Open Data: Driving Scale & Impact

Open Data: Driving Scale & Impact

This session was moderated by Eva Friesen, President and CEO, The Calgary Foundation and featured:

  • Lucy Bernholz, Visiting Scholar, Stanford University Center on Philanthropy and Civil Society
  • Michael Lenczner, CEO, Ajah
  • Brian Walsh, Executive Director, Liquidnet For Good

Transcript of session:

Lucy Bernholz, Visiting Scholar, Stanford University Center on Philanthropy and Civil Society: My job is to orient you all to this space and since I don’t have the privilege of knowing you, I have no idea what you already know, so bear with me. The theme is “open data and impact” and I want to give you the opportunities that I see for building new enterprises and changing the way existing enterprises work, understanding that data are a fundamentally different and new raw material, but it’s really Michael and Brian who are out there doing this right now.  I’m in academia thinking about it.

First, I’m going to tell you a story that I just heard last week.  It has to do with Syria.  There was this horrible chemical weapons attack on a school in a city in Syria, and Human Rights Watch has volunteers on the ground throughout the world. Folks in Syria, and they have a person and a half – full time equivalent person and a half – who try to understand, using social media and online information, what’s going on in the countries that they’re watching for human rights abuses.

There’s clear evidence of chemical weapons, crimes against humanity, a human rights abuse.  It’s right in their sweet spot and they’re trying to figure out where the chemical weapons came from and who used them. This is of great importance to the entire planet and there’s great divisiveness about who did what to whom.

This person and a half at Human Rights Watch and their on-the-ground volunteers start thinking about what data they can access to determine if they can figure out where the chemical weapons came from, using cell phone video and texted photographs from the site where the missiles landed and people were killed. They started gathering the photographic imagery.  They also sent the half-time data analyst in Geneva off to look at YouTube videos and any other information they could glean from social media.  They rallied their other volunteers from around the world who also sent things they could find on the web through open sources, through pulling off of Facebook feeds, YouTube, Twitter, all this information to the analysts.

They were also able to take photographs of the people on the ground in Syria of the actual remnants of the missile so they could see the materiel that was left behind.  They ultimately wound up looking at, about 400 YouTube video channels.  They had a couple of dozen cell phone photographs and images and videos.  They had a lot of material from cell phones of the weaponry.

In one of the 400 YouTube channels that they looked at still by still, they actually saw a missile. They found a still image from a video of a missile.  They were able to cross-tabulate that, if you will, with the images on the ground so they could figure out where it was coming from.  They were able to analyze that against these other images and reconstruct, essentially, the arc of travel of the missile.  That led to the analysis and the announcement that the missiles had been sent from a certain mountaintop.  They were sent by the government sources and that was what Human Rights Watch was able to do.

It’s all open data.  That’s what open data can do.  You can use it to reconstruct immediate past history, in that case, a significant crime against humanity, a major global political incident, and you can use it with one and a half paid staff, volunteers on the ground, and it’s data.  It’s video data, it’s photographic data, but it’s all been digitized.  It’s all ones and zeros and it’s all readily available if you know what you’re looking for.

That’s just one example of how you can actually use this information within the type of work that social enterprises and non-profits are focused on.  Usually when we talk about data we’re thinking about numbers and revenue flows and things like that, and that’s data also.

Once this stuff is digitized, it’s actually all data. The real moment we’re in is one in which we can bring these different sources together in ways that can not only create new types of analysis, such as what Human Rights Watch was able to do, but it also allows you to do what you’ve been doing, starting from a different place because you can see the landscape of what you’re working on in a different way.

There’s a couple of terms floating around that some of them mean things and others don’t mean things.  For example, ‘big data’, you hear that all the time.  There’s no definition of ‘big data’, so don’t worry about it.  What matters is that the data are open, because if those staff and a half person at HRW and their volunteers hadn’t been able to actually get the video images from Google and YouTube and cell phones, it wouldn’t have mattered.

There’s some characteristics of openness that truly matter.  They have to be available online.  They have to be able to be pulled down by computers. That’s what’s called ‘machine readable’. You want to be able to grab that stuff and bring it to where you’re going to do analysis of it and hopefully the more your computer can do that and you don’t have to manually do it, the faster and easier you can do it. The data has to be licensed for use.  They have to be up there with some sort of sign on them saying ‘you can use this’.  If they’re not licensed for that use it makes things a lot more difficult, but if they meet those three criteria, then you’re talking about open data.  It may be photographs.  It may be numbers.  It may be government data sets.  It could be medical records.  It could be all kinds of things, but those are the qualities of open data that then lead us to this thing called big data.

Big data is different from small data.  Remember, there’s no definition of it, but because all of the data that’s available in that form, what you get are data sets of data sets and while you have a data set of photographic images, and Michael will talk about the data sets he has of charity information, then you’re actually talking about big data because you’ve got these different data sets.  They may be completely unrelated and disparate, but the analysis becomes possible, comparing them, and you start to be able to ask a new kind of question that you couldn’t ask before.

That’s the moment we’re in and this moment is very much here for the future.  In 2000, the year 2000, 25% of all of human stored information was digital – you count all the books, all the stuff on computers, all the newspapers, everything, all the information we’ve created as a species, over thousands of years, 25% of it was digital 13 years ago. Thirteen years later, less than 2% of it is not digital.  All the growth is in the digital side, and that’s the direction we’re headed. Understanding how to use that as a raw material and really running with it is the great opportunity.

