The following are the outputs of the real-time captioning taken during the Twelfth Annual Meeting of the Internet Governance Forum (IGF) in Geneva, Switzerland, from 17 to 21 December 2017. Although it is largely accurate, in some cases it may be incomplete or inaccurate due to inaudible passages or transcription errors. It is posted as an aid to understanding the proceedings at the event, but should not be treated as an authoritative record.
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>> ‑‑ this is not big data, but the results, they were scary, anything, you know, you have to think about how the data is used and the way in which the conclusions work out in the way it is cooked, you know, speaking for a couple of studies, we had a fabulous confusion in the U.K. about drinking red wine. Red wine is good for you. They compared all of the health statistics and they looked at people who drank red wine as opposed to beer. Logically speaking it should be no different because the amount of alcohol, it should be the same, the results. What they were looking at was the socioeconomic is groups. The people that drink red wine tend to be middle‑ or upper class and the people that drink beer tend to be working class. What they were actually measuring, but failed to include in the study, is the general approach to healthy eating, whether you would have a salad with the wine as opposed to beer and those are the sorts of things that they need to take into account, the hidden pitfalls and barriers and lastly it helps us stay on the work.
As computers, robots, they do this, we're moving from an era from formal university learning to one of lifelong learning. Anything you can do to make it more accessible to people who are in work, who stay involved in the latest techniques, that would be really helpful because we definitely need to be more informed policymakers. There is a big turnover, a lot of people who come here and go back or go on, we certainly think we can help people provide more, better quality advice with less arrogance. We know this because my data tells me so, to encourage the healthy questioning and it is really good.
>> KATHARINE HOENA: Thank you so much. This is interesting. I like the emphasis on lifelong learning, which we're doing all any way I guess. We're probably more in the future, but are we asking right question to all data and what are the consequences of the data analysis and the implications of the knowledge that we have created with this data and to what end is it used? These are big questions. I would like you to give an overview of these risks associated with data analysis.
I would like to move ahead. I'll give the floor to Heather Leson, the Data Literacy Lead from the IFRC. She will tell us more about what the IFRC is doing in terms of developing their own training and capacity building in the area of data.
>> HEATHER LESON: First of all, thank you very much for inviting me to IGF.
We have completed in one year of the Data Literacy Foundation, we have 190 national societies, there is half a million staff across all of the national societies, there is millions of volunteers. How can we be data ready? That's a large mandate.
I have worked on data programs, data education for the last few years, I used to work in ET and I broke it down and found a wedge working specifically with the information management unit and the health unit.
Can you hear me? Is that okay? Sorry.
Basically I'm interested in feeding the data, working with the data and learn by doing. That's our MO in terms of it. We have four streams of the activity, and so today what I will share with you is three of those streams and then some of the challenges and opportunities embedded in terms of the curriculum we have done.
A couple of years ago Bloomberg had a really great article on what is software, code, software for managers. It is a long read. That is the kind of version I'm hitting at trying to reach managers as a senior person in our organization. They say I don't know how to read a map. First of all, thank you for telling me, second of all, let's solve the problem. There are people that don't know how to add a column to an excel diagram and people that say can you please find an online course for me. Our program is around building the data culture, that's the first element we have a lot of leadership in terms of data, a lot of activity, super talented people in our house.
The first thing I did, we checked the Informal Data Working Group in the Geneva office and asked do you have skills, what do you want to learn, share, and then had informal sessions. I had 25 throughout the year. Some of those sessions have been remixed in other countries, including a basic data issue, no laptops, computers, humans, pieces of paper, critical thinking. This is a course that my colleague has created and that's been really popular. So just trying to figure out how to help people, discover a channel of learning.
While some of this stuff may sound really fluffy, on the other side of it is that we can ask and give people courses, but if we can build a culture around learning and a culture around data skills where we have the finance department teaching the health department with mad mobile skills, teaching them different portions of it and we can level our organization, meaning constantly there, building different aspects of it.
The biggest priority my first year was to build organizational trust and confidence. You've got this. Part of asking people to just take an online course is really ‑‑ it is really generous. Some people learn that way. We have to think of how others learn. A big thing we have done is try to match people. If they have a data mentor, right, that's really important in terms of leveling it.
The second thing, around curriculum development: And when it comes to curriculum development, there is a lot of activity in‑house. I don't want to recreate this. I'm not really interested in creating the how to use Excel. Excel and Microsoft has amazing courses there. It is about picking and choosing people self‑identify that.
A course is a flat‑earth model. You need the people around it to build the culture for it. That being said, we're building curriculum. I have user tested a curriculum ecosystem map and ‑‑ again, I went through the same tactic, asked what they wanted to learn and what they could share.
In Nepal, in a small branch, there was an astrophysicist teaching people mobile data collection. Honestly, he's got this. He could teach the whole country. He's very talented.
