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for the next 45 minutes we've got the.remainder of the hour to really expose.you to a set of projects that took place.as part of what Bloomberg calls its.immersion immersion activity that they.put together over the last a couple of.years I'm Justin Hendricks from NYC.Media Lab I'm executive director of that.lab it's a consortium of universities.and media companies media and tech.companies here in New York we do a lot.of work with Bloomberg we were quite.pleased to play a role in helping to.coordinate this immersion program this.year as well as last year and quite.pleased to work with Bloomberg on our.product other areas as well.touching data science and touching.related fields we are going to move very.very quickly through six presentations.from six different teams they're each.going to give you a sense of what they.did in their projects sort of combining.their approach to data science with.real-world problems and we're going to.hope to see some some parallels in the.way that they approach those problems.and the way that they applied you know.different techniques different ways of.thinking about applications of data.science to the sort of social and other.problems that they addressed and we just.saw a bit of a sizzle reel that's what.we call it in the media industry I think.about our first project with Grand.Central partnership and that leads me.right to introducing you on who's going.to tell you more about that project and.I introduced you to his liaison at Grand.Central so you want I'll hand it over to.you to present and be our first to go.and if we'll start the countdown clock.back there everybody see that somehow.we've got to finish in 20 minutes all.three of these presentations in QA so.we'll do our absolute best to keep on.track over you hi my name is Joanne I'm.from NYU center for print science and.progress a short for cusp and also I'm a.PhD candidate from in urban engineering.and informatics at the tangent school of.engineering New York University I'd like.to introduce Rochelle petrikov and.from Grand Central who's a vice.president of operation and.administration from Grand Central.partnerships so I would like to give a.over overall introduction about the.Grand Central Partnership short for GCP.and they have been investing.approximately 15 million dollars in.person seeing all the assets on the.street including the street street as.streetscapes features that's like pools.news boxes and file reports and trash.cans and they also in charge of.maintenance and they replace all these.amenities on street in this business in.the business improved district and our.key goes through data science we've been.thinking what we can do in three days.it's not just using data science of some.problems but also really trying to.combine the data science analytical in.science and operational in science from.the organization and what what we're.thinking about is trying to establish a.protocol for this regular analytical.approach to understand how we can better.maintain and report the condition of all.the assets on the street trying to think.about how we can use data science to.create a scalable and or reproducible.analytical insights so what we cannot do.in three days three days are really.short so we planned to think about the.first day gonna be for scoping we're.trying to think about what kind of.problem and what's the scope.conceptually and also physically.considering what territory we're dealing.with especially in terms of spatial and.temporal complexity we started with data.exploratory to understand how these.reported data on the SS conditions which.we call targets and think about how we.can combine operational research with.analogical insights from data science.and always also think about trying to.identify key indicator what matters the.most for from the operational.perspective and and also we for the.first day we trying to think about what.the limitation of current data what's.the perfect in how we can get.and how to compare what we have in terms.of the delimitation.such at the scale of the data and the.frequency of the data or the resolution.of the data so here is a screenshot of.the example about the total number.accumulated number of the reported.darkest different intersections in the.street inside of Grand Central District.and we also think about trying to.visualize what kind of condition.happened in different assets because.we're dealing with actual tangible.physical assets on the street to figure.out if there's any peak seasons or if.any frequent of issues happen at a.specific location and second day is.really to validate what we learn from.data and against what we learn on the.street and talking to the test and this.is a very unique opportunity from data.scientist perspective because we're.normally dealing with data in front of.computer we don't really know who.generated data and how they generate.data and this is really the way we can.observe the operation for me in the real.world and real time context and talk to.the data collectors and why how they.collect the data and what they think.about the data are gonna help their.daily operation and they also validate.the quantitative results from the data.processing and exploratory analysis on.the street on the specific location and.the specific assets and lastly while.trying to address certain biases.potential biases in data because we're.dealing with a data generated by the the.applications we're really dealing