I like to think about data, but lots of people don’t.  So there’s a whole bunch of data, so what?  Think about the sort of basic activities of your life that have changed because someone somewhere was able to take data sets and pull them together and present you with an app or an interface that allows you to do things differently.

Some easy examples of this are: how many of you have used a paper map lately? Okay, two.  I’ve never been to Calgary before. Last night I was able to find a great place to eat and get there directly because when I pulled out my phone, it told me where I was on the planet and where the restaurant was.  I didn’t even have to locate Calgary on a map. We’ve completely changed the way we interact with geographic information, topographical information, restaurant information, hotel information.  It’s completely changed the way we travel.  It’s put us in the center of the data and it gives me precisely the information that we’re looking for.

The same is true with auto-correct.  You know that really annoying feature on your phone?  That’s big data, using other people’s spellings and dictionaries and matching it up, saying you couldn’t possibly mean what you just wrote.  You mean this, and then you say “no, I don’t mean that, I mean this”, and guess what?  It gets smarter when you do that because your action, your interaction with the data, creates more data and that kind of cycle is what’s going on under a lot of these things that we have moved completely to the background of how we go about our data.

Spam filters, if only they worked, would be a fabulous example of big data, open data, at work.  It’s taking other data sets of information and analyzing key words saying she really doesn’t wanna see that message and keeping it out of my inbox, and sometimes it keeps it out of my inbox.  That’s big data.

So our interaction with it doesn’t necessarily happen on the back end. It happens on this other end where our lives are.  Either we’re presented with something we wouldn’t have seen before, like the restaurant where I ate dinner last night, or things that I don’t want to see are kept away from me, as through my spam filter, and I interact with it as through the auto-correct feature and the system gets smarter.

This abundance of information, digitized information that we have, is changing some core assumptions that have influenced the way we do an awful lot of our work.  First of all, most of us are, particularly folks in the social sector, are often trying to figure out “what kind of impact have I had?”  “Did my actions here cause change here?”  And the whole mindset about how you might figure that out is based on “can we show causation?”  Any evaluator worth his or her salt is gonna say “no”, and they’re gonna say “no” largely because all of the methodologies and analytic tools we have are based on using subsets of all the information that could be gathered and saying, “well, we see some relationship in this subset of information between these activities and this outcome, but we can’t extrapolate because we’re looking at a subset of information.” That’s completely valid, except for the fact that in a world of big online open data, the subset is the full set.

We’re actually moving to a situation where for real researchers, there’s no longer going to be subsets.  To get wonky on you all, you can see the entire data set.  It changes our assumptions then about the value of a correlation.  If in fact you look at all of the data, or all of the available data, and you see a correlation between this set of activities and this outcome, but you’re looking at the entire picture, is that good enough? It might be, so we need to change how we think about showing impact.

I want to quickly identify for you it’s not all good, it’s not all happy stories. There’s some very important assumptions that are changing faster than we can either change our human behaviors, our expectations, and certainly our policies and practices.             The one that’s most interesting to me in my work at Stanford has to do with privacy and the interesting reality that we’re in right now. This is also very much an opportunity, but I’m going to present it first as a problem.  For the lifetime of me and, which covers everybody in this room because I’m by far the oldest person in this room, we had, as individuals, basically three ways to protect our privacy when we’re talking about online information. We could opt out of a data base.  We could say, “no, you can’t put me in there”.  We could rely on the researchers or the company that was collecting the data to anonymize it and take out my name and my address and just leave other stuff, or even give me a number, some random number that meant nothing except to that research data set. Or I could carefully read the full 72-page privacy policy on every website I interacted with, think about it in depth, and decide that in fact I didn’t agree to those privacy terms and not use that site, or sure, fine, you can do whatever you want with my data and sign it and go about my business, because I was actually just trying to find a restaurant where I could have dinner in Calgary.

Opting out, anonymization, and individual notice and consent don’t work anymore.  They don’t work.  If I opt out of a data base, data base A, but there’s seven other data sets that have information about me, it’s the analysis across those data sets that’s going to help them identify me, whoever the ‘them’ is.

This is an enormous business opportunity, whoever figures out how to provide the right level of privacy in a big data age, the level of privacy that individuals will trust, so that they’ll continue to use these services, and they’ll feel rightfully protected, and they’ll have legal recourse, whoever figures that out – fabulous!  It’s a huge problem.  It’s a global problem.

At Stanford, we’re looking at it not as a business opportunity, but from a research perspective.  There, the ethics of various industries are being challenged by this fundamental shift to a big data infrastructure, everything from medical research to journalism, digital humanities. Michael and I were talking about humanitarian aid, the HRW example I gave you. Those are long-standing sectors that have codes of practice that have professional ethics imbedded in them.  There are others, but those are the ones we’re looking at.  We’ll probably expand to some others.  In this digital environment, they’ve got to reconsider their ethics of practice. That may be one place that some of the answers to privacy come from, but I think it’s also going to come from entrepreneurs who take the problem very seriously and try to come up with new solutions to it.

Within this whole framework of open data, there are at least three different kinds that I think about that I can see and that Michael and Brian are going to talk about in terms of types of data upon which one might build or think about a social enterprise or a social business.

