract to delivering actual utility for users over the long-term, but it also hedges against the prospect of paying for unused software (or even paying for vaporware, as in the case of the BMGF case against Saama).

We know from experience that shifting to a new way of doing things can be difficult. We used to be a custom-web development shop and we did pretty well in that old model. The transition to a SaaS offering was painful because we had to work harder to earn our money and expectations went up dramatically. Nonetheless, we know the pain has been worth it because our customers are holding us to a different standard and it’s forcing us to deliver the best product we’re capable of. As a result, we’ll not only have happier customers, but a stronger, more sustainable business doing what we love.

Stop the bamboozling.

If you have any tips or recommendations for buying software, please share those in the comments below, or feel free to reach out to me directly. We’re always looking to share what we know and learn from others. Good luck!

MERL Tech London is coming up on March 20-21, 2018 — Submit your session ideas or register to attend!

Submit your session ideas for MERL Tech London by Nov 10th!

MERL Tech London

Please submit a session idea, register to attend, or reserve a demo table for MERL Tech London, on March 19-20, 2018, for in-depth sharing and exploration of what’s happening across the multidisciplinary monitoring, evaluation, research and learning field.

Building on MERL Tech London 2017, we will engage 200 practitioners from across the development and technology ecosystems for a two-day conference seeking to turn the theories of MERL technology into effective practice that delivers real insight and learning in our sector.

MERL Tech London 2018

Digital data and new media and information technologies are changing MERL practices. The past five years have seen technology-enabled MERL growing by leaps and bounds, including:

  • Adaptive management and ‘developmental evaluation’
  • Faster, higher quality data collection.
  • Remote data gathering through sensors and self-reporting by mobile.
  • Big Data and social media analytics
  • Story-triggered methodologies

Alongside these new initiatives, we are seeing increasing documentation and assessment of technology-enabled MERL initiatives. Good practice guidelines and new frameworks are emerging and agency-level efforts are making new initiatives easier to start, build on and improve.

The swarm of ethical questions related to these new methods and approaches has spurred greater attention to areas such as responsible data practice and the development of policies, guidelines and minimum ethical frameworks and standards for digital data.

Please submit a session idea, register to attend, or reserve a demo table for MERL Tech London to discuss all this and more! You’ll have the chance to meet, learn from, debate with 150-200 of your MERL Tech peers and to see live demos of new tools and approaches to MERL.

Submit Your Session Ideas Now!

Like previous conferences, MERL Tech London will be a highly participatory, community-driven event and we’re actively seeking practitioners in monitoring, evaluation, research, learning, data science and technology to facilitate every session.

Please submit your session ideas now. We are particularly interested in:

  • Case studies: Sharing end-to-end experiences/learning from a MERL Tech process
  • MERL Tech 101: How-to use a MERL Tech tool or approach
  • Methods & Frameworks: Sharing/developing/discussing methods and frameworks for MERL Tech
  • Data: Big, large, small, quant, qual, real-time, online-offline, approaches, quality, etc.
  • Innovations: Brand new, untested technologies or approaches and their application to MERL(Tech)
  • Debates: Lively discussions, big picture conundrums, thorny questions, contentious topics related to MERL Tech
  • Management: People, organizations, partners, capacity strengthening, adaptive management, change processes related to MERL Tech
  • Evaluating MERL Tech: comparisons or learnings about MERL Tech tools/approaches and technology in development processes
  • Failures: What hasn’t worked and why, and what can be learned from this?
  • Demo Tables: to share MERL Tech approaches, tools, and technologies
  • Other topics we may have missed!

Session Submission Deadline: Friday, November 10, 2017.

Session leads receive priority for the available seats at MERL Tech and a discounted registration fee. You will hear back from us in early December and, if selected, you will be asked to submit an updated and final session title, summary and outline by Friday, January 19th, 2018.

Register Now!

Please register to attend, or reserve a demo table for MERL Tech London 2018 to examine these trends with an exciting mix of educational keynotes, lightning talks, and group breakouts, including an evening Fail Festival reception to foster needed networking across sectors.

We are charging a modest fee to better allocate seats and we expect to sell out quickly again this year, so buy your tickets or demo tables now. Event proceeds will be used to cover event costs and to offer travel stipends for select participants implementing MERL Tech activities in developing countries.