data? they have the idea, but they don't have the resources to necessarily make that idea -- bring that idea to fruition. and, so, this is going to mean huge things for innovation as the mayor said, and huge things for our customers. we work with a lot of civic start-ups who are trying to do better things for their community and by combining private data that you see in here with companies, with the public data through the portal, we can actually start to make businesses around the data. >> i think we're at the very, very beginning of this. one of the best parts of the job is i get to hang out with other people who are really trying to do very innovative things and be very creative. i talk about some of the data sets we use. most people still don't know they exist despite great efforts from the city and others. and, you know, a lot of entrepreneurs that we're looking for is the way to get leverage, a way to build an experience or build a product before we have 100,000 users using it. open d

data. and basically you go to the utility website. you can download your own green button data which by itself is, well, i'm an energy guy, an energy geek. i consider with confidence. it is not interesting, necessarily, but when you take your green button data and you give it to some companies, they have amazing things they can do with that green button to, again, save you money. something as simple as if you look at your green button which is kilowatt hours for those that are engineering minded, a line grab if you think about t some companies today can look at your green button and figure out if your refrigerator is broken function need a new air conditioner. that's real money if you think about it at a commercial or industrial scale. that is one data set. to your other question about what is the federal government doing, we're seeking not just an energy, but across the government to engage entrepreneurs and innovators across all the different sectors. for those of you familiar with the hi

and i'm pulling data from multiple data sources. here we have data from the city. we also have data from private data sources that read -- not going to mess with it. there's one of the variables now, the bottom you see there is median household income. we're pulling in all these different data sources, creating a beautiful visualization to tell a story at the neighborv level of how the city is growing. and some of the things you're seeing on the map here are a pipeline of information about both residential permits over the past decade or so, how has the city allocated permits throughout different neighborhoods in the city. and some of the other things you're seeing on the map is the approved businesses, the businesses are currently doing business in the city. one of the things we said once we started visualizing on the map the slow and more rapid growth of residential -- residential property in soma and then in 2011 you just saw obviously a huge residential boom in the downtown area. so, we've just actually -- we're a company in southern california. we just relocat

data. you have to do things like this, where you get the community together or you have people actually talking about it because the demand side, as we were talking about it, will be there because there is going to be someone there. there have to be people working with it who are getting out there. i think this is what this city is going to be really powerful. in terms of other cities doing as well, chicago is doing some really interesting stuff. scary cool stuff. they're taking 3 in 1 data, pothole request and crime report and matching it up with social media. they're getting this really deep and rich picture of what is going on in the city. and you can do that with data when you think about it creatively. philadelphia as i mentioned, they are really active in open data. and new york, again with 3 in 1 is doing smart analytics. i think that's what you'll see happening as well, government starts to become smarter, make better decisions, better policies. this term algorithmic regulation, whi

and we are working with open data and trying to see how we can turn data into information, data into knowledge, and the kind of decision products. >> hi, i'm john, ceo of motion loft. we're trying to understand how people move around cities and provide that data to the public to build new tools for public safety. >> hi, i'm [speaker not understood] with code for america. we're a peace core for geeks. we're trying to bring talent from the private second for and government to innovate. we work with dozens of citieses across the country and next year we should be working with san francisco which is up, great. >> i'm filling in for a second. [speaker not understood]. >> hi, i'm yo yashida for appaliscious. >> we'll try to get a conversation going to talk about typically what you're doing. i'd be interested in hearing each one of you talk a little more about what you think -- it certainly is a term we're hearing a lot today and in the past. we'd love to hear what you think open data is now, sort of how far along are we in terms of actually getting governments to release that data and actu

data. we did a lot of data, put things together. we did things like added value, added certain locations. and i would love to be able to pull that back up to a shared community portal. from what i can tell it wasn't necessarily promoted at this time, but i think there is a lot of value in that. and shannon, interrupt me if i'm speaking -- if that's -- if i'm stating anything that's incorrect. you know, i know that they went through a special process. maybe we make it a little easier, a little more seamless so that anybody is pulling down data and doing things, they can't push it back up. that was one of the things we would love to see. >> probably shouldn't have been in public, but we were very naive about the whole process and we were actually trying to feed content locations around the city. and started to do this and started to think there must be a list out there. there must be a list of every park in san francisco. i would find it in wikipedia. we stumbled into the sf data website and

data. this is really exciting for us. >> we're going to tell a quick story on how private data can stimulate civic innovation. this summer in singapore, we had about 30 data sets opened p. up. one data set was about a million or so records of taxis all over town. typically taxi data stuff, it's hard to get, private companies, they don't want to open it up competitively. one taxi company at the last minute opened up, motion loft made it efficient to see what would happen. because they contributed to the stuff, we had five separate teams working on could we do collaborative consumption of sharing taxis, build a dash panel for companies to know where to go. had i hatch i had been working on a model to see how much overlap there was. they sent us to tokyo to work on that. so, this one set, a million records of where the taxis were not only led to a lot of innovation, but people realized 15% of all taxi routes kind of where people went, about 15% of that was all overlapped. so, a city that never

data. there is a lot of value there. how do you sort of balance, protecting the value of your data and commercial viability versus making it available to the public? >> so, we have a unique problem, i think, to a lot of start-ups in the fact that we have a product that we sell and a lot of different vertical. we also have data we want to provide to the society at large. and how do we not step on our own toes and give away our own data and make the company worthless. so, it's tough. it's a definite fine line between the two. today we announced that we're going to give crowding data to the city so you'll know where crowds are in the city and when they occur and where they occur. and that really isn't commercially viable for us. so, something we can provide to the city without any repercussions financially, which is great. we want to do more of those things. i made the decision to release this data about three minutes before a speech that i gave at spur a couple weeks ago. and literally, yeah,

do you have access to that data? >> the arrest data includes san francisco residents and people coming into san francisco and arrested while in san francisco. >> okay. so it includes people who are not residents so the proportionsate rates are include people not living in san francisco the surrounding county. you might want to look at that. >> if the data is available we would. >> seems like there are thoughts on that. we are interested in data driven policy but it has to be accurate doe i think that a carve out that will make a difference.