There’s actual data on social enterprises, social businesses, and non-profits – the data of the sector, and Michael works with a lot of that information.  There’s data within social enterprises and helping the enterprises, the non-profit, how the business themselves use that information is an enormous opportunity. Then there’s the data that is generated from these enterprises, so what they learn, what they share, what they do, which may be part of a circle with the other two, but there are three different kinds of data.  You could think about them as three different raw material sets for what’s possible to build.   It’s an incredible startup opportunity if you can help folks make sense of any of those three kinds of data.

Michael Lenczner, CEO, Ajah: I’m the CEO of Ajah, founder of Ajah.  My background is I worked in the non-profit sector for 15 years; started an organization called Ile Sans Fil, that sets up and continues to set up free internet access in Montreal in different hot spots, serves hundreds of thousands of people – visitors and Montrealers, in different places.

We’ve written our own open source software and that’s being used internationally and that’s now a successful social enterprise.  It took us seven or eight years to get our first employee, to go from a volunteer-based organization to a full-time organization with employees.  Now we’re expanding to five employees this year, which is exciting.  I’m also on the board of Apathy is Boring that has a social enterprise part of it as well and I served for five years on the board of a capacity-building organization. So I really have to look at the sector and see the needs.

It’s from that that I started Ajah.  As a geek, I was doing open source and then community wireless and then open data since 2005.  I actually started Ajah because there was no funding for open data in Canada.  Back in 2007, 2009, the only funding I’ve ever seen for open data in Canada has been for my friend’s open source in Montreal by the McConnell Foundation recently.  I congratulate them for doing that.  It’s about time.

My company tracks funding to Canadian non-profits, and we use open data to do that.  Here’s the different people we track funding from.  Here’s the different data sources, so you recognize many of these as the funders of the non-profit sector.  We pull in provincial and federal data.

Since 2005, all the contracts and grants have been published quarterly on every single federal website.  We pull information from the Canada Revenue Agency.  They’ve been digitizing information for a long time and Lucy’s been one of the people fighting for that digitization to happen in the U.S.  We’re 10 years ahead of them and it’s ongoing, which is great.

Here’s an example of proactively disclosed information from Status of Women.

Here’s information on the provincial level. There’s no enforcement of this type of information being put online, but governments like to tell us what they’re doing with their tax dollars, if it’s going to things we think are good, so Alberta here has $4 billion and 80,000 grants put up on their data base and we scrape that and we just pull that in.

Here’s some more provincial information, completely different format, put up by Quebec.

How do we create value from this open data?  Example number one, it’s the service graphic called Fundtracker and that’s an online service.  There’s two competitors in Canada that are major. Imagine Canada, has a service, and there’s a company called Big Online or Metasoft. We use open data to give us an advantage on them.

Because our clients don’t care about us tracking open data, they care about us tracking funding in Canada, we also pull in information in other ways that isn’t government open data.  BMO lists who they give money to.  CCBO lists who they receive money from.  This is the Canadian Cancer Society. If you click on any of these, you get a list of everyone that’s given them money that wants to be on their website at the different levels.  We pull that information.

That’s supposed to look kind of a little bit scary, but basically it’s just a bunch of different data sets there that we pulled together, and we do name matching or we can batch based on numbers, depending on how people report these different things.

There’s a variety of formats and we present a really nice friendly interface that shows that information.  So this is from the CRA.

Here’s several hundred grants they received from foundations and sureties.

Here’s 15 different government grants they’ve received from different funders. Fifteen different government funders or 15 different grants received and then probably 100 or so different charities, so that’s really useful information for fundraisers.  That’s what they already do.  They look at their competitors.  They go to their websites and look at their annual general reports.  They use the CRA.  The thing is, though, they can’t look through 80,000 CRA records to see who reported giving money to that charity, so we do that.  It’s way more efficient to do it that way.  They can’t look through 22,000 websites for annual general reports.  We can.That’s the main service we offer.  We’ve been selling it now for three years and it’s going great.

Another way we can use that data is to help funders gain insight and we’re doing this now.  So this is what the world looks like to a funder – they are giving money out to a bunch of different organizations.  These are three different funders, and they’re all funding this organization and two of them are funding this organization. So that’s what the world looks like to this organization.  There’s a bunch of funders around them that they get money from so if you tie all these pages together, you can actually get a sense of what’s going on and we can show funders. We do this for the Fundtracker service.

Here’s funders that are like this funder because they grant to the same organizations, so they’re traveling in a pack. They might not know about each other, so we now have the website.  It’s where we’re offering our services.  You probably recognize a lot of these organizations, PFC, the Circle, CGN.  We’re using this data to offer services, data driven services, to most of the funding groups in Canada right now.

The third example is policy and research.  I don’t have an undergrad degree, but I’ve always been part of academic collaboration and really enjoyed working with academics.  We do that even though it’s not really a money maker for our company.

We’re listing per capita spending; if you take the Alberta lottery funds of 80,000 grants, we’re just mapping it on some per capita stuff.

Here’s our publications. We have two publications that I’ve done with Susan Phillips at Carlson.  This is the only master’s program in non-profit studies in Canada. It’s new, philanthropy and non-profit studies. Then there’s another publication based on our data that is looking at funding changes to religious organizations.

