The Data Revolution: How to Turn Data into Actionable Insights for Social Good

In this edition of the #TechForGood Series, Prem Durairaj and Alexander Paxton from Data Elevates share why change is necessary with the great opportunity epoch for organizations in the social sector to unlock true social change with a data-driven approach! They discuss the various buzzwords in the data world, their experience and success stories in the field, the obstacles for impact-driven organizations, and the - data-aware - the data-nurtured world of the future they dream of!

?? In their words: “The potential for NGOs and social impact organizations to increase their impact by taking advantage of data assets has never been greater, and never before have there been so many powerful and affordable data-driven tools that are available to organizations to help them better serve their beneficiaries, to better serve their users (…) But in many cases, there's still a huge gap between social impact organizations and the kinds of things that are being done in the private sector in for-profit counterparts. So, Data Elevates exists to help demystify these tools and to work with organizations to better make decisions on how to invest in their data assets, in their data systems to increase their impact and use these tools. One of the things that set us apart, from some of the organizations you might find in the private sector that do this work is that we very much come from a social impact background, we're a mission-driven organization.”

?#dataforgood?#collaboration?#opportunityishere?#thankyou?#inclusivegrowth?#socialimpact

Nelly: Good morning, Xan, and Prem. I'm so happy to have you guys for this edition of The Tech for Good series. I am such an admirer of your work, your team, of the way you guys put things together, and the way you guys manage projects and clients. And of course, the patience that you have with people who are not tech-savvy but who want to start in this world of data for good. This certainly was my case when I first approached you guys. And I'm so thankful and it's been amazing all I've learned from you guys, working together, and collaborating.?

So, very excited to have this first opportunity to have you guys in the program. And I wanted to start out, of course I know, but to share with the audience, what Data Elevates is? I know you guys are an impressive team. Why now? How did it come about? How did you guys come together and say, “You know, this is necessary for the world”? And why you guys?

Xan: ?Thanks, Nelly. It's great to be here. You know, in terms of the why, as you know, and probably a lot of your viewers know, the potential for NGOs and social impact organizations to increase their impact by taking advantage of data assets has never been greater, and never before have there been so many powerful and affordable data-driven tools that are available to organizations to help them better serve their beneficiaries, to better serve their users. And more and more, we see those NGOs and social enterprises and public institutions like governments are aware that these tools are out there. They've heard this phrase: "data" or "big data" or "machine learning", and so they're really excited about the opportunities. But in many cases, there's still a huge gap between social impact organizations and the kinds of things that are being done in the private sector by for-profit counterparts.

So, Data Elevates exists to help demystify these tools and to work with organizations to better make decisions on how to invest in their data assets, in their data systems to increase their impact and use these tools. One of the things that set us apart, I think, from some of the organizations you might find in the private sector that do this work is that we very much come from a social impact background, we're a mission-driven organization. I really want to see our partners not just use these tools but understand these tools and be able to ask and demand more of their data systems in a sustainable way. They have the skills and background knowledge to continue to improve sustainably over time.

N: ?????Super! Thank you, Xan. And who are you guys? How did you guys come together? Who is the team?

?Xan: ??Prem, you want to take this one?

?Prem: Sure. Absolutely. So, my name is Prem Durairaj. I'm the Head of Business Strategy and Operations in Data Elevates. And I have about 15 years in international development experience. We all came together in D.C. and all kind of moved into different places. But we're all kind of working in this sphere. We've been working in international development separately and together for a large part of our careers. And, you know, we all saw the value in using data for, well probably a little bit later, but using data for good. And so, myself and I'll also mention Rob Segan, who is the Head of Technical Direction, he’s also been a huge proponent of this at alls different sort of careers. And Xan, maybe you can talk a little bit about yourself.?

Xan: ??Sure. I'm Xan Paxton. I'm Head of Data Insights here at Data Elevates. And basically, I help lead our work in data pipelining, engineering, and data analytics.

N: ??????And before we get into some of these buzzwords like the data pipeline, just to visualize, what is data for social impact or data for good? Could you guys share with us a few success stories where it's very clear how this can certainly add value and enhance the impact social programs or economic and social impacts initiatives can have?