First and foremost, who are the leaders? How can we give them the resources? And there are some branches that just don't have access to the Internet, the digital divide is a barrier for us to reach people. What they told me, Heather, create the curriculum, the data stuff you have done, the data manager stuff, the data sharing 101. I have all of the courses and exercises for it, responsible data, data protection 101, put it on a flash drive, that way we at least have it.
Thinking through what people's workflows are depending where they live is super important. We're taking these ‑‑ I have about 30 modules created with people, user tested with their community across the world. Those modules have been tested in northern Nigeria by one of my colleagues. They have been tested in Honduras, they have been tested around the world. The goal is we basically tried to figure out what the skills are and what we need to learn and put that in the package. We have a starter kit of the big data playbook, and that data playbook will go in and have next levels up.
What's important, it is created by the community. Right. Again, they're super‑talented people that cocreated. We have user tested it with the folks, and now it is a matter of publishing it. I created a way for people who are going to the field can take it.
Heather, I need information management, someone has created that. We created a means for them to find it. Discoverability is super important.
Lastly, none of this is possible if we didn't work with partners. That's why I'm excited to live Geneva. The idea of a curriculum partner is important. We created this stuff for our staff. I believe there is a higher ‑‑ I have worked in the Open Source world for many years and there is a shared curriculum that could happen. Just because I created data for managers does not mean that we could not take up the humanitarian examples with other examples. I think there is a huge opportunity for this community to do that. We have created partners with missing maps, a global community of map makers where people learn about humanitarian response while making, building a map. We have worked with some people with amazing free courses online, data basics.
You were talking earlier, Phillippa Biggs, about asking questions. They have a whole module on asking questions. We did that in our workplace. They have one about raising questions and trying to build a response. We have done that. That will be more and more pushed out. I would say if you can find the partners that already have the curriculum, great, there is lots to be done.
We also partner with humanitarian data exchange and are working through the workloads of how to share data. While we can talk about the datasets and how much data we have and big data, what we love is small data. Small data is important to help people do their jobs. If we work with partners to train and do that, and we have done it in some other locations, building the common curriculum that we can Co‑Chair.
Just to say that we have had a number of challenges in terms of building out the curriculum. I'm one person. That's one challenge.
Second challenge, ‑‑ how I deal with that, it I rob and steal from other departments, can I borrow this staff member? You find your allies. The skills and competencies are so dispersed, for someone senior telling me I don't know how to read a map and another junior person asking for help with a spreadsheet, we need a service desk model of more people training and understanding and helping with that data vulnerability.
Building the global spaces for people to have content: As I said, I have to put things on flash drive. We're testing the online course platforms. People learn different ways. I created scenarios and session designs. A session we have is walking through the dataset to make decisions and to ask people what roles and responsibilities and risks. So having a sense of play while building things is really critical for us.
Translation, technology, and lastly data is about feeling. When I say data is about feeling, it is about a feeling of change and a feeling of can I do this, what is my ‑‑ what is my responsibility to that, you know, will I be respected if I make the wrong decision, the human behavior activity which is why I focus so heavily on culture and while I say that curriculum is really important, curriculum was only as good as the people around it. I think that's why you need a network.
Thank you.
>> KATHARINA HOENE: Thank you. It is very, very interesting to hear very practical experiences from you, from developing this curriculum.
Two things I picked up: The importance of building a data culture in an organization and the other thing, there are very different needs within each organization and the question is how are we meeting the needs, how can we meet the needs within one curriculum. In that sense, is even one curriculum even enough? Having said that, I would like to hand the floor to Pierre Mirlesse from Hewlett Packard sharing his perspective and that of the business sector.
>> PIERRE MIRLESSE: Thank you.
So Hewlett Packard Enterprise, and in my role I consult government, nations and cities about their IT strategy which stands for Information Technology, information, it is really becoming core to the functioning, the proper functioning of governments, cities and nations.
Other the past year as we have lived through an explosion of information and data thrown at us, you can see this as an opportunity or as a real challenge. If we back up just in 2011, the German government came and talked about industry 4.0. If you look at the nature of that term, it actually is all about the automation of the industry and the injection of a lot of data that transforms industry. I think we have come to a place now where governments and cities are getting to their own revolution so it additional destruction which is based on obviously a lot of data, more data that's available, it is all of us creating the data with our mobile moans, over 50% of the Internet traffic now comes from mobile phones. We consume information from mobile phones but create a lot of information, it is not just the pictures we take, but also the location where we are, it is the interaction we have with the services we consume from government or private sector. That information and data is becoming bigger, we talked about big data, but it also has other attributes and it is becoming richer, richer information in the form of self. It is not automatically structured data. It can be unstructured data. It is also said this information is forever. There is right to be forgotten obviously but data tends to be persistent until you actually decide to delete it. Another interesting factor that contributes to that explosion of data is the everything connects. It is not just us connecting with our friends or business partners, it is machines and things connecting between each other to not just recreate the information but to make new data out of existing data. That's what machine learning dis, Artificial Intelligence, making decisions based on the information that's analyzed and ingested.