That’s how we use data and open data and government data for what we do. We’re able to do this is because I had a background in the non-profit sector and I’d been aware of open data since 2005, so you kind of see these different opportunities.  The lobbying groups that I’ve been a part of have basically only been about open data and getting governments to proactively disclose more data at the federal and municipals levels.

The future is there is a standard for international funders to publish information about who they give money to, called International Aid Transparency Initiative (IATI).  We want to take IATI and apply it domestically so that the same way that publicly traded companies have obligations to report certain information, we want to enforce the standard for domestic funders.  We want to create a standard that’s going to be very close to IATI and then have it be used by domestic funders.

We’re going to implement that standard ourselves.  We’re not going to wait for people to start publishing their data in IATI.  We’re pulling it all in ourselves.  We’re going to publish it in IATI. That’s our plan, we’re going to publish IATI on behalf of the federal government, on behalf of provincial governments, because we can.

I haven’t really talked to many people in the social economy.  We have an amazing group in Quebec.  If you want to use this approach to track social entrepreneurship, there are RFPs out there.  There’s data bases of our RFPs out there.  There’s data bases of awarded government contracts.  G3010 has the earned revenue.  There’s corporate registries.

I think people should use this approach and we’re working with academics to get them to use a quantitative approach as opposed to doing survey.  A survey costs a bunch of money and it works once.  The answers are sitting there in data bases.  You have to work with programmers to get those answers out.

Brian Walsh, Executive Director, Liquidnet For Good: My name is Brian Walsh. I head up Liquidnet for Good.  Liquidnet for Good is the corporate impact arm of Liquidnet.

I need to unpack both of those things. So what do we mean by corporate impact and what do we mean by Liquidnet?  Liquidnet is a financial technology company based in New York.  We have offices around the world.  We connect large institutional asset managers, pension funds, mutual funds, endowments and the like, to trading opportunities.  So we formed a community of 750 of the world’s largest asset managers who use Liquidnet, use our software, to trade large blocks of public equities safely with each other.

Our members in this Liquidnet community have, collectively, about $13 trillion of assets in their management, so these are the big players in this space.  We help them to trade so that they can protect their trades from predatory competitors, so it ultimately lowers the trading costs for these big institutions and ultimately adds to the returns to the end investors – the people that have mutual funds, pension funds and the like.

That’s Liquidnet.  My role at Liquidnet is sometimes I’m called the Chief Impact Officer.  My role is to leverage all the resources of the company, so not just our financial capital, but our human capital, our technology, our distribution network, our space, our brand, whatever resources we have as a company, trying to leverage all them to have impact.

We try to do that in three broad areas:

  • Locally: By “local”, we mean all the places where we have operations. In Canada, we have an office in Toronto and do some work there.
  • Globally: Our main project globally is a work in Rwanda. We have a youth village for 500 high school aged orphans, and
  • What we call “systemic impact”.

And our thesis on corporate impact, to distinguish corporate impact from the feel-good of corporate philanthropy or the look-good of corporate social responsibility, it’s not a rich mitigation play and it’s not a “let’s make ourselves feel really good play”  It’s really “how do we have as much impact as we can using the resources at our disposal?”

Our thesis around corporate impact though is that it can be the most successful and most impactful if you take the core competency of the organization, the core competency of that company, and apply it to challenges in the social sector.

Liquidnet’s core competency is using technology to make a market place more efficient.  That’s what we do very well.  That’s what pays our bills.  We’ve done a lot of thinking about how can we take that, that understanding of technology and that understanding of markets, and apply it to challenges we see in the social sector.

That’s led us to two initiatives.  One is Markets for Good, which I’ll talk about in just a moment, and the other is a series of investments to help accelerate the field in the practice of impact investing – but it’s all  at heart about “how do we think about the capital formation process across the spectrum of capital?”

Liquidnet’s in the commercial capital space, but we’re thinking about the capital formation process in philanthropy and the capital formation process in impact investing.

Markets for Good is our effort to imagine a social sector powered by information.  It started as Liquidnet’s kind of placeholder for how we started thinking about how we can improve the practice of philanthropy.  We became deep partners with the William and Flora Hewlett Foundation and the Bill and Melinda Gates Foundation. They joined this initiative and we launched it formally, publicly, last year at SOCAP, the Social Capital Markets Conference.

We are trying to drive a broad conversation about what it’s going to take to increase and do a lot of the things that Michael was talking about, increasing and improving the flow of data and information.  One thesis we have is that capital flows follow information flows – so if we want to have more money flowing efficiently to the most effective organizations, we need to have more information about those organizations. We also realize there’s a lot of other things that go along with that, behavior change and the right climate of incentives. What does it take to have a thriving society?  What does a thriving society look like?  Well, we need three sectors in society to be thriving:

  • We need a public sector that creates the right enabling environment. That means the rule of law, protection of rights, the basic infrastructure and security for people, a responsible open and democratic government, basic social safety net, and an effective regulatory framework – the rules of the road.
  • We also need a thriving private sector. These are the companies and organizations that provide innovative products and services that meet the needs of end consumers, provide secure and fulfilling jobs for employees, and ultimately that assist with financial value creation.
  • The third sector we need, obviously, which brings a lot of us here today, is the social sector – a thriving social sector that is helping to generate social value, that’s providing innovative sustainable solutions to market failures along with civil society, culture, human capital development; all those good things that are part of the social sector.