with.user-generated data what what about the.potential preference or bias of these.users where so here we can see this is a.bar chart of all the dockets reported by.the the different staffs colored by.different staffs so we can see how these.different team members engaging with.this digital tool and reporting data.differently but on daily basis and when.we hope that the same month and this.and then we can see actually these.staffs in there reporting things.different very different in terms of.volume of the condition so they kind of.pay attention to different assets or pay.attention or have kind of a preference.to certain assets and the last day is.really about synthesizing from both data.science and operation first of all we're.trying to consolidate some key insights.about what are the major metrics you can.measure or indicators you want to keep.track of on daily basis monthly basis.for the operation and also trying to.identify analytical routine what is a.scalable and reproducible thing that.staff or for the organization can can.conduct on the monthly basis and also.saying we we talked about discuss about.future data management so luckily at GCV.is on the process of the the version 2.of their application so we we have a lot.of really valuable discussion on what.we're going to do on this better version.of the application later on what kind of.additional data you can include it or.report and lastly a potential.integration so we talked about Big Data.a lot of time but I think it's not just.the length of data data it also the.width of the data where you can.integrate different data into your.original data set so here we can see all.the business locations within a district.so this is where we can really combine.the streets condition the access.condition with the local business and to.create more business intelligence or.social value in a long term so the key.insights just on high level we single.three key insights first I'll construct.digital inventory for better data.reporting and second is set up baseline.measures for more responsive operation.the last one is I to identify.spatial-temporal a pattern for.preventive operation and long term.planning for the better investment our.takeaway from immersion in short three.days I think I learned a lot from the.organization to also validate a lot of.data from data sign.this perspective combines a really daily.experience of the actual operation so.first is to invest digital very.inventory as a really a foundation for.data-driven operation and how you set up.this framework matters later on like.what kind of digital or analytical.insights you can gain from this this.opera this practice and also to.understand the users when analyzing the.user-generated data lastly to also the.very data analytical insights with.actual operation insights this is where.there are scientists we don't really.rarely have a chance to talk to the.actual operators and lastly I want to.point out since we are talking about.better date data for better governance.or really this is really we want to be.aware of that limitation of the data in.terms of social biases and also the the.potential impact of data generated to.the to the organization and also to the.neighbors thank you thank you okay so.we're gonna we're gonna leave New York.and next go to Paris.so who's starting okay great so John.come on we're gonna let you a jump flip.we measure you start and tell us what.you got up to okay so thank you very.much we said to present you as the.project we design with with Bloomberg as.a sheet.it's a project of dashboard keep here I.dodgeball for the city and we are I.present you easier J from open data soft.on the projects and I'm John Freeman to.data officer for the city of Paris so.rapidly why is a very important project.for a city to build a KPI dashboard.simply to increase all the transparency.process for inhabitants for Parisian and.it's important for all the team of the.of the mayor to have a good steer and.following of the of this public policy.and now it's a real big commitment on.the data transparency mile road map so.it's a big goal for my my road map I.would treat this this subject and this.project with with Bloomberg associate.it's very simple.well current situation because we are.reporting in the city of Paris.and we have a big document an annual big.document of three hundred pages with a.lot of KPI a lot of figures and very.beautiful documents like you can see.with a lot of table and a lot of Excel.and a lot of things that always update.monthly a lot of work for any NGO chief.or to create this this reporting and no.connection no direct connection with IT.system it's a real problem when you want.to see dashboard or real-time City.dashboard so we began the project with.the goal of simply build a new a new.design for for is dashboard for his KPIs.simple one in one page we can see ten.because we have put the types of temp.KPI and we can see on the first page.only the red one and the green one of.course it's a simple indicators and.after this first vision of the KPI of.the city we have to to walk with each.agency to build new KPIs and new goals.for for the city of course it's.important for this type of project if.you want a long-term goal to work with a.very good tool because if you just make.a prototype just.projects you can go to trash trash it.and so just to work with an actual.partner of the city open data soft and.to try to integrate all this process and.all these project all the tool of open.data soft and a second