Good afternoon, everyone. Pretty good to have you all come here to talk about metrics right after lunch. So I appreciate that. As he mentioned, Sallie May is an organization that helps people plan, save, and pay for their education. And we have servicing centers across the United States. Predominantly, we're supporting US citizens, but we do give loans to international students and students going abroad. What I'm going to cover today is really some of our journey for our data governance program. Because what I want to talk about is along the way where we captured metrics and why they were important and the types of metrics we captured. And then partway through, I'm sort of going to deep dive because we're going to go into our data quality program and talk about how we formalized doing business value calculations and really step you through that process. And hopefully it will be straightforward. And if you have any questions, please feel free to go ahead and ask. And then I'll also touch on our data quality dashboard so you can see how we're presenting that information to folks in our organization. So for us, data governance is an enterprise program. It sits underneath our enterprise data management strategy. And the data quality program also sits underneath our data governance program and you'll see why that's important. So it's basically where we're pulling together the business and the IT folks to work through data issues that span our organization horizontally. So we have a couple of things we do. Proactively, we develop policies and procedures. We react to data issues coming up with resolutions for those issues. And then when I refer to ongoing efforts is everything you have to do for your stakeholder care. Which as many as you know, it takes a lot of your time. How many here already have a data governance program in place? And how many are here because you're gonna be starting one? And you want some? Okay, so we're about half and half, I think. Which is good because it's interesting to see over the years how the audience is maturing and there's more and more speakers talking about data governance. Here's our timeline. I'm not gonna go through each one of these in detail, but so you see we've been going now for about six years. And I'm gonna touch though on some of these phases just to talk about the metrics we captured during that particular phase. So this presentation's all about metrics. So our first initiative was to identify our enterprise data. So it was very important to show the before and after images. And so these were the metrics that we captured. Now we did a process where we had a lot of conceptual models and we took those and drilled down to the logical level. And then my group, I had a data modeling team so we had access to all the physical schemas. And we took those and drilled up to the meat in the middle and we moved like processing data, Q type fields, that kind of thing. So as you can see, we were able to demonstrate that when you got rid of all the noise, only about 15% of our data was truly enterprise data. We defined an enterprise field as a field that was used by two or more business areas. Also from this effort, we were able to show by line of business, the data that each line of business was using. So you know, of course you're probably like us, you have lots of systems that support multiple lines of business. So that was the first opportunity we had to really go through that. And so if the originations area said, well what tables do we use in the system? We could now tell them that and show them the fields that they were using. So these were metrics that became very important, not only to kind of let everybody know as we were laying the groundwork for data governance what the scope of our project was, but then we can use that metric and I'm gonna talk about the maturity model we developed to then show how many fields were standardized and what percentage of the total that is. So having a metric like this will help you over the years. Our next initiative was a pilot project and it was called the Seven Data Elements Project. Where as a company we were moving from predominantly marketing to lenders and schools to marketing to consumers, students and their parents. And management asked us to look at these seven fields that they thought the data wasn't very good quality and it wasn't timely data. So we did that initiative and we went through a two day jazz session to identify all the issues and possible resolutions. But we then had 43 action items that needed further investigation. So for the next six weeks after that, we actually went through, meaning on a weekly basis, working through those action items and tracking that information. Then we also for a metric tracked the number of implementations that were successful and so it turned out that actually out of everything that needed to be done which included both business changes and IT changes, we implemented about 75% of the changes. And you're gonna see in the 70s is a recurring theme here. There's always a few that seem not to get done because other priorities come in and take precedence. So we were actually asked by upper management to track those changes for the next six months to a year. Now at that point, I wasn't very good at metrics and I realized after we had gone through most of that that we really need to show the value to the organization. So what we did, we went back, we worked with the marketing VP and identified some scenarios and we're able to show this business value for that project. Increasing revenue by 2.4 million and we reduced costs because we were moving from a snail mail sending out letters and to the Bowers on marketing campaigns to email which was a big thing at the time. And so we were able to show those costs and that's what one of the key lessