Tag Archives: MERL Tech DC

How I Learned to Stop Worrying and Love Big Data

by Zach Tilton, a Peacebuilding Evaluation Consultant and a Doctoral Research Associate at the Interdisciplinary PhD in Evaluation program at Western Michigan University. 
 
In 2013 Dan Airley quipped “Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it….” In 2015 the metaphor was imported to the international development sector by Ben Ramalingam, in 2016 it became a MERL Tech DC lightning talk, and has been ringing in our ears ever since. So, what about 2018? Well, unlike US national trends in teenage sex, there are some signals that big or at least ‘bigger’ data is continuing to make its way not only into the realm of digital development, but also evaluation. I recently attended the 2018 MERL Tech DC pre-conference workshop Big Data and Evaluation where participants were introduced to real ways practitioners are putting this trope to bed (sorry, not sorry). In this blog post I share some key conversations from the workshop framed against the ethics of using this new technology, but to do that let me first provide some background.
 
I entered the workshop on my heels. Given the recent spate of security breaches and revelations about micro-targeting, ‘Big Data’ has been somewhat of a boogie-man for myself and others. I have taken some pains to limit my digital data-footprint, have written passionately about big data and surveillance capitalism, and have long been skeptical of big data applications for serving marginalized populations in digital development and peacebuilding. As I found my seat before the workshop started I thought, “Is it appropriate or ethical to use big data for development evaluation?” My mind caught hold of a 2008 Evaluation Café debate between evaluation giants Michael Scriven and Tom Cook on causal inference in evaluation and the ethics of Randomized Control Trials. After hearing Scriven’s concerns about the ethics of withholding interventions from control groups, Cook asks, “But what about the ethics of not doing randomized experiments?” He continues, “What about the ethics of having causal information that is in fact based on weaker evidence and is wrong? When this happens, you carry on for years and years with practices that don’t work whose warrant lies in studies that are logically weaker than experiments provide.”
 
While I sided with Scriven for most of that debate, this question haunted me. It reminded me of an explanation of structural violence by peace researcher Johan Galtung who writes, “If a person died from tuberculosis in the eighteenth century it would be hard to conceive of this as violence since it might have been quite unavoidable, but if he dies from it today, despite all the medical resources in the world, then violence is present according to our definition.” Galtung’s intellectual work on violence deals with the difference between potential and the actual realizations and what increases that difference. While there are real issues with data responsibility, algorithmic biases, and automated discrimination that need to be addressed, if there are actually existing technologies and resources not being used to address social and material inequities in the world today, is this unethical, even violent? “What about the ethics of not using big data?” I asked myself back. The following are highlights of the actually existing resources for using big data in the evaluation of social amelioration.
 

Actually Existing Data

 
During the workshop, Kerry Bruce from Social Impact shared with participants her personal mantra, “We need to do a better job of secondary data analysis before we collect any more primary data.” She challenged us to consider how to make use of the secondary data available to our organizations. She gave examples of potential big data sources such as satellite images, remote sensors, GPS location data, social media, internet searches, call-in radio programs, biometrics, administrative data and integrated data platforms that merge many secondary data files such as public records and social service agency and client files. The key here is there are a ton of actually existing data, many of which are collected passively, digitally, and longitudinally. Despite noting real limitations to accessing existing secondary data, including donor reluctance to fund such work, limited training in appropriate methodologies in research teams, and differences in data availability between contexts, to underscore the potential of using secondary data, she shared a case study where she lead a team to use large amounts of secondary indirect data to identify ecosystems of modern day slavery at a significantly reduced cost than collecting the data first-hand. The outputs of this work will help pinpoint interventions and guide further research into the factors that may lead to predicting and prescribing what works well for stopping people from becoming victims of slavery.
 

Actually Existing Tech (and math)

 
Peter York from BCT Partners provided a primer on big data and data science including the reality-check that most of the work is the unsexy “ETL,” or the extraction, transformation, and loading of data. He contextualized the potential of the so-called big data revolution by reminding participants that the V’s of big data, Velocity, Volume, and Variety, are made possible by the technological and social infrastructure of increasingly networked populations and how these digital connections enable the monitoring, capturing, and tracking of ever increasing aspects of our lives in an unprecedented way. He shared, “A lot of what we’ve done in research were hacks because we couldn’t reach entire populations.” With advances in the tech stacks and infrastructure that connect people and their internet-connected devices with each other and the cloud, the utility of inferential statistics and experimental design lessens when entire populations of users are producing observational behavior data. When this occurs, evaluators can apply machine learning to discover the naturally occurring experiments in big data sets, what Peter terms ‘Data-driven Quasi-Experimental Design.’ This is exactly what Peter does when he builds causal models to predict and prescribe better programs for child welfare and juvenile justice to automate outcome evaluation, taking cues from precision medicine.
 