present you have.to and the project is for us and for all.the agency engagement on this on this.project meaning to begin to constrict.and to build a new area with data for.the city and for the agency proxies you.can present us some details of KPI the.idea was to have at a glance on the.homepage the 10 KPIs by five domains we.have a platform that allow City to share.data and also to create dashboard with.real-time update so if we take for.example the number three the mayor's.program set a six-year goal for creation.of one hundred and thousand square.meters of new spaces for startups today.four years later they develop more than.twenty seventy seven thousand new square.meters so the goal is achieved displayed.in green with a checkmark if we go if.you click on the KPI you can go deeper.into the data and see the value of the.KPI today but also the abolition from.the same period last year and several.visualization for example for this one.the best thing to do was to display on.the map to see where those new spaces.are the idea was also to be able to join.another data set with other data like.for example on this one the employment.rate so if we look at another.kpi the number to the mayor's program.goal was to enter within a minute 85% of.the goal to the city call center in.August 2017 with eighty seven point five.percent the goal is achieved so.displayed in green with a check mark and.if we go deeper into the data we can see.that compared to the same period last.year.it's an increase of two percent so it's.a good news and the most important graph.is at the bottom of the page we try to.analyze data to understand why sometimes.the services don't reach the goal and we.discover it's each time there is a great.increase of the call that they are not.able to enter 85% within a minute it's.very important to access of one vision.one one view of the KPI on and the.details you you mentioned that we can.click on the data sets so yeah there are.data visualization that you are you can.all the data set data to make another.analyze of KPI if you want and very.important for the capers the dashboard.is to create dynamic data and we are.turn data the city of Paris I've fixed.my streets up for this lot of data with.this app and they're an important goal.for the services and the agency because.they are to to get a response less.within within ten days so it's important.to what manage this exist KPI and to see.in the real time where is this this KPI.for the Argentine for management to and.so with open data soft we.yes we want to automate the data that.are feeding the dashboard and especially.for this KPI the number nine regarding.the issues process within ten days we.create connector with an API directly to.the IT system of the city to make the.service of the data it's our it's.updated so for them it's a way to be.sure to know where they are in real time.but also we put them on a map to make.them to help them analyze the data and.they find some sociological reason for.the major cluster that you can see on.the map so it's automated and - next is.to automate the other KPI sorry with.this with this tool we can also database.position but you can also calculate the.KPI and it's not too easy to to to build.just to finish please.rapidly some some outcomes of this.project of course it's just the first.step this new dashboard and we want to.work with all the agency to expand all.the KPI on the dashboard we want to fix.all KPI because the project reveal that.all KPI are perhaps to redesign and the.time to create a new KPI with this.project we want of course to automate.all the process of production of the KPI.like I explained we want to use this new.connector with the Meucci domain.multi-product that data project it's.it's a very big big thing for us to work.out with this new connector for all the.Lagasse the IT lugosi of the city and of.course we want to publish this operation.the first goal we want to resync all the.process of the reporting with this.project and it's a very important first.step and thanks a lot with rows and.alleys Thanks okay so we're gonna come.back across the ocean to Bogota and I.think you've got a few minutes we've.added some time on for you and I I'm not.giving it to Xavier I'm giving it to.time hi okay great good afternoon.everyone this is the presentation of.Bogota citizen complaints dashboard this.is a work we did with Bloomberg.associates and the various little debate.Duryea is vitally something like I will.call I will I will call the Bay Area BD.so that means it's like ombudsman of the.city that's that's my role in Bogota and.Bogut is over a million population this.is the capital city of Columbia we have.more than 250,000 complaints for all.these 76 public offices by average every.year so what we're trying to do with.this project is to make all of this.information available for public.officers to improve the decision-making.in the city and what's important is we.are one of the three oversized agency in.the city but the most important thing is.we are like a bridge between citizens.and the administration so it's very.important for us this project because.it's going to be a tool to improve that.dialogue between citizens and the.administration.by well there while you reading the.objectives we have for us is really.important to help to improve public.management in the city but as well to.put data analytics in the public agenda.of Bogota so people should start talking.about the data is important to improve.the policy process and we