To have a thriving society, we need all three sectors in society working together effectively to create value.  My sense is that first, to get to this thriving society, we need more people who are bi-lingual, but also who are tri-lingual, who understand the cultures and the impediments and the frustrations and the pressures and the time frames and everything else of each sector and learn how to speak the same language and have a common understanding.

My dream would be, in ten years if we’re asking this question in this room, it wouldn’t be three people who have tri-sector experience, but almost everybody in the room.

A thriving society also requires thriving technology.  Taking a look at the private sector, the private markets are really effective at their financial value creation.  Why is that? Well, you have different actors in the private markets:

  • You have investors who make investments in commercial businesses.
  • Those businesses, those entrepreneurs in the businesses they create, they provide goods and services, a set of activities to customers and they’re very excited.
  • Those customers provide feedback, and the feedback they provide – not just feedback like writing Yelp reviews and things like that, but they also provide feedback by buying the products and service or not back to the business.

When the business has a successful innovation of products and services that are responding to customer needs and meeting needs of the market, the commercial business is successful and it can show that by profits and evidence of return on that investment back to investors. There’s the right alignment of incentives and there’s also the right feedback loops.

But it’s all built on a really robust structured and connected information infrastructure for this sector so people are able to seamlessly provide information – whether you’re a public company filling out your form 10K or your public rulings about all your data and sharing your data or if you’re a consumer you give data.  We have all kinds of data sets about market sentiment and the like.

We’ve raised the capture data from all these actors and a whole ecosystem of tools that help all these actors make better decisions. In the private markets, we’re pretty good at this financial value creation because of that information infrastructure that facilitates that knowledge sharing.

In the social sector today, we now have different dynamics at place.  So we have funders who are providing time, money and experience and expertise to non-profits and social enterprises, social businesses; who then provide a range of interventions, and those interventions can be goods and services, but let’s call it more broadly interventions right now, to beneficiaries. And it ends there – we don’t really listen to beneficiary voices.  We don’t capture that feedback.

If you’re a beneficiary of a non-profit, God help you, you’re just there to receive their largesse – right?  You don’t have an agency and you don’t have a voice.

If you’re a non-profit and you struggle to report back to funders; first of all because a lot of the funders don’t ask, but it’s also very difficult.  We don’t have the tools in place to demonstrate how effective your intervention was. We don’t have that information infrastructure that helps support and underpin the system. We have an unstructured information and knowledge base, so we have limited supply of information, but also limited demand for information.  Funders aren’t necessarily asking for it.  Non-profits aren’t necessarily incentivized to provide it, and, and beneficiaries certainly don’t have a voice.

This all leads to limited knowledge and an uncertainty about the impact.  We know that there is social impact happening.  We definitely have social value creation, but we don’t have a great tool set to really know for sure what that social value is and how we measure it and report it.

One of the other big challenges here is capital flows in this market are very inefficient and very costly. If I’m a business person and I’m raising capital to grow and expand my business, I can go to the capital markets and, depending on interest rates and the strength of my balance sheet, I can raise capital at maybe a cost of from anywhere from five to 12% of the capital I’m trying to raise. If I’m a non-profit and I’m trying to raise money for my organization, how high, what’s the cost of raising capital in the non-profit sector?  Yeah, 22-23%.  I’ve seen up to 40, 30, but any non-profit accountant worth their salt knows how to fudge the numbers and what’s overhead and what’s the program? It’s also not accounting for that 60 to 100% of non-profit’s executive director’s time spent on fundraising – it’s that kind of soft costs that aren’t taken into account.

The capital formation process in the non-profit sector is abysmal.  It’s wildly inefficient.  It’s very ineffective.  Also, the impact of the interventions is often unclear, not that we’re not having impact, but it’s unclear in how we measure and report them.

That’s the challenge we see today.  Markets for Good, it’s a broad initiative between Liquidnet, the Gates Foundation and the Hewett Foundation, among others – actually, the F.B. Heron Foundation just joined the effort – to really discover how the social sector as a whole can better generate, share and use information to improve outcomes and change lives.

Our vision is a social sector powered by information where beneficiaries are at the forefront, not an afterthought; where they have agency and a voice; where programs and interventions by non-profits and social enterprises are more effective and responsive to real needs; where capital flows efficiently to the organizations that are having the greatest impact and able to demonstrate that impact; and there is an overall dynamic culture of continuous learning, development, adoption and innovation.

How do we get there?

One of the things we need to do is to build a system of infrastructure to collect and share data and knowledge.  We need to have tools in place to allow all the actors in this ecosystem to generate data and to share it and then to be able to use that data and make better decisions.  We think that that’s the way to get to greater evidence of impact and also greater impact.

We need to take the limited and unconnected and dated information and we need to create an information infrastructure, classification systems, taxonomies, protocols, reporting centers and the like, that help us turn that raw data into comprehensive, comparable and timely information.  “Timely” is also key here. I heard somebody say recently, you wouldn’t cross a street looking at crosswalk information that was five minutes old, would you?  No – you want to know what that crosswalk says right now.

In the social sector we’re often looking at data information that’s six months old, 18 months old, three years old, if we have it at all. It’s very hard to make effective decisions without timely information.

What kinds of information are we talking about?  Now, Michael and Lucy talked about several types of information.  There’s several categories of information. One is the social issues, the baseline social issues.  What is needed?  What’s the market landscape look like?  What’s the market analysis, the baseline social indicator data about the different issues and the needs of beneficiaries?