One example of a naturally occurring experiment was the 1854 Broad Street cholera outbreak in which physician John Snow used a dot map to identify a pattern that revealed the source of the outbreak, the Broad Street water pump. By finding patterns in the data John Snow was able to lay the groundwork for rejecting the false Miasma Theory and replace it with a proto-typical Germ Theory. And although he was already skeptical of miasma theory, by using the data to inform his theory-building he was also practicing a form of proto-typical Grounded Theory. Grounded theory is simply building theory inductively, after data collection and analysis, not before, resulting in theory that is grounded in data. Peter explained, “Machine learning is Grounded Theory on steroids. Once we’ve built the theory, found the pattern by machine learning, we can go back and let the machine learning test the theory.” In effect, machine learning is like having a million John Snows to pour over data to find the naturally occurring experiments or patterns in the maps of reality that are big data.
 
A key aspect of the value of applying machine learning in big data is that patterns more readily present themselves in datasets that are ‘wide’ as opposed to ‘tall.’ Peter continued, “If you are used to datasets you are thinking in rows. However, traditional statistical models break down with more features, or more columns.” So, Peter and evaluators like him that are applying data science to their evaluative practice are evolving from traditional Frequentist to Bayesian statistical approaches. While there is more to the distinction here, the latter uses prior knowledge, or degrees of belief, to determine the probability of success, where the former does not. This distinction is significant for evaluators who are wanting to move beyond predictive correlation to prescriptive evaluation. Peter expounded, Prescriptive analytics is figuring out what will best work for each case or situation.” For example, with prediction, we can make statements that a foster child with certain attributes is 70% not likely to find a home. Using the same data points with prescriptive analytics we can find 30 children that are similar to that foster child and find out what they did to find a permanent home. In a way, only using predictive analytics can cause us to surrender while including prescriptive analytics can cause us to endeavor.
 

Existing Capacity

The last category of existing resources for applying big data for evaluation was mostly captured by the comments of independent evaluation consultant, Michael Bamberger. He spoke of the latent capacity that existed in evaluation professionals and teams, but that we’re not taking full advantage of big data: “Big data is being used by development agencies, but less by evaluators in these agencies. Evaluators don’t use big data, so there is a big gap.”

He outlined two scenarios for the future of evaluation in this new wave of data analytics: a state of divergence where evaluators are replaced by big data analysts and a state of convergence where evaluators develop a literacy with the principles of big data for their evaluative practice. One problematic consideration with this hypothetical is that many data scientists are not interested in causation, as Peter York noted. To move toward the future of convergence, he shared how big data can enhance the evaluation cycle from appraisal and planning through monitoring, reporting and evaluating sustainability. Michael went on to share a series of caveats emptor that include issues with extractive versus inclusive uses of big data, the fallacy of large numbers, data quality control, and different perspectives on theory, all of which could warrant their own blog posts for development evaluation.

While I deepened my basic understandings of data analytics including the tools and techniques, benefits and challenges, and guidelines for big data and evaluation, my biggest take away is reconsidering big data for social good by considering the ethical dilemma of not using existing data, tech, and capacity to improve development programs, possibly even prescribing specific interventions by identifying their probable efficacy through predictive models before they are deployed.

(Slides from the Big Data and Evaluation workshop are available here).

Do you use or have strong feelings about big data for evaluation? Please continue the conversation below.

 

 

MERL on the Money: Are we getting funding for data right?

By Paige Kirby, Senior Policy Advisor at Development Gateway

Time for a MERL pop quiz: Out of US $142.6 billion spent in ODA each year, how much goes to M&E?

A)  $14.1-17.3 billion
B)  $8.6-10 billion
C)  $2.9-4.3 billion

It turns out, the correct answer is C. An average of only $2.9-$4.3 billion — or just 2-3% of all ODA spending — goes towards M&E.

That’s all we get. And despite the growing breadth of logframes and depths of donor reporting requirements, our MERL budgets are likely not going to suddenly scale up.

So, how can we use our drop in the bucket better, to get more results for the same amount of money?

At Development Gateway, we’ve been doing some thinking and applied research on this topic, and have three key recommendations for making the most of MERL funding.

Teamwork

Image Credit: Kjetil Korslien CC BY NC 2.0

When seeking information for a project baseline, midline, endline, or anything in between, it has become second nature to budget for collecting (or commissioning) primary data ourselves.

Really, it would be more cost-and time-effective for all involved if we got better at asking peers in the space for already-existing reports or datasets. This is also an area where our donors – particularly those with large country portfolios – could help with introductions and matchmaking.

Consider the Public Option

Image Credit: Development Gateway

And speaking of donors as a second point – why are we implementers responsible for collecting MERL relevant data in the first place?

If partner governments and donors invested in country statistical and administrative data systems, we implementers would not have such incentive or need to conduct one-off data collection.