Another element fundamental to industry 4.0 or government 4.0 is the need to have immediate response or immediate action based on the information that's ingested. Take, for example, a city that feels that it has a lot of traffic or pollution. Immediately from the sensors you can have a reaction to reduce the speed limit of the cars in that city. If a city has nobody walking through the streets, the sensors can feel that and decide to reduce the lights in that city. If you want that city ‑‑ the city to consume less energy and at the same time it becomes almost like a living organism that immediately reacts to the information it collects.
Information is exploding. It comes with the challenges of privacy, data sovereignty. It comes with challenges of security, am I at risk of being attacked in something that creates prosperity for my country or city? Do I put at risk my citizen with the information that is core to the way that I function either as an industry, a city, as a nation? There are big opportunities as people depend more on data to create prosperity, but as well as big risk because that data, that information, it is being targeted and it is a potential threat to the functioning, the proper functioning of societies.
>> KATHARINA HOENE: Thank you for giving us a sense of different kinds of data we're dealing with in this data explosion and the kinds of implications such as privacy and data sovereignty.
Last but not least, our final panelist, Sophie Huber was able to join us. She's the director of the center for continuing and distance education at the University of the Geneva and I would like to hand the floor to her. She's sitting over there, so if you can direct your attention here, please.
>> SOPHIE HUBER: Thank you so much. The snow fell for the first time in Geneva!
Listening to you, I realize that the way I framed my short introduction this morning actually very well connects. Actually in late 2015 somehow once upon a time the University of Geneva, we started to put our heads together on different disciplines and colleagues, professors and researchers, some of them having nice connections to enterprises, to institutions related to national institutions, IGOs and we were wondering actually how could we better actually answer training needs.
From our point of view and this is where we started, we had three lessons and there was one (?) (too far from microphone to hear clearly).
The man is the center of all digital action. So in our thinking, we realized first that a lot was happening at the University, especially terms of continuing ‑‑ addressing all of the professionals that were already on the job and had been five, 10, 15 years on the job, for some of them, they had already a University background, so they were at the University in different disciplines, faculties, of course, with our colleagues from societal informatics and in health and in information and social sciences, et cetera, et cetera so that was really lesson number one, like I said, there was a lot already within the institution to offer as training.
Now, lesson number two, it was that we realized that the data officer or the data scientist, as we coined it at some point, we were really talking about one of those men, someone who on the one hand could collect data, use the message really well, knew what technology was and available, new what questions to be asked, that was one hand. The secondhand of that man or woman was how to protect the data and that person would know the law in the institution and the technologies that were out there to protect the data and possibly as in the E.U. GDP regulation would also be the future link to an authority.
Collection and protection of data, two elements, the two last ones, they were analysis of the data, of course, again, that perfect man or woman would know all of the methods and would also understand the purposes of asking questions to that data. The purposes for business development, purposes for policymaking again in health, maturation, et cetera, et cetera.
The last branch or arm of this man or woman was communication, of course. Communication internally or externally of what came out of this data gathering and data analysis. Again, the perfect person would know exactly the tool, the techniques to communicate, know how to visualize and know specifically the essential messages to address the two different sets. Really a perfect man or woman. That was the data officer we had in mind.
Then sliding calculation, we realized that, of course, we had the training there, but adding them all so that the idea of man or woman could be created, it would be a really hard process, about three years of training, even part time a good three years. So it was just impossible. We could, of course, reformat, compact the curriculum, we could try to change the formula, putting all digital, but there was also another thinking on the way and that was when they use curriculum, know better about the training needs of the people coming to the University and understanding them and let them somehow create a sort of personalized curriculum within that offer making sure that on their way that it wouldn't be just a patchwork of elements but a structured learning journey out of which they could come with diploma if needed, it exists, diplomas of continued education, diplomas that could be valued as credentials.
Somehow, ‑‑ (audio interference). They may not be, what may be needed is actually more mainstreaming of capacity in network's organizations, where a number of colleagues knew actually that it was part of the competencies and capacities, why not the same time understanding what their colleagues need and it would be a team of women and men, not just one person.
Thank you.
>> KATHARINA HOENE: Thank you for reminding us that this is a multidisciplinary effort, that we already have a lot of data, but also a lot of expertise in our organizations and more importantly, if you want to combine all of the necessary skills in one person, we're looking for a perfect person that does not exist as such.
Thank you to our panelists for giving us a very, very good overview of the kinds of questions, kinds of issues, the kinds of key points we need to keep in mind. It is very broad, as expected.