The next is information about organizations, so their goals, their strategies, their activities, their operations, their finances, their effectiveness, their impact. Within the locus of the organization, that’s at the enterprise level data about those organizations.

Then data about the capital flows, about the resource flows.  They’re not just capital flows, but also volunteer time and human capital, all those grants, the donations, the investments that those organizations receive; the interventions that they deploy. How do we learn the best practices of these interventions?  How do we share them?  So how do we share those interventions?

Then, finally, outcomes data. What works?  The outputs, the results and the impact of those interventions.  What’s the collective learnings of the whole social sector?

These are the five broad categories of information that we think we need to capture.

We also think that if we capture this information it will lead to greater impact. That’s taking raw data, turning it into something that can be useful, that will help us have better insight, that’ll lead to better decision making, and ultimately leading to greater impact. It’s not just raw data for data’s sake.  It’s all about data for better decision making for greater impact.

Taking data from raw multiple sources, turning it into information, this is where open data comes into play.  We need policies in place.  We need the right enabling environment to foster that data to be provided.  I think the Canadian government sounds like it’s done a lot of great work on open data.  The U.S. government has a long way to go.

There have been some fits and starts in recent years, but how do tackle this upstream data issue?  How do we we set in place the right policies, not only for governments, for all of us, to make more of our data is machine readable and able to be part of the overall data system?

And we need open platforms to aggregate and organize and make sense of that raw data, kind of like Michael’s company.  Then that information becomes knowledge and then we need actual apps and services to power insight and help people make decisions.  It’s not just data information, but it’s actionable, and that leads people to make decisions that lead to action and impact. If you’re a funder, it can help make decisions about which organizations you fund.  If you’re a beneficiary, it can help you make a decision about what resources are available for you in your community.

There’s an organization website called Aunt Bertha. It started in Texas and it’s trying to go broader, but it’s helping beneficiaries search by zip code/postal code to find out what are the resources available for them in their community.

How do you make better decisions? What are some of the sources of data?  We talked about the categories of data and Lucy and Michael both talked about sources of data, but one is obviously governments – government data and government data sets.  Other data sets that are out there:

  • private;
  • semi-private;
  • data exhaust: data that comes from what companies, individuals and all of us use and give off through our day to day transactions;
  • data philanthropy, where companies and organizations will gift their data and make it publicly available;
  • data from communities themselves;
  • data from beneficiaries: there’s a great organization called Great Non-profits that is trying to be a Yelp for the social sector, so capturing that voice from beneficiaries, but also from volunteers, from donors, people who can write reviews about their experience working with non-profits and social enterprises;
  • data from organizations themselves;
  • data from intermediaries: like community foundations and the like;
  • data from funders: funding organizations whether they’re institutional funders or individual funders or governments;
  • data from practitioners: all of us in the room – the wealth of information and data in our own minds and we need better tools to help unlock that data and help share it.

Those are the sources of data.  Then we need online information platforms that are open that help us take those different sources of data, make sense of them, turn them into knowledge apps – different tools, websites and the like that help the different actors in the social sector make better decisions, which again leads to action and impact.

I encourage you to learn more about Markets for Good. If you haven’t already, check out the website, follow us on Twitter and check us out on Facebook and LinkedIn as well.  We are always looking for new contributors.

We blog about twice a week about different themes, different topics that are of importance to this conversation about “how do we move this forward?”  “How do we, really get to that vision of social sector power by information?”  “What are the impediments to us getting there?” “What do we need to do to go from where we are today to that future vision?”  We’re hosting that conversation.

We always love to have your insight and people’s insight from all over the world and across the social sector – and from other sectors as well – that can help us inform our learnings and have a kind of repository for collective intelligence there.  I encourage you to check that out

Question: I see open data as a fundamental building block for collaboration, but the majority of non-profits I’ve talked to, they see more barriers than benefits. For example, costs and all the plusses and minuses that come with more transparency. How do we overcome those?

Lucy:      Those are all absolutely legitimate barriers.  In some cases, the real challenge is privacy. It is far bigger than the social sector.  The social sector, there’s a lot of things they need to be thinking about,  but the privacy thing is going to be solved at a grander scale, if you will.

The cost issues are significant.  I think one of the challenges is, and this conversation sort of exemplifies it, is data for what?  You know, what are you talking about?  And we have deliberately, because we’re a panel at a conference, talked about it at the most macro level and, and actually not much at the enterprise level.  I think Michael, I think Ajah, is a great example of where access to data where data are open, there’s a third-party vendor intermediating it and cleaning it up so that the cost to the non-profit has actually dropped.

I’m not trying to punt an answer to the question.  I think that for individual enterprises the question really ought to be, what data for what purpose and are there ways for me to think about answering the questions I’m asking now more efficiently, using existing data sources or using third-party products?  I think there are positive answers to that question.

Michael: I’d say that probably 95% of organizations shouldn’t bother with this right now.  The question is, are you one of that 4% that could do something really productive?  If you look at the information flows that have happened in the for-profit sector, they’ve taken three centuries: limited liability of corporate structures, disclosure information. We need to do that over the next 50 years in the non-profit sector.

It’s not going to be one platform.  There’s not going to be one standard.  This is just the beginning of this type of work that needs to happen in the non-profit sector. It’s going to be very hard to report because we all do different things well. That’s why there’s going to be about a thousand different reporting standards for each type of work that is developed over the next 30 years by practitioners in that field.