are doing that.by example that this is the way we are.telling other public officials Bogota.that this is important we work in a.large initiative of the BD which is.called like a strategic management of.information and this is part of the.current city development plan we got.those 250,000 complaints in different.channels we have just like in the middle.just one data set which is called s DQ s.is the whole information in just one.platform is like the central recipient.and then we try to do with this.dashboard to put this information.available for the mayor of the city.local mayors the directors and.secretaries and 15 sectors and the city.and mainly this is very important for.the heads of the 15 Services office in.those 76 public offices this is the way.we decide how are we gonna do we fill so.we decided to work in a period of 18.months which means from the beginning of.current mayor peñalosa period on those.18 months we have more than 370 thousand.complaints we think that we decided to.work just with the 40% as the it is.georeference then we decided three.sectors the three sectors with the.highest volume of complaints which are.health transportation and education and.within those we decided to work with two.issues by each of them so again we start.with we did the world with over 23,000.complaints out of the 317 this is the.prototype and I read that how here to.present.very short period of time so I'm going.to walk through the dashboard in a.second but first let me tell you that.this is my second year in a row that I'm.presenting here as a data scientist it's.such a pleasure to be here.last year we when we were presenting a.really cool project with benefits that.address a really cool nonprofit.organization Matt Stevens director of.data Sciences here and this is also.another great project that we did.together with the bed area team from.Felipe's the head of the Innovation Lab.with scheffler hill which is over there.which is the other data scientists that.we put together this amazing - work that.I'm going to show you very fast I'm.asking you that you can ask we can.provide the link and you can explore in.your cell phones in your notebooks that.would be much better than I'm gonna I'm.gonna run through the presentation very.very fast but I'm encourage you to take.a look of all the functionalities that.the dashboard has because it's really.something that we put a lot of effort.and I really wanted to look it through.got it okay just 30 seconds did that.work the main section consists into two.maps where Nagre gates complaints by.sone the other shows a particular.location in the map you can filter at.the top by by sector by period of time.there are some time serious line plots.to see the evolution of the number of.complain through time totals by sector.when he'll complain is clicked you can.see the name of the hospital the name of.the college when the school when.education complain is clicked and the.specific address for a transportation.complain you have a series of bar plots.that aggregate is through several.dimension and the way that we ambition.the use of this - were is that the.decision-maker individual can navigate.through the complaints to see for.instance what happened in this.neighborhood there is one hospital that.concentrates most of the complaints and.the issues in that hospital are related.to outpatients appointment schedule.and medical record history so this is.how this - work could be navigated to.allow decision-makers to check either.corrective or preventive actions to deal.with this issue and provide a better.service for the citizens of Boulder just.one minute.the some long-term outcomes that we.would like to highlight we have a lot of.work to do with the data in Bogota we.have a lot of data but we have to make.it with more quality and available for.the decision-makers we're going to.improve the way services provide are.provided in the city it's gonna be.really good for them for the.decision-makers because it's going to be.closer to the citizen needs and it is.really important for the Bay Area.this project is related with the create.strengthen trust between citizens and.the city administration so that's it.thank you very much we we unfortunately.have a whole lot of time i we have.really a time for just one question.which I'm going to put to all of you and.maybe each team will think about one.person answering this but each of you.have looked at applications of data.science in cities in improving the.relationship between citizens and.governments you've learned something.about that that application and doing.this project what's the biggest thing.holding our cities back from being more.data-driven just based on your.experience yes.based on my experience it's actually.having the data collecting the data and.having the data we think that if we.analyze more data especially public data.we can act in a preventive way.especially yes to improve the lives of.the citizens and for example of the.complaints we just showed Jaime Rhodes.is kind of a layer so public officials.can improve the way they make public.policy so it's very important for us for.that say that we are a lot of data city.we produce lot of geometry I agree with.you because it's not the only thing you.have to connect this data we have to.build API you have to build a new a new.tool for connection and for user science.so there are very big technical project.well I think you can tell from these.presentations that we are going towards.that future at least so I want