With that in mind, I would like to open up the floor directly to questions from the audience. What kind of thing struck you as particularly interesting? What would you like to follow‑up on? What's your own experience of learning about data analysis, integrating data into the organizations?
>> AUDIENCE: I note we don't really talk about with data, that is the middle that we receive within the data, what we see ‑‑ what we generally talk about data, the glorified data, that everything is so accurate, everything is so perfect and there is no margin of error. When we see that there is a lot of error within the data and also when we analyze the data, and also the error when we make decisions about the data. My question is in general, for example, HP, the other organizations, working on the technologies, if you have considered including the level of error, not in terms ‑‑ how does that impact our decisions at the end?
>> KATHARINA HOENE: Thank you so much.
This question is for Alberto Pace.
>> ALBERTO PACE: I'm sorry, what was your name and where do you come from?
In terms of error, I think it really depends on the type of data that we're talking about. For example, if you take face recognition, there is no certainty. There are systems that allow you to capture images of faces that could be in a city to help accelerate the capture of somebody, but there is never 100% certainty of facial recognition, it comes to more certainty. It is a bit like translating software that we use in front of us, it is never 100% perfect, it is close but it is never 100% perfect. You have to keep that in mind with data. There is perfect data out there, there is data that's binary, well structured, understood. I think things that will improve on the decision, it is computation. Everything will compute, over time more things will compute to bring a higher level of certainty. Everything will connect. By the intercorrelation of multiple data points, you will be able to have more clarity on what you measured. Hopefully, you know, the IT industry, all of us, will aim to have everything understood. When I say have a better certainty, take the GPS for example, you know, initial findings of GPS and precision, it was not very good. If you're able to have intercorrelation between GPS and other systems, the cell towers, then the position, geographical position, it will be known very easy so the increase of measuring device will allow us to have more precision on the things we're measuring. Never 100% certainty.
>> KATHARINA HOENE: Thank you.
Anyone else?
You want to comment?
>> Sure. Thank you. Perhaps when you speak, if you can push up the volume if you're a fast talker, that's always helpful.
When it comes to, you know, reliability of data, we have to go back to the data users. Right. An example of a surveillance program we have used for response and so the community itself got feedback on the quality of the data. This is what's accurate, this is not what's accurate. When thousands of points of data is educated from are the front, then you can get accuracy and improve the computerization of it and the use of technology. If you don't have the front end then you'll still have a data quality issue.
I think the important thing, when we talk about data consumers, producers, when talking about community, creating the data, for example, when creating data, you don't tell them what you did with the data, you tell them why you're taking the data. You don't tell them how to improve the data, then you won't be able to get to a point of accuracy. It is about educating around data quality as well as working with the systems and tools. Software is software. If you don't have human behavior addressed on top of it with that, then you'll ‑‑ the software will detect that there is a problem but you'll still have to have the human, the human advocation protocol if you will to go back in and recharge it.
I do think quality software can show us where the validation points are so that we can go back. As you look through that, you can get reports, okay, this is where you need to improve. That in itself is why we need both.
Thank you.
>> KATHARINA HOENE: Thank you.
I think we have also questions from online so let us bring them in. I'm sure they have been waiting for a while.
>> We have a question from the online participants, from Laurel, she's asking could we make such a data curriculum mandatory for all government members?
>> I was at a meeting a couple of weeks ago and it was said that when you come to the organization you have to learn how to change a tire. I love that. We need a data ‑‑ for data, it is very important for everybody in humanitarian field to know, everybody in the office needs it, it what is the onboarding for that? I would say the curriculum discussion really is important, and while we go through the HR protocols of bringing people in, it has to be sustainable, what's the safety check, how do you update the data skills? These are things we can kind of do organizationally and so, yeah, the government needs to have data skills, I think everybody has different kinds of data skills and it is just first of all checking that and helping them rather than penalizing people.
Thank you.
>> KATHARINA HOENE: Thank you. Any other questions from the floor?
Let's start over here.
>> AUDIENCE: Hello. I'm Ernest from Freedom House.
I'm wondering about ‑‑ I have no fear about the future, children engaged with new technologies and spending more time and we can correlate those numbers. At the same time, I was wondering about the transparency and the perspective of the future, how the private sector sees the provision of the transparency of keeping data safe and how to ensure that it is not being misused in such a manner of data mining, harming or affecting the behavior of the consumer.
I was wondering actually it is not the government, not also for private sector to keep the data safe but it is for everyone, for public, the transparency provided.
Thank you.