Brian: Just building on that, because I think that’s an excellent point, in the commercial markets, the capital markets, there are some standard metrics, P&L statements, so you can compare a company, in a way, in very different fields, the different sectors, by using some universal benchmarks: how profitable are they, their return on equity, things like that.  But then in the business community, when they look at companies, they do compare against the whole spectrum of companies, but they also look within that specific sector and they have developed KPIs for those verticals so you could look at an insurance company and compare it to a retail company at their broad measure of how profitable they are. But then, within a retail company, if you’re comparing one retail company to another, they’ve developed some metrics like ‘same store’, ‘year over year sales’ and insurance companies are ‘how many policy holders’ and ‘assets under management’.  That’s their kind of metric.

Within the social sector, it’s hard for us to have a broad overall metric of social value creation, but I think that within specific verticals we’ll develop some really great metrics that will be organic and come from the bottom up and they’ll only be useful insofar as they help practitioners within that sector, within that vertical, make sense of their program’s operations.

Question: How do balance the need to have open data to enhance goals such as reducing environmental impact and the desire or need to monetize such information?

Brian:    One thought would be, how do you change behavior, and one of the things you can do to change behavior is you can give them a written report that they’re going to check off that they have and tuck it away or you can create a vibrant digital benchmark, help do some peer data analysis, make that public, and you have a top ten list of the top ten best companies that are doing this and the top ten worst companies that are doing this – making that a name and shame kind of thing.

Then the revenue model would be to provide consulting services, not just on doing the reporting, but helping them improve those numbers to get them off that bottom ten list or get them onto the top ten list.  The other best practice would be to take it out of the PDF and put the data in open data sets that can be analyzed and you could be the first person to take that data and visualize it and create some really robust tools.

Lucy: Another thought, and it’s a problem that organizations across the spectrum are facing, is to say to your 700 participating organizations “okay, you’re making this progress on these environmental goals, but what other goals are you trying to seek and how are you measuring those?” There may be governance related goals or other kinds of goals, and then you’ve got a set of what would look like ancillary metrics. You can begin the conversation with them by making your data about how these enterprises have improved their environmental practices and then find your peer organization who’s working with organizations on governance challenges, or employee relations or employee ownership or whatever it is.

That’s an opportunity where you’re actually saying, “Here’s the data we have.  Here’s what we know.  We’re going to track this over time.  How is it useful to you?  What can you learn from this?  What can we learn from you?”  You’re creating not just a set of secondary partnerships, but probably as Brian just mentioned, a whole new set of consulting or metric-gathering opportunities.

I wouldn’t worry so much about people running off with your data in its raw form.  Raw data is raw data, you know.  If I had had just the GPS locations of the restaurant I ate with last night, it would have not done me a whole lot of good. The question is, if you look at your data set in relationship to other people’s data sets and you bring those data sets together, what new possibilities emerge?

Michael: I think you’re asking a question that a lot of organizations are going to be asking: “I have data.  It took me a lot of money to gather.  I should be able to sell it to somebody?” The answer in the vast majority of those cases is there’s not.  Absolutely no one’s going to buy that data as such, especially with limited geographical coverage.  We have this great government data.  People were interested in it to some degree, but we had to go out and gather all this corporate data manually. It was never in our original plan to do that extensive data collection, using humans to do it as opposed to just computers.

Anil’s project as part of Innoweave, looking at how you can better present your data in online forms and different tools, I think that’ll help you look at your data and for a lot of non-profits. It’s exposing your data in better ways, getting more people to see it and then from that it helps you connect with your funders.  It helps you grow your connection to the community, and I think you can get the consulting practices and stuff off of that, absolutely.  I think it’s worthwhile financially to do that work.

Question: We are so far behind where we need to be on this issue. How do we create the culture we need to truly embrace open data?

Michael: There’s society lobbying groups for open government in a bunch of countries.  The Royal Bank has put a new grant up to promote it and open data in developing countries.  Brazil, I think there’s a pretty strong open data groups there. I’d try to get CRA, this type of information on, on corporate registries and on charities up to the top of the list and educate the data people, the geeks that are promoting these policies, to see if you can push this nationally.

In Canada, the dirty secret is very little of the data that we use as a company do we have the license to use.  We’re ahead of the U.S., I don’t know why, but just these two types of data we are ahead of the U.S. It’s been very fortunate for us as a company, but in general, we’re far behind the U.S. in terms of your open data work.

People don’t talk about charity stuff because people don’t know about this, these types of data sets and we influence those lobbyists, society lobbyists.  I think that’d be a good step.  It’s only going to work over five years.

Lucy: It’s really hopeful for us all to think of behavior change, but it doesn’t work that way.  I don’t think we’re aware of all the culturally imbedded intermediaries which mean data and behavior change.  So I don’t have any easy answer for you because I don’t know why the cultural structural reasons that Brazilians give at the rate they give now, but I think there’s lots of opportunities for experimentation and I think open data is one part of a new set of ways that the technologies allow us to connect as individuals more easily.

Openness about the charitable opportunities that exist there, coupled with crowd funding opportunities coupled with crowd engagement opportunities – it’s not going to be one of those things.  Plus it’s about  changing behavior at the top level among high net-worth individuals and, at that point, you’d have to do a whole lot of market research that’s specific to Brazilians to understand where in the menu of things that are keeping people giving at the rates they’re giving, where might it change.