Xan: ??Yeah, absolutely. So, I think taking a step back, I think I wanted to talk a little about data for good. When we think about data, think about digital information, think about different types of data, structured, unstructured, quantitative, and qualitative. And so really when, for Data Elevates, when we're thinking about data for good, we're thinking about the utilization or using data for good. And we think about that, we're thinking about across sort of the data-driven decision-making cycle. So, for example, just a few points within that, you think about data collection, you know, collecting data in a way that is sort of usable for analysis, it is formatted in a way that can be stored and managed, is collected in a way that's ethical, that considers and honors the stakeholders, the vulnerable communities that we're collecting data from in a way that respects their privacy. You know, there's storage and there's analysis, and the analysis I was thinking about, data that generates insights that can be used for improving products, improving projects, improving organizations that are looking to do better with that information.

And then finally, just to go briefly through this, we're thinking about communicating data. So, data storytelling, data visualization, things like that, think bars or charts, infographics, and things like that to really support stakeholders and leaders and decision-makers to actually implement some better processes, better ways of supporting communities and vulnerable communities that we all want to support. So, really just thinking about it, kind of a holistic way in terms of kind of honoring those that were both collecting data from, but then also the people that we're trying to support. And then... Yeah.

Nelly: And do you have some like actual success stories where you saw a product being in hands, where you saw service being improved, or where you had, or you saw that the impact was increased because they deployed or integrated a data for good? We could say approach.

Xan: ??Sure, I'll take this one. I think one of the more recent examples that we have and relevant is under the data.org Inclusive Growth and Recovery Challenge. Data Elevates is partnered with Fundación Capital based out of Colombia, as well as our partners in Mozambique, UX; to build and enhance a job market platform designed for informal workers. So, basically, it's a low-tech solution in the sense that it uses SMS and USSD, which basically is all you need to know as it works on any phone regardless of whether you have a data plan or not.?

And this platform's been running for a few years, and they have a database and they've been collecting data about these users. But in many cases, they haven't really thought about how are we going to use this information that we're collecting beyond just the bare minimum of making our platform work? How are we going to actually generate insights? How are we going to increase the value of our product to our users and understand how our platform can be improved to generate more job opportunities for people?

And so, in that particular opportunity, what we've done is work with these organizations to better understand where they want to go, and what are the kinds of things they want to see out of their data system beyond. And part of that is educating and exposing them to some of the concepts and some of the tools that are available, that are being used in the private sector, and some of the management approaches that are being used for data-driven decision-making. And then powering them with basically a data pipeline. So, basically collecting, cleaning, and making that data available to business intelligence tools as well as applications for machine learning and improving processes through AB testing.

So, in this case, it's very much kind of an end-to-end application of a modern kind of data system and data-driven approach. The one thing I might add is that the way I think, what we've really found through this opportunity and through this project is what's really been motivating, what's really resonated with people is an approach or an ethically driven approach to data management. And by that, I mean people are really, really interested in making sure that they're doing the best they possibly can for their users, protecting their data, and using it in a way that's not going to cause harm. And because people are motivated by helping people that are using modern data systems, so collecting, and storing data in a way that's secure, that minimizes the risk to those users is a motivator for investing in better data systems. So, we've really had a lot of success and are really excited about where that opportunity is going.?

N: ??????Yes. And could you guys walk us through some of these buzzwords like data pipe? What's the difference between a data scientist and a data engineer? What is a data pipeline? What is a data lifecycle? What is a data system? And overall, what is a data-driven approach? Like what do we mean by that?

Prem: Sure. I'll take this one to start with. You know, I like to think about a data system as being a little bit like a kitchen. Right. I think a lot of people have heard of this thing called a data scientist because it's been in the news a lot over the last few years. And people heard about data scientists and they're doing machine learning and they're doing really cool things and they're finding all these interesting insights from big, big data sources. If the data scientist is like a pastry chef in a kitchen, a data engineer is like the guy who sets the kitchen up, who makes sure that the oven has gas, that there's flour and sugar in the cupboard, and that things are organized and where they're going to be so that the different people who are playing in the kitchen have what they need when they need it in a format that they need it.

So, what a data engineer does, in a little bit more technical sense, is looking at the data assets that you have. So maybe a database, maybe it's something that's sitting in a spreadsheet, maybe it's a third-party data set. So, something that's available from the UN or the World Bank or from a local government institution and says, “How can I get all these things into one place so that people can use them effectively and not have to spend all their time playing with a bunch of different files or trying to clean?” And the engineer is the person who sits on top of the system with the bird's eye view and says, “Okay, this person needs access to these data sets, and they need them in a clean fashion, let me make that happen!” And happen in a way that's automatic as much as possible, happen in a way that is useful for them.?