>> I don't know if I can speak for all of the public sector. I would say it is our joint responsibility to put in practice some data protection policy, the government from the law, from the government standpoint, that's the case in the E.U. with the GDPR for example, the data residency, but also for the private sectors. I would say, you know, there are Best Practices to be thought of for cities, governments, organizations such as having a CIO, a chief information officer that's on top of those notions, that is able to guide the government through the proper curriculum, I agree, there is a need for a curriculum, there is absolutely a gap of understanding of the challenges and opportunities that data and insight coming from the data can represent for a nation.
I would say it is also important beyond the curriculum to have a board, an advisory board, collaboration board inside of a government, inside a nation so that people share the ‑‑ we're talking technology here, the fundamental about technology, it evolves over time, the threats and capability.
The government, they need not to just have a good understanding of what technology can provide today and what is the governance today, but also to keep an ongoing conversation between itself, the different stakeholders and also with the private sector to understand what new threats and new protections you may have and what new capabilities can generate new possibilities. Data is the new currency for both the good guys and for the bad guys so we have to work together, governments and private sector, to be able to come with right policies and make responses.
>> KATHARINA HOENE: Thank you. It is a different mind that data curriculum is needed and then the question of a chief information or data officer.
There was another question all the way back. Go ahead.
>> AUDIENCE: I'm Johnny Pen from the University of Cambridge, and I'm a Google Technology Fellow from the European Fund.
I'm on a bit of a fact‑finding mission. My question is it seems that in Civil Society, we're asked to prototype solutions for data management, you know, all across the continent, around the world, it is a waste of energy because people are working on the same problems and they're not ‑‑ they maybe aren't able to share their solutions without a standardized language. Have you come across anything like this, a proposal on how Civil Society can standardize techniques that we use and all benefit from each other's work and all grow equally.
>> AUDIANCE: I think in science it depends on the sector.
For example, in physics, there is very, very well consolidated standard where the worldwide physics communities is working together constantly sharing data using well consolidated standards. This approach has ‑‑ it has been many years to expand this approach to biology and medicine. Of course, the major problems in this approach, in the sharing of the data on consolidated standards is the respect of the time. Immediately, the science asks a hospital to have data from patients, it becomes very, very easy to understand that even as the data is there, the technique of big data to, really underpinning the validations, it is sometimes rather easy to avoid the effort of making the data anonymous. It is a good example where clearly there is competing interests and there is the need of maybe science will have access to all of the data and the need of respecting the privacy of the organization.
This comes back to the previous question, where maybe ‑‑ there may be competing interests between what is the private sector and what you would like to do with the data processing compared to the privacy of the individual would require. That's where the importance of the curriculum of the government of the people ‑‑ of the people in the governments area is very, very crucial to decide how far things can go and where they should stop.
>> KATHARINA HOENE: Thank you. That's a really important question. We have several panelists that have something to say. Thank you for the question.
First I would like to hand it over to Phillippa Biggs and then Heather.
>> PHILLIPPA BIGGS: That's very intriguing, the question. Mainly because I'm not entirely too sure what or who you're referring to with Civil Society. They're not that the organized necessarily, they don't necessarily have the data capacity or resources. Obviously in the U.K. a whole bunch of medical records were handed over without consent to patients for a very noble purpose, kidney failure diagnosis, but, of course, there was a lot of other information in the records and you can't entirely be sure the purpose that will be used for.
I was interested in doing a DNA analysis recently with ancestry in the U.K. and they have a nice, long legal agreement entitled privacy agreement so it is very good, feel good factor. When we read this agreement ‑‑ which took some time ‑‑ basically the whole gist of it is that you're passing across your biomedical information, your saliva sample would go to the states where it would be held indefinitely, wouldn't be used for the purpose stated and then destroyed, it was going to be held indefinitely and then they guarantee the privacy of the sample so that the time which they owned the subsidiary in the state, they assume that subsidiary will be bought out, the sample and the ownership rights and the information associated with the sample would be past to that company.
Basically the net affect was you were affectively signing away the privacy right to DNA information in an agreement that was clearly stated, privacy agreement, and of course we don't know to what purpose that sample will be used.
We have lung cancer in my family. I thought that would help find a cure for lung cancer, I would be the first to volunteer my sample if it’s to design the next generation of bioweapons, biological warfare, no thank you. You don't get to actually decide what the information is used for, and once it is in the public domain or the private domain, but to be bought out by God knows who, so a lot of this privacy, it is a lot ‑‑ it is almost ‑‑ I dare say, it is a lot of false solutions, creating feel good factor among users when in fact you're entirely in arguments with the lawyers over who owns what.
>> HEATHER LESON: I think your question is really about standards and sharing.
In my background, I have been around a lot of different Civil Society groups. I guess when it comes to standards, I think that the humanitarian data exchange, with the humanitarian exchange language, there is a taxonomy available for how we share the data and the practices for the number of organizations that have datasets there in terms of being transparent is important. There is a data trail included with it. You know what the agency is and where it goes. It is important to anybody creating a dataset to have that data trail there.
In addition to other standards out there, there is more education happening. I really love what Civil Society is doing, they had a digital world tour, all of the videos, they're online, they're around the world talking to activists, Civil Society, government, business, they're all in the same room. I went to the London event tucking about the shared curriculum, what are the practices, lessons learned, the Best Practices around data and data use that's really important that organizations and universities get on board and work with those partners and researchers, absolutely critical, and foundations, of course.
The other groups that are doing really good in terms of education, responsible data forums, MIT Media Lab I already mentioned, Welcome Trust is working on a sharing agreement around healthcare data and how do we share it in the HCR, they're working with us, we're working on how do we share data and the protocols of it. That's why I created the modules around what is the questions we need to ask before you have the data, who is responsible. All of this to say is that there are a number of different avenues and organizations that are doing that, but there is no one place. Another one is Civicus, a massive Civil Society organization with a whole program called Data Shift, and they work with a group engine room so all resources are there, the problem is that I just listed off at least 15 organizations and I can do that because I have worked in this space, but if you're a smaller NGO, a small Civil Society, you kind of need a guidebook for all of the tools that are out there already and how do you access it.
Thank you.
>> KATHARINA HOENE: That's a really good point about a guidebook. That may be needed to navigate, but not even the data or the data elements but the organizations that actually deal with the data analysis that you can draw on.
Other questions from the floor?
>> AUDIENCE: I'm from the University of Geneva, and I want to ask two questions of the panel:
First of all, thanks to DiploFoundation and Geneva Internet for putting the panel together. This is a great opportunity to hear from a diverse group in Geneva.
Geneva, we're a small city in Europe. In the other sense, we're extraordinary with all of the international organizations here and we at the University of Geneva are in the middle of a strategy process thinking what can we do better in terms of our education offering for international Geneva, for many on the panel, in the room.
The two questions: I think it is nicely illustrated here that there are certain silos of knowledge within our city here and what could we do to be better to break those down and collaborate on the issues of data, data governance and data officers.
The other question, outside Geneva, who are the partners both to be collaborating with and whom should we offer education to around the world, what should our role be? These are two questions which I would like you to respond to.
>> KATHARINA HOENE: Thank you so much. Thank you for bringing us back to international Geneva and a specific context that we're operating in here and the Specific Needs related to that.
I hand the question over to the panelist.
>> I think, you know, this is absolutely a fantastic question.
As you mentioned, in Geneva, there are is incredible talents, great minds coming together. I think an institution such as the University of Geneva could bring us altogether to think about what would be right curriculum for states, governments, thinking about how data information can improve government resilience, resilience in the world with increasingly using data as an asset, a weapon, how can government improve the prosperity, as I mentioned earlier, data is currency and creates wealth, but also it is vulnerability. How can we collaborate better? Thinking about standards of collaboration, how data fits into this.
You know, if you take it one more level, Geneva is one of the three headquarters of the United Nations and the United Nations goes with the SDGs. What is the role of the data in the SDGs and how can we come together between Civil Society and academic think about providing right curriculum and right guidance for some of the leaders when you think of the SDGs and how data information can help them tackle the challenges.
>> SOPHIE HUBER: Thank you for the question. I would like to say two elements that come into this and brings it down. It is fascinating how we all think of a big system and how the ecosystems are actually, they are competencies and experiences and standards, et cetera, et cetera, out there. When we try and change the ecosystem from passive to active, that's when we actually come to the crux of the organization, within the ecosystem, different actors work with very different purposes and under very different conditions and somehow, especially under the SDGs, if they want opportunity to move beyond those, you know, see only the finer point of the institute, that may be fine. I have been working for I would say ten years now in international Geneva, always from an educational point of view, trying from time to time different opportunities to modernize the ecosystems, changing them from passive to active, and it is a lesson in humility always on how difficult and how much energy you need to put into an ecosystem to create momentum and keep it on. Right now there is one experiment that's not related to international data but it is a project from an NGO that started to modernize the ecosystem and I'm quite ‑‑ I must admit ‑‑ really am interested to see how much the ecosystem will be modernized. I feel there is a need for a strong, very positive external leverage to mobilize the ecosystem. I'm wondering whether we within international Geneva are enough to do that.
>> It is a very interesting question.
I'm not entirely sure ‑‑ I think the U.N. has a very well organize the, a lot of statisticians working on the data collection for the SDGs. Other than providing sort of general statistical capacity, that's a very, very huge process. I'm not sure I see a huge need to step in there.
What I think would be most interesting in taking on where the data is a couple of the mindsets, so the fact that training, boring, obligator, I have to do it but it won't be relevant to me, that would be good to address. The fact that statistics is some kind of weird special thing that involves users of learning and you know, only mathematicians could do it, that would be good to address. Basically, I personally would see the biggest need in ‑‑ there's a lot of admin jobs in government, in Geneva, that are going to disappear in the next 15 years. Those people, once they leave the workforce, it is going to be very, very difficult for them to get back in. Honestly, if I was thinking about what Geneva could really contribute, it is not really worrying about the mathematicians, they're working with the merchant banks these days, they would talk about 50 big PhDs a year, now they get 49 and they are doing a stupid start‑up with a boyfriend, girlfriend and only one goes into working on big questions. So University of Geneva, yes, you could cater to the advanced Doctoral students, but honestly, I would think those actually ‑‑ there will be a very big need for your middle class admin person who knows how to handle basic bookkeeping, software, but not much more math, and if you train them to the fact that it simply won't be there by the time that Artificial Intelligence takes over and what will they do that they could contribute, that's a really big question that a lot of societies, including rich‑banking Geneva, will be facing in the next ten years. A little bit of advance would be very, very helpful.
>> KATHARINA HOENE: I'm scared to work in a bank now. I never would work in a bank. I worked in an oil company, an IT company, not a bank.
In relation to your question, how do we work with international Geneva and globally, I'm glad you brought it up. There is a thing called the Internet. I think we have to learn the Internet, we have to work like the Internet in some ways and with technology, I should be clear that there are humans that use that, here we are at the IGF honoring that.
I think there is four approaches. One is about discoverability. I listed off a bunch of organizations with training materials, but we can't necessarily act with that. If you're in an ecosystem and you have a map of what's available, vetting it, able to say, this is all created, what else needs to be created is the first step. Second, about modular learning, so I have created a bit of that, people can say, you know, Heather, I need X so I have something on that and I can go to look to people to create it. We have a lot of network in our organizations who have things and we ask in our organization and act externally. Those working in data literacy or education, it is a small town, and so I think that's exciting because things like this data is the focal points and we're working in humanitarian response and working with other groups.
Yesterday I was at a Chamber of Commerce meeting and they're all thinking about digital literacy, data literacy is one of that, there are large institutions working on this, we can partner with them.
The third, social learning: As one of you said, there is a lot of talented people in Geneva, I never hired anyone to come in and do a data training course. We have used our own capacity and what I have done, I have found those people and they self‑identified or we hunted them down and asked them to do a training class and that social learning and skill sharing was really helpful to the organization and International Geneva could help us with that.
By all means, bring in experts, but also think about what is your network of hey, do you know how to do this, that, I have a quick question. The U.N. created something called the Information Management Working Group, there are 600 of us on this Skype group, its very loud. If you had a question on information management in the international space for humanitarian response, there is a network of people doing that. That's a beautiful gift.
Lastly, none of this is possible without a bit of community management and engagement. Somebody has to be the fire starter. Somebody has to find those ambassadors and make sure that there can be norms that are available that somebody is not over talking or that they're minimizing skills or whatever, you know, as we go through so much change in organizations, as Phillippa Biggs said, we have to be ready for the fact that this is going to be hard for some people and we want it to be a generous, fun thing but will be able to help organizations modernize and be ready for the risks as Pierre said.
Thank you.
>> KATHARINA HOENE: Thank you.
As the session draws to a close, last chance for any questions from the floor?
Then I would like to hand over to each of our panelists for a kind of final word, reflections on things discussed this morning, on their own experience, maybe things that shifted in their minds after coming together, having the discussions on the kinds of things they conceive as the next step for all of us or as the next steps for themselves. It would be great to hear from you as a final reflection wrap up.
We'll start here and go around.
>> PANELIST: Thank you.
I think I'm going to leverage the idea of changing one specific mindset, which is learning is not boring but fun, it brings you forward. Of course, you need to know why you're doing it and see some return on education. Maybe there's more on us to prove again that there is relevance in coming back to University, there is relevance in spending time back into learning, and they have impacted, it is not only for yourself, for the career, and there is impact for your family, for your community, for ‑‑ as a greater good.
On that I think we can definitely go and be better and do better while at the same time making yourself as flexible as possible and going to some sort of coaching, someone coming in rather than you know, putting them in that curriculum.
>> PANELIST: Mainly I take away a lot of encouragement and I'm very heartened that at brain power around this table is clearly intent on collaborating and each party bringing what they do best to the table. I think this is a very promising initiative and I would love to follow it will more closely.
>> PANELIST: Probably what came out from the last questions was the importance of education in this very quickly changing environment. We have seen the data is more and more unstructured and reliable and then we use computers to improve the reliability of the provisions, but awareness that the predictions could be false, sometimes it is forgotten. There is a process that takes what the computer has concluded as the best probable issue as the truth and sometimes this is wrong especially moving from statistical numbers which are certainly correct into other cases and this is where the awareness of the governance to protect the individuals is essential. There are many, many examples where things can go wrong and people forget.