to thank.this panel three cities you know three.different continents and three excellent.presentations thank you very much and I.believe let you all go out this way so.you can hand back your microphones and.while we're basically changing cast.members we're going to invite a new.group up onto the stage thank you very.much and I'm gonna ask them to come on.now so we've got three teams who are.going to join us from I mentor.matriculate and bringing hope home and.I'm hoping that our organizers are going.to be a little bit generous with us in.terms of time since we are running a.little over Bob yeah I believe we're.starting with you is that correct yep.sorry here we go.excellent so we're going to start with.matriculate and you all will I'm Alan.you'll tell us what you're going to.present yes so matriculate is a national.education non-profit our mission is to.empower high-achieving low-income high.school students to make the leap to our.nation's top colleges and we do that by.recruiting selecting and training.undergraduates at school to become.virtual advisors helping our high school.students both gain the basic knowledge.they need to navigate the process and.get to know people who show them what it.actually can look and feel like to be.successful at selective schools in.particular we serve students who are.likely to under.and who often live in communities.without access to strong in person.college advising and we do this in.partnership with college point Bloomberg.philanthropies initiative focused on.virtual advising for high achieving low.income students.we're almost on year three of this.effort and we've grown rapidly last year.serving five hundred students this year.a thousand students initially working.using Salesforce as our central tool for.our high school students and our college.student information and for our college.students to be logging their daily.weekly or monthly interactions but.Salesforce didn't meet the user.interface expectations of our college.students so we pivoted to air table.which while successful from a user.interface perspective doesn't offer the.kind of data visualization tools we need.to manage this kind of scale and to.quickly view the data we look at on a.weekly monthly and and more often.process so that was a challenge we.shared with BA via we need dashboards to.see the data points that matter most to.us on a regular basis and in an.incredibly short period of time he.somehow managed to achieve that goal let.him talk a little bit about that thank.you so much okay so so I were given the.task of either suggesting a building.tool like power bi or something or.making a new tool which satisfy our.requirements so our requirements were.that we need to use air table as a data.source we need to have real-time updates.and we need to be cost effective so for.those of you don't know air table it is.just an online storage service which can.be considered every hybrid of.spreadsheets and databases it is them it.was a very good user interface and a lot.of features but like everything else it.comes at a cost so there are a lot of.constraints in the API while fetching.the data from a table like we can only.make five API calls per second in each.API call we can just have hundred.records we cannot just get the updated.records and stuff like that so in the.initial days I try to explore.friend different tools to figure out if.something can match I can use it.directly or not.so I float power BIA I explode - you do.I float clip folio everything else but.the thing was each and every one of them.has some more the other kinds of.limitations so in the end I decided to.write write my own new dashboard which I.did so I use Google charts Google charts.is just a online visualization tool it.is completely free simple to use and.very powerful so what i did was i coded.my application in javascript such that.all these api constraints are met and we.can also get a data visualization using.google charts so for the dashboard these.are all the different features that I.have in my dashboard like because my.dashboard with Google charts is.completely free and it supports.different all different browsers major.browsers like edge or Firefox or.everything so it is platform independent.I have used only open source technology.so it does not require any single penny.to build or use this dashboard for a.long period of time plug and play so you.can just just like our website we just.open the application and it will show.you the real-time update from that point.in time so you don't need to install any.particular software in a system to first.get that and then open the dashboard.it's just for everyone anyone can F you.can just open it up and see the current.state of the system and lastly like it.also has real-time updates because every.time you open the dashboard it fetches.the data set from the a table API using.the code I wrote and shows on the using.google charts and of course it's cost.effective because it only consists of.open-source technology so it is real.apart from the development cost sure ok.so this is just a small clip which I.showed from a dashboard it has actually.many more charts and it is different.thing but for now we have just showed.two different charts over here and we.actually have like different tables in.our a table base so for each table we.had a different section in our dashboard.which shows the current status of those.particular things as matriculate it's.like growing further so I hope this.particular dashboard might