And so, the data scientist, on the other hand, is one of the people who might be consuming that data and playing with machine learning tools to do predictive analytics. There might also be data analysts. So, people whose job it is to visualize data, explore questions about different approaches, different management approaches. If I roll out an update to an app, say, what was the effect of that on our users? Are they happier? Less happy? Are they using it more often? Less often?

And of course, also kind of in the data ecosystem of people, you have people who are collecting and using that data on a day-to-day basis. There might be program managers, there might be people in the field collecting data, and all of them are part of the data team. So, there are definitely specialized roles and I think as an organization grows, it can invest in these various roles. And as you mentioned, an engineer is someone who can kind of sit back and help make sure people have the right information they need in the format they need it, so they can do their job.

Nelly: One of the things...

Prem: I was just going to quickly add that when we think about like a data-driven approach, I think just to like kind of summarize the things that it's been talking about. I think the idea here is I think, it's really kind of this holistic look at data from all the way, from sort of collecting data to storing and managing data in a secure way to analyzing data to be able to communicate that data, to then create more data-driven decisions.?

So, when you think about it regarding an organization, it really in some ways hits on almost, if not every person within an organization, to some extent, some more than others. But everyone needs to be brought in, everyone needs to be connected to it to really create this sort of real data-driven approach to how decisions are fundamentally made. So, I think Xan kind of alluded to that sort of through all the different kinds of aspects of it. But just wanted to add that in.?

N: ??????Thank you. And elaborating on this more precise vision of what it is to have a data-driven approach and what it is to put together a data team. What are some of the challenges that you've seen working with social impact projects and social enterprises, with NGOs, with different initiatives? I know that you've worked in Africa and Latin America.

For example, one of them is that a lot of us, collect a lot of data, but then again, data quality becomes an issue because data is not collected properly, it's not stored properly, and is not managed properly. So, I remember you guys when you put together a diagnostic that you came together with the data collection, the data management, the data analytics, the data privacy, and security, and like the whole data governance broad view. You guys taught us about that. And so, my question is, where do you find the major pain points for social enterprises, for NGOs, for social impact projects in general, or economic and social impact projects?

Xan: ??Sure, I'll start it off, and then maybe if you want to add to it. I think one area that I see that kind of comes to my head right off the bat is actually less on the technical side and more on, in all honesty, like on the funding side, to some extent. So, I think one of the challenges is that social impact organizations, particularly NGOs, are fundamentally or structurally are funded through grants and donors, and donors and donor funding. So, it's very project-based, you know, there's a project, it has a certain duration, whether it be a few months, whether it be a few years. But at some point, that duration generally ends and the funding stream for that project ends from the donor.

And because of that, and understandably so, organizations, these organizations are generally pretty cash strapped. And so, they need to kind of provide the evaluations and things and collect the data really in relation to that project, which is great for the project itself. However, what ends up being a challenge is that interconnecting data to support the organization grow itself and hence, realistically, support the projects that they then pursue is a problem because the funding is limited to actually do that piece. So, I think that's one of the big problems, actually.?

Nelly: Super, super interesting. I've certainly seen that in the flesh that that's the case. And what are the opportunities? So, with the pandemic, I saw these really inspiring stories about what was done with the data and with health, with some health-oriented or within the health industry. Also, when you see natural disasters, now you see how the data could really help target aid to more vulnerable populations. I'm sure that you guys, much more than I, have seen a lot of cases where data really enables very sophisticated, pinpointing of the main challenge in different territories. So, what opportunities do you see within the social sector to start working in a more data-driven approach and to start attracting talent that has this? Or, you know, or looking to enhance their data capabilities where I know that Data Elevates could add so much value.?

Prem: Sure. You know, I think the COVID example is a good one because I think one of the things, we've seen over the past two years is that people have a much better understanding. They're becoming more aware of some of the possibilities of what's possible when you can use and collect data and act on it in real-time. And I think because people through the dashboards that are made available through universities, through governments, through just some cool online tools that allow you to track day-to-day what's happening in your area, even down to the city or the county in the United States. And people are using that to make decisions about whether they're going to go out that day or whether the next week or whether I'm going to go to the movies this weekend.?