What’s exciting about this moment then is that the cost of doing that is significantly less than it used to be. The cost of even asking the question is less than it used to be and the cost of failing in efforts to give people things they’ve never seen before and see if they behave differently around them is also less than it used to be.

Brian: Back to the earlier comment of “capital flows follow information flows”, if you can demonstrate early success and early wins, I think that can help validate it.  If your stats say that Brazilians give a tenth less than the U.S. – we have a different social contract, we have a different conception of the welfare state – so there’s kind of historic reasons why the U.S. has a more robust philanthropic market place because we have less government spending, probably overall. I would much rather start with the early stages of a social sector and building the right tools now, using the technology and the frameworks we have now, than our challenge in the U.S. of changing these legacy systems; that is a headache.

Question: What happens where there is a debate about what data means and it becomes difficult for the public to understand what the data means, especially in the context of the disparity of social groups to manage and analyze that data? As well, and on a related issue, how do you avoid data information overload?

Lucy: The first one is an enormous social justice issue and it’s an absolute fallacy to think that the simply having more data available is going to, by itself, address any of the power dynamics, any of the injustices, any of the social and educational disparities. In fact, what’s likely to happen if we take that approach is that the gaps will grow larger and so now is the time to address that: it’s a set of skills, it’s a set of insights.  It’s still statistics.  I hate to say it.  You know, it’s still statistics.  We’ve got to be able to interpret these charts. The one small piece of what’s changed that is opportunistic if used deliberately, it just isn’t gonna happen in the structures as we’ve known them, is the opportunity to listen and engage and involve the key data set that Brian listed here, which are the beneficiaries of whatever it is we’re talking about, is now possible.  It’s actually very possible to listen directly to the people that are trying to help, and in fact, engage them and have them co-design.

There’s nothing automatic about it.  It requires complete changes in organizational structure and human behavior and the desire for power.  It’s critical to stick your finger right on it now because if we firmly believe that if we just think the availability of open data is gonna close the power disparities, we’re fooling ourselves.  It’s going to accelerate the difference, left to its own devices, and it’s our job to make sure that doesn’t happen.

Brian: My preference is, and I have a bias toward products, there are some product solutions out there, some that exist and some are waiting to be created. When Michael was going through his presentation you saw these screenshots of the different raw data bases and he had that schematic and it was very overwhelming and very scary, it was like, “Urh,” you know. But then he said, “Oh, and now here’s the product and here’s a product to help you make sense of that data.”  That’s very powerful.  It all gets to the knowledge apps, the knowledge products that we need that will help us make sense of that data. We’re talking about opening up data, upstream data, so it can come downstream and turn into, to use the metaphor, completely turn into water that we can drink, so it’s not overwhelming us and drowning us in data.

There should be better products out there to help you navigate through all the data that you’re overwhelmed by, and I think that it’s just a matter of, if they don’t exist now, you should create one, start a new product, start a new business that helps make sense of the data for others. As Lucy said, she had looked at her app and it wasn’t the raw GPS coordinates. There’s a lovely interface of a blinking blue dot on a dynamic map that she followed.

Michael: Do you know Michael Gerstein?  Michael Gerstein is a Canadian professor who’s looked at problems that are caused by open data.  I don’t think everyone needs to take a Master’s in statistics.  I think as a society it’s great if you become more numerate, let’s use that word, but I don’t think that’s actually the goal for everyone. Specifically as a non-profit sector, the social economy, what should we do about this?  I don’t like it when governments take money away, there is a very limited amount of money they spend on open data.  I would rather that they spend time releasing more data sets than building interfaces for the public, personally, because they’re not good at building interfaces for the public. They’re not good at building software period.  I would rather they continue to focus on releasing more data and I don’t think the response to the non-profit sector has to be, well, we need 99% of organizations using open data.  We need to have some programmers working for the non-profit sector.  We need five organizations that are not teaching data literacy, that are not just 75% of the organizations.  They need to be doing data stuff themselves.  I’m not sure who’s going to fund that work in Canada because we’re pretty cautious compared to the foundations in the U.S., but I think that’s what needs to happen.

More About SEWF

sewfThe Trico Charitable Foundation was honoured to host SEWF 2013. It made history in a number of ways – it was a first for Canada and attracted a record number of speakers and attendees (1,000 individuals from more than 30 countries and over 100 speakers from 20 countries) – but we are most proud of the quality of the discussions on Skills Building, Social Finance, Indigenous Social Enterprise, Collaboration, Policy and Research, and Social Innovation.

A special  thanks to Photos With Finesse by Suzan McEvoy for the pictures, BizBOXTV for producing the videos, and Employment and Social Development Canada for helping to make this post-event coverage possible.

We would also like to thank the following partners for making SEWF 2013 possible:

Organizing Partners:

Lead Sponsors:

Presenting Partners:

Supporting Partners:

Friends of SEWF:

Media Partners:

Each year SEWF gives a different host country an incredible opportunity to celebrate and nurture its own social enterprise movement. The inaugural SEWF met in Edinburgh, Scotland. Since then it has been to Melbourne, Australia; San Francisco, U.S.A; Johannesburg, Africa; and Rio de Janeiro, Brazil. Learn more about SEWF’s history here.

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