i think the bcc made a good point about the data and the data reflected in the pathway reflects the data gap in the school system and i'm going to be a broken record. the achievement gap, the opportunity gap persists whether it's english language arts, whether it's math and it's especially persists with students with disabilities so while we're very proud of the work that we have done and

i think the bcc made a good point about the data and the data reflected in the pathway reflects the data gap in the school system and i'm going to be a broken record. the achievement gap, the opportunity gap persists whether it's english language arts, whether it's math and it's especially persists with students with disabilities so while we're very proud of the work that we have done and appreciate what the bcc said we have a lot of work to do still to close the achievement gap because it persists in every aspect of our educational system and you beautifully captured the work of the district and what we're committed to going forward. i'm going to publicly ask the bcc to work in the transparent manner they have worked with staff going forward. i think you have heard examples from the community perspective identifies holes in our communications structures, in our mi

simply structured data? that is the kind of technological change that would enable the kind of competition on privacy we need to talk about. focusing on usage in focusing on proving -- improving transparency. >> let's talk about self regulation. facial recognition with respect to digital is one of those things. do we think self regulatory efforts have worked? having fallen by the wayside. we have the little code of conduct. we have the little blue icon. when we are talking about facial recognition and digital science, is that form of notice effective temple what needs to be done to strengthen that sex -- self-regulatory regime. >> we mentioned several self- regulatory programs. if a company says we adhere to these guidelines and they do not, that is a cut and dried case. the ftc can provide backup in that area. as to whether the companies are complying and whether there is a big problem, a lot of this is in the early stages. >> i am it really proud of the work we did on our self- regulatory regime. we were j

of consumer data industry association. can you give me some idea, can you volumize how much data is in your association, by the way, stuart? we represent a lot of different models, consumer credit associations --. >> how many --. >> they have probably 250 million credit reports and we're updating four billion data elements a month. >> that's a sizable amount, is it not, evan hendricks who is editor and publisher of "privacy times". by the way he has written this great book, a great read --. >> named book of the month. >> credit scores and credit reports, how the system work and what you can do. do you want to comment about anything he has said? >> information about social security very valuable. one of the tough things about sit that we don't own data about us. when companies like stuart collects it, they own our names, we don't own them that's why they can traffic in our names. >> when you own a volume of data you can sell it for a subs shal commercial reward. >> it's an extremely lucrative business. >> we have the chairm

it is that you should not be surprised by the use of my data or the re-use of my data. if we have that as our guide, i think the question becomes the confidentiality and the business practices that happened around us outside of the consumer. that tend to block access to a lot of bad data. from the consumer viewpoint, we would love to see quality and value data. as a whole, the challenges were individual consumers and patients and families are not probably ready to be the demand for its for unlocking that information. they are simply not using public reporting and other quality measurement data for choosing physicians at the volume we would like and it is a chicken and an egg because there is a lot of data that is not particularly useful to consumers. i think it is an important question and my hope, as we began to see quality measurement strategy is in multi-settings, began to connect the dots. we want to take a whole person orientation. we want to think more holistic play about a person expresses an episode care and measure around that. >> have two more minutes. >> than

you have gdp data in your report, but obviously, nobody checked to see if the gdp per capita data you are using tracks between international organizations, which had a two to one difference. if we do not track the effectiveness, does it do any good to have measures of the effectiveness? >> ok, where we start? [laughter] let me try to deal with the first question. and then i will get to the multi part question. on the intangible benefits, yes, we do recognize there are some. but what i was trying to say benefits for a program or policy, they should be articulated. we should find out after we go into the audit. i recall an instance where we were doing an audit the guy said, oh, our purpose is to do this and this. and we said, we ask you when we did the audit were your reasons work and you gave them to congress. we took your justification and applied it. particularly when you are looking at so many ip programs prove they had intangible benefits that they could not enumerate. that is not what they told congress. i do not do policy. i do not do military policy or tactics. we do not do for

there are a lot of data gaps. i was a local elected official and we worked very hard to get schools to collect data on incidents of violence in the school and, frankly, there's a lot of data integrity issues involved. you don't want to be the school that has the highest number of incidents in your district. and so there's a built-in perverse incentive to sweep things under the rug. and so part of the challenge moving forward and i have had many conversations with superintendents and law enforcement officials as well about how we can address this data integrity issue and how a school won't be hoisted by their own petard because they had the courage to collect the data when other schools kind of look the other way. so, again, it's a hard question to answer in ways that are other than anecdote. there have been survey data and things of that nature, but i feel uncomfortable saying unequivocally this is what we know, these are the trends. i like to be evidence based and i'm not sure the evidence allows that. >> roslyn,

so my hope is that we get the data of the type of 1-5 cases. for the district attorney's office, i think getting to data that is really useful, so that we could see what the needs of your office is really important. i did feel that some of the slides and the use of the court events was [ph*-eugs/] leading to note if you could respond to that. >> i could. the court events are the only information that we have that could not be manipulated. if you want to go back for information that looks more favorable, i could do that. we tried to take universal data that we have available today. it's not necessary ily saying it's great, but to take that data and i think most reasonably people will say understanding that a court event could be anywhere from a 30-second standing in front of a judge to hours' of litigation, i don't think it's unreasonable to say that you can extrapolate for that that is indicative of the total workload and especially if you consider and you understand the nature of the work, and you understand that actually the most complex case