help them.track their current status and.in decision-making in the future to come.so lastly are lessons learned so first.that I think that no free lunch right so.if you want to have good features.everything but need to pay a cost like.we saw an 80 ml case that it has good.GUI good interface but I need to like be.a lot of API constraints so we need to.figure that those without and data.science and visualization I think is.playing a very significant role in like.even for nonprofits or smaller.organizations so I think we need a lot.of people with data science skills to.cater to those requirements thank you.okay so next off we're going to go to I.mentor so I believe Lea you're gonna.start Eugene Michael tell us about what.you did for I mentor my name is Lea I'm.a research analyst at I mentor and I.mentor is a one-to-one mentoring for.college success program so we pair.college-educated mentors with high.school students in New York City the Bay.Area and Chicago and we kind of have two.main components of our program it's.emailing or communicating on our.proprietary platform once a week and.then having a monthly in-person meeting.at the student school we hope our.matches to last about like three to four.years throughout the students high.school career and to basically help them.navigate the college process and enroll.and persist in college and we have we.just we collect so much data about our.students and mentors we we survey them.three times a year we collect.participation and engagement in data on.our proprietary platforms so how.frequently they're communicating what.they're saying and we also get data from.the National Student Clearinghouse about.their college enrollment and persistent.patterns so we we just we have so much.data and we have a research and.evaluation team but most of our time is.spent just helping to make sure we can.collect this data and support our.program staff who were doing the direct.service and so although we have so much.information one of the biggest.challenges we're facing is just like we.can't even dig and.- into all the stuff that we want to dig.into and do the fun analyses we actually.have about like 2 million data points.for each student over the course of.their time in our program depending on.how longer they're so we have a lot to.dig into and we tend to spend more time.with the students and so we kind of.wanted to focus on the mentors for our.data fellows and these photos are not.like poster at all this is like really.what happened so well before before.Michael and Eugene came I just tried to.collect as much of the mentor side of.data so we have like their application.demographic information all of their.surveys as well but it's the same sort.of stuff we have for our students we.have for and I'm tours we just haven't.had the capacity to dig into that.information and so we kind of wanted to.see if we could find any sort of.interesting like patterns or profiles of.what like a successful mentor might look.like in our program so I'm gonna turn it.over to them so they can talk about what.they did thank you yeah yeah so we had.just three days and we tried like first.to understand what can we do and we.looked at the data and the data is.magnificent it's actually we really.appreciate the efforts that I mentor.team has done it's really great it has.so many data points it has like the.interaction has the statistics of the.like interaction between mentor and.mentee it has the oldest service so I.think it's actually that like the.services that the most useful part of.the methods that we looked at because it.has the service all over all the mentors.that participated over in many years and.additionally we had the statistics on.conversations which is like how many.times the mentor and mentee communicated.over over the like over the period of.the mentorship and I think Lea and the.team they figured out that if they the.the mentor and mentee communicate like.more than 65% of time it's actually like.60 to 65% like above average and it's.like very successful.also we had some challenges with the.data and because it's a really big.amount of data and it's it was separated.separated between different databases it.was pretty challenging to actually.connect all the data points and find out.the whole mentor profile and that's what.we spent like most of our time to to get.the profile for the - for the data to.input and another problems that we faced.and we actually we faced on we focused.on you on the mentor and inferential.because the main she is information.because it's a high school student.they're actually protected by the law so.we couldn't use the any information.about that.so we primarily focused on on the mentor.and on the statistics and so we tried to.help with like augment the decision.making process for the program managers.who decide and evaluate the Dementors.and what we've done we were thinking.about trying the different machine.learning models and hitting the date.about the mentors and their service and.their demographic data and their about.about info and bio to get some useful.predictions of what would the program.manager revelation would look like and.actually was a very interesting.experience because we we we did a came.up because we had the limited amount of.times we didn't have time we didn't have.time to actually try many models we.tried the very simple models and we we.saw that we could increase a little bit.with like the baseline models about like.15% but the there is a like a very it's.a good