And so, people are applying these principles in their daily lives. And I think that really opens up an opportunity to transfer that mindset into your work life as well. I think that there are a lot of opportunities, not just in the visualization space, but I think people have more of a mindset of, okay, let's find ways to not just collect data at the beginning of a project, in the middle, and in the end, let's find ways to use that data. And I think donors, if you're a donor-funded organization or if you're a project-based organization, are understanding that there are opportunities to build that end and build funding for that end throughout the entire life of the project.

And I think the organizations that can think that way, that can educate their proposal staff on what's possible or bring their data engineers or their data team, if they have one, into the proposal process, I think they're actually going to be more successful going forward in that space if that's what you're thinking about. So, I think there are a lot of opportunities there.?

One more thing I might add is there is a lot more low-cost, relatively low barrier to entry tools that are being made available. So, maybe your organization doesn't have big data. Maybe you're not going to be building, you might not be building your own natural language processing engine, but there are more and more tools that are powered by very, very powerful models, very pre-trained models for natural language processing, for computer vision, or things that make the barrier entry to applying machine learning tools very inexpensive. And you can, without having to do all the investment, that say, some big private sector organization might do in servers and computers, you might be able to buy it on a subscription basis. And in many cases, a lot of those tools are even free to start and relatively small for the types of data we're talking about for social impact organizations. So, I think that there are a lot more affordable opportunities for people to get their organization's feet wet in the data space.

M: ?????Super! I think that, yeah, that there's this increased sensitivity to this because I'm sure that John Hopkins never imagined the traffic that it was going to have on its data visualization tools as they did in 2020 and 2021. They became like this global reference of how things were going with the pandemic. And I always like to end the interviews with one powerful question, because I think that it's really inspiring if we could start synchronizing what all of us in the tech and data for the good sphere are dreaming of. And if you were to dream of the perfect future, starting with you, Prem, what is that future? What does that future look like??

Prem: Yeah, no, it's a great question! Well, we think of a lot, I mean...

Nelly: An easy one to end.

Prem: Yeah, absolutely. Yeah. So, for me, that future is really one where data skills and data literacy is inherent or all people essentially have data literacy and a certain level of data skills, whether they be from all sorts of different countries, socioeconomic backgrounds, etc. It's a world where essentially data skills are honestly similar to the ability to speak a language. It's that accessible and that available and that understanding so that the playing field is level across all people, which essentially creates equity. That would be my answer.

Nelly: Super! Thank you. What about you, Xan?

Xan: ??You know, I think I'll build on Prem's. You know, I think that skills and literacy are critical. And I really feel like the perfect world for me would be a world where everyone in an organization from the bottom, from all the way from the field data collector folks, all the way up to the CEO or president, all see themselves as an integral part of the data system. And they may not be managing a database, they might not be pushing data around, but maybe they know enough to demand, to know what's possible, and to demand better data to inform their jobs on a day-to-day basis.

And I think that that demand is the beginning of what I would call the data-driven decision-making cycle. When people demand better data, when they demand those investments, then you'll see improvements elsewhere through the cycle as people say, “Okay, I really need to invest in this, and we can actually make this work on a day-to-day basis in our organization”.

Nelly:?Super! I would add to that that I think that there have been so many challenges for the development world, like there's been so much money into the developing world for ages and I've never seen such an opportunity as there is now for more impactful projects and initiatives in the world, with a more data-driven approach, with real-time and based on real needs and pain points of the users worldwide.?

So, I think if there was ever an opportunity for the developing world to showcase the impacts and transformation and disruption of the major problems that we face, I think it's today just because of what you guys are saying, no? This is accessible, this is possible, this is much more... How do you say? Like, I don't know, I don't want to use the word cheap but so much near you than it ever was.

So, it's a great and inspiring moment for all of us in this sector because the tools are there, and the possibilities are there like there never were before. So, it's great what you guys do, it's inspiring what you do. It's been great to talk to you this morning. Thank you so much for your time, for your work, for your authenticity, and for the ethics, I know that you guys put into everything that you do. So, thank you!?

Xan: ??Thank you for having us! We appreciate it.

Prem: Thank you. Bye-bye.