For example, one DFID Country Office we worked with noted that a lack of solid population and demographic data limited their ability to monitor all DFID country programming. As a result, DFID decided to co-fund the country’s first census in 30 years – which benefited DFID and non-DFID programs.

The term “country systems” can sound a bit esoteric, pretty OECD-like – but it really can be a cost-effective public good, if properly resourced by governments (or donor agencies), and made available.

Flip the Paradigm

Image Credit: Rafael J M Souza CC BY 2.0

And finally, a third way to get more bang for our buck is – ready or not – Results Based Financing, or RBF. RBF is coming (and, for folks in health, it’s probably arrived). In an RBF program, payment is made only when pre-determined results have been achieved and verified.

But another way to think about RBF is as an extreme paradigm shift of putting M&E first in program design. RBF may be the shake-up we need, in order to move from monitoring what already happened, to monitoring events in real-time. And in some cases – based on evidence from World Bank and other programming – RBF can also incentivize data sharing and investment in country systems.

Ultimately, the goal of MERL should be using data to improve decisions today. Through better sharing, systems thinking, and (maybe) a paradigm shake-up, we stand to gain a lot more mileage with our 3%.

 

Present or lead a session at MERL Tech DC!

Please sign up to present, register to attend, or reserve a demo table for MERL Tech DC 2018 on September 6-7, 2018 at FHI 360 in Washington, DC.

We will engage 300 practitioners from across the development ecosystem 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 DC 2018, September 6-7, 2018

Digital data and new media and information technologies are changing monitoring, evaluation, research and learning (MERL). The past five years have seen technology-enabled MERL growing by leaps and bounds. We’re also seeing greater awareness and concern for digital data privacy and security coming into our work.

The field is in constant flux with emerging methods, tools and approaches, such as:

  • Adaptive management and developmental evaluation
  • Faster, higher quality data collection
  • Remote data gathering through sensors and self-reporting by mobile
  • Big data, data science, 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 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 standards for digital data.

Championing the above is a growing and diversifying community of MERL practitioners, assembling from a variety of fields; hailing from a range of starting points; espousing different core frameworks and methodological approaches; and representing innovative field implementers, independent evaluators, and those at HQ that drive and promote institutional policy and practice.

Please sign up to present, register to attend, or reserve a demo table for MERL Tech DC to experience 2 days of in-depth sharing and exploration of what’s been happening across this cross-disciplinary field, what we’ve been learning, complex barriers that still need resolving, and debate around the possibilities and the challenges that our field needs to address as we move ahead.

Submit Your Session Ideas Now

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

Please submit your session ideas now. We are looking for a range of topics, including:

  • Experiences and learning at the intersection of MERL and tech
  • Ethics, inclusion, safeguarding, and data privacy
  • Data (big data, data science, data analysis)
  • Evaluation of ICT-enabled efforts
  • The future of MERL
  • Tech-enabled MERL Failures

Visit the session submission page for more detail on each of these areas.

Submission Deadline: Monday, April 30, 2018 (at midnight EST)

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

Register Now

Please sign up to present or register to attend MERL Tech DC 2018 to examine these trends with an exciting mix of educational keynotes, lightning talks, and group breakouts, including an evening reception and Fail Fest to foster needed networking across sectors and an exploration of how we can learn from our mistakes.

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.

You can also submit session ideas for MERL Tech Jozi, coming up on August 1-2, 2018! Those are due on March 31st, 2018!

MERL Tech DC Conference wrap up

Over 300 MERL Tech practitioners came together in Washington DC the first week of September for MERL Tech DC.

Kathy Newcomer, American Evaluation Association President, gives her opening talk on Day 2.
Kathy Newcomer, American Evaluation Association President, gives her opening talk on Day 2.
Blockchain was one of the most popular sessions.
Blockchain was one of the most popular sessions.

Core topic areas included organizational change and capacity; evaluation of MERL Tech and ICT4D; big data, small data and data analytics; tech tools to support qualitative methods; new and emerging technologies with a potential role in MERL; inclusion and ways tech can support ‘downward’ accountability; practical sessions on tools and methods; and community building in the MERL Tech sector.

Check out InSTEDD’s fantastic recap of the event in pictures and Tweets.

What does “MERL Tech Maturity” look like?

In plenary, groups worked together to discuss “MERL Tech Maturity Models” – in other words, what are the characteristics of an organization that is fully mature when it comes to MERL Tech. People also spent some time thinking about where their organizations fit on the “MERL Tech Maturity” scale: from brand new or less experienced to fully mature. (We’ll share more about this in a future post).

The Data Turnpike was voted the best depiction of a Maturity Model.
The Data Turnpike was voted the best depiction of a Maturity Model.

As always, there was plenty of socializing with old and new friends and collaborators too!

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Stay tuned for session summaries and more, coming up over the next several weeks here on MERL Tech News!

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

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

Submit Your Session Ideas by Friday, May 12th!

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

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

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

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

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

Submit your ideas here!