Statistics shows that in Switzerland the majority of people speaks German, but this does not mean that everyone in this room speaks German. There type of generalizations that are applied to the individuals that can have a very, very dramatic consequences.
>> PANELIST: For a final comment, I would like to leave with some thoughts and recommendations maybe.
The first one, train yourself as a leader that you are in your organization. It is important to personally invest time into your own education. This is a very important one that will drive your organization, your personal growth or not.
The second one, as a leader of your organization, being part of the team, it is create a framework for innovation. Data will provide ‑‑ again provide ‑‑ prosperity, can provide better resilience of an organization and that framework should be one that is open. There are doubts of information and cities that it should be open. When you look at the most successful cities and nations around the world, those that have approach and an open way to data, those are the ones creating success for their cities. The third one, we should not be scared. I heard the comment earlier, oh, my ‑‑ you know, my scientist, they want to go and create their own start‑ups. You know, start‑ups do solve big world problems. They actually create solutions in collaboration with government and with Civil Society to address some of the bigger challenges. Some of the biggest companies in the world today were just start‑ups ten years ago and they have created prosperity for many folks. New jobs have been created by data that didn't exist ten years ago. Data scientists, RT architects, technology offices and there are some low‑wage jobs such as Uber drivers, you may be an interpreter, some of your kids, family members, they're creating their own information. We should embrace it. If you look at the fear of jobs being eliminated by data, it is the nature of society that jobs evolve, you look at the early 18th Century, most of us were in the military, now most of us are in the service.
The world is transforming. There is a new IT revolution, a new world revolution going on before our eyes and jobs will shift. For us, for our kids, to take advantage of the opportunities you have to train ourselves on the topics and areas.
>> PANELIST: Here we are, we're Civil Society, government, universities, humanitarians, business, you know, we need all of these different components to be able to deal with data gaps. While I agree with Pierre, there is so much opportunity, I'm a humanitarian, we're leaving people behind with data. There are data gaps, there are education gaps. Yes, focusing on the leaders, working with world economic Forum, global shapers, working around the world working on digital revolution trying to help people.
We also need the IGF Internet Society Ambassadors running around in t‑shirts. We need them to be ready. We need the small Civil Society groups to work on that. A shared curriculum, of course, is very important and that will only happen in the multistakeholder places and that will only happen in partnership.
Now, we at the Red Cross, we work with business, we work with universities, we work with Civil Society groups, and we do that because we have many problems to solve and in partnership with local communities who are sometimes very ready but if we don't actually think through how we make sure that that gap is addressed well. We're going to be chasing the big data, new algorithms which is exciting.
I'm a bit of a data cowboy too. I'm from Western Canada. I'm allowed to say cowboy.
The idea that we are making the data digital device and a data divide, that's a frightening thing we should look at. We look at the leaders and train in International Geneva. For the rest of the week I would ask what are you willing to do to build the conversations to address the digital divide and the data divide and how will you go home and implement that?
Thank you.
>> KATHARINA HOENE: Thank you.
This is the end of the session.
I would like to start by thanking the audience for being here early this morning, for fighting the snow, fighting fatigue.
Most importantly, I would like to thank our panelist for sharing their successes and their views from different backgrounds, very different kinds of experiences with working with data, trying to establish a data curriculum, it has been extremely exciting. The most important thing as we move forward is this point of ‑‑ the questions, it affects all areas of society, impacts all of us in our work life, our private life, and it calls for multidisciplinary approaches, working across different organizations, different sectors and really it is something that we need to come together on because otherwise we have no chance in understanding data properly, using it properly and putting it to good use in various areas and humanitarian and the business sector, so on.
Thank you so much.
I think this calls for more work and more intense focus on what can we do to train people and to raise awareness and to all come together and work together.
>> I was just sharing his concerns, I didn't put ‑‑ I presented the professors ‑‑ he said it in our meeting. So ‑‑ you know, the fact that the professor is concerned about doctoral students and I'm sure coming from MIT there is others, but it is his concerns, I just want to clarify that.
>> Just one word, thanking the organizers here and the Geneva Internet platform which is already doing a great deal to bring together International Geneva around these issues and just you wonder about the data from this session, thank you to GIP, this will be inserted into the University of Geneva's Digital Strategy, and it is an online platform open to anyone, you can add to the strategy and I'll leave some cards here if you want to contribute.
Thank you again to GIP.
>> KATHARINA HOENE: Thank you.
Speaking of that, we also have ‑‑ my colleague in the back of the room, has been taking notes, there will be a report of the session on the digital watch, which is an initiative of the Geneva Internet Platform. She'll provide a detailed summary of the whole discussion which is a challenge I'm sure and it will be very valuable for us to move ahead.
Thank you so much.