opportunity to improve there.because we can improve the model itself.but as well we can improve the quality.of data and that is actually like a very.good action point for like us both to.work on because if you can make more.connections and may create a better.mentor profile you actually can get a.better prediction and as well.Michael focused on the.of the media success and actually.initially found that the the amount of.mentors which were considered that it.really matters it actually didn't matter.and I was actually very interesting.finding and another one was the the.frequency email email conversation he.actually measures for the mentor and.mentee and so that to sum up it's.actually like the very interesting part.is how to improve the gathering of data.and how can you get better data and.improve the model and try actually to.build a recommendation system for the.mentor and mainly to predict a matching.score and build it on top of all the.data that we have and the the most.important that I wanted to mention is.actually to provide a transparency and.fairness for the given predictions and.regulations because it's actually we it.really matters for the like data that we.use and I like to prevent biases in the.data and you know I have I wanted to.give few seconds to microseconds to.higher level points I think in in this.project it's very evident there are two.features of that the science one is.trying to explain things and that is.trying to predict things and sometimes.you can predict for example mental.performance but you don't really.understand what constitutes a good.mentor and sometimes you can explain.what is the success of a mentee but you.cannot predict who is gonna actually.apply to college in the future and the.second one is for that for good.immersion day it was wonderful.I think only exploration for one day two.days or three days can lead to.interesting results both for the.participating organization and for us.because you outside your comfort zone.and be also planning to collaborate more.in the future because of that so thank.you excellent please.okay so bringing us home is bringing.hope home Vincent and Paul gonna tell us.about their project my name is Paul.Eisenberg and I'm the co-founder of.bringing hope home what we do is we pay.bills for families with cancer we're a.non-profit we've been around for nine.years and if you get diagnosed with.cancer even with healthcare benefits.your out-of-pocket can be up to $35,000.I experienced this when I went through a.six nephew battle with my late wife.Nicole I had a great job a great.everything was still struggling we just.didn't think that was right so we start.an organization we pay bills for a rent.mortgage utility car payment food.whatever that family needs we help them.in any way we can we treat him like.family we've helped over 4200 families.local in Philadelphia and the.surrounding area were coming up to New.York Baltimore in Pittsburgh and we had.a great experience with Vince and I'll.let him start it off all right thanks so.I'm going to talk about two problems and.opportunities with the data has some.meaningful insight and then two other.problems that we don't have time to talk.about that we looked into so the first.one is you know the Lord demand that.there's a lot of families that are.experiencing cancer right now even just.in a small Philadelphia area so over the.years Paul's team have instituted like a.first-come first-served process where at.the first of the month the application.gets opened a bunch of people try to.submit their applications and then once.you hit 30 or 35 it closes and then we.might sort of a grievance from that.about there from an outside perspective.is that you have no idea how many people.are trying to submit so we're missing.that crucial why Matt it could be two.people a month it could be a hundred a.month so I talk about that a bit later.and how that drives into how much money.we can give out to each family each.month the second big thing would will.contact talk about is they're trying to.identify top nominators so families that.are going to cancer right now are.actually nominated to burn in her poem.by a member of their medical team but.don't only social workers because they.see a lot of the interactions with the.family and because we want to partner.with those people we want to know.we want to reward the consistent.nominators now the two things that I.looked into where there was a lot of.complaints that the wait time was too.long so once you would submit an.application it'd be six to eight weeks.before you're actually paid and so for.these families that are experiencing a.lot more of a financial burden than.normal.there can be a long time when you know.you're on the on the ball line having a.power shut off or whatever we don't pay.the family we get the bills from the.family and pay the bills directly yeah.and then the second thing I want to talk.about is there there's sort of three big.systems no elegant way to communicate so.we talked about the old law okay so the.data that we use is say I was for some.applications the feed into Salesforce.and you can just get it from there.Salesforce is a great utility just.download all the data into like CSV.format great props to them I looked at.three main table so there's applications.but three and a half thousand.applications over the last seven years.five hundred and fifty medical team.referees over 120 different institutions.at hospitals so the big sort of time.sink for me was the data is very dirty.as it always is stuff like a user into.into the fields so if you have a typo.and your name your address your email.address that she comes up is a second.record and because I was trying to look.at specific social workers for example I.needed to link up those those records.and then stuff like once they migrated.to Salesforce in 2014 so all their.Tuesday before 2014 were just uploaded.and we're missing a lot of the richness.of the data that Salesforce provides ok.so coming back to this first analytic.which is projecting demand so this is.really getting to the fact there we.don't know how many people are under.need each month we know how much money.we have right now so how much can we.give to each family and so this is a.figure of the monetary amount of the.grants given out over the last seven.years and so you can see these huge.undulations and there's a lot of.complicated reasons for why some of.those occur.but in the last couple of years you can.see there we're at fifteen hundred for.about a year and made a step up to two.thousand and so there's being a lot of.discussions Paul on a staff in this.board about what's a meaningful.impactful amount that you can give and.balancing that was helping as many.family as you can each month so data can.definitely play a role in this so like I.said before we don't know how many.people are trying to apply but what we.do has have is a surrogate of that so.it's the application that your Tuesday.process you first register super simple.30 seconds and then there's a much more.detailed application process we get to.submit bills and whatever it's a lot.more time-consuming so we can use that.first registration of intent as a sort.of surrogate for the demand so the.figure here on the left is actually the.top bar is the number of registration.applications that are started in the.bottom bar is how many that are funded.and this is quarters over the last.couple years so barring the first graph.I mean first quarter it's actually not.that big of a gap so over 2016 is about.thirty a quarter that aren't funded but.that I started and it's actually not too.high so after some conversations that we.had while I was there.Social Work is saying they would.actually like to help more families and.they think a thousand even would be.still impactful so if we took all the.money in 2016 that bring hope home.handed out spread that evenly over all.the pre-applications that were started.you get twelve hundred dollars so is it.here's a good case with you know super.simple with respect to data analytics.can give a nice objective Monteria math.the poor can take to his board and say.we're gonna move forward with this.things will change we'll keep an eye on.it we'll see how it goes for and this.was that she implemented in September's.application cycle about 40 applications.were received which actually wasn't.anywhere near as high as vo as we were.all bidding we had some sweepstakes.and we could we could actually found.Orleans so this is a really great step.moving forward I've got no time but this.is the second analytic which was there's.kind of a more of a person or.exploratory thing which is trying to.identify those nominators social workers.that are really performant they get.there they get one or two applications.in every month consistently regardless.of what's going on and because we want.to partner with them we know who they.are so I had a couple of Christians I'm.gonna try of address so on the x-axis of.both of these graphs is the total number.of successful applications so there's.been some social workers that have had.more than 20 applications funded and the.lift figure is the y-axis is the median.time between starting and Ritchie.getting the application in and approved.so even the best social workers the ones.in the weird oval still take between.fifty and a hundred days to get their.application together and on the right.figure is actually the average amount.average number of grants awarded per.month they're active so as you can.expect as you go to the very very far.away the very very performant social.workers get pretty much perfectly to.applications every single month so they.are right there.as soon as it becomes 8 a.m. they at the.bottom and they're down so Nick's tips.that I've recommended to the bring home.team is that to bring together those.three from data sources something like.tableau be really great and it's.interactive you can see who they are.talk about outcomes yeah so we we.reduced the amount and we actually have.dropped our wait time for families from.six to eight weeks to about three to.four weeks it was much cleaner we listen.to our social workers and as a.non-profit we have like four levels of.customers social workers donors but most.importantly the families we were able to.drop the amount of money which twelve.hundred dollars on sound like a lot but.you would be shocked at how far that.goes I have four kids in college 1200.hours a lot of money to me.but we were able to increase our family.help and our delivery much much faster.which was tremendous for us.thank you I'm told our organizers.unfortunately don't have time for Q&A in.this session I believe all of you will.be here a little later to answer.questions in the from the audience and.in fact people have them I just want to.think again Bloomberg for doing this.Gideon Amanda Sean everybody in here.who's involved in this thank you so much.so a round of applause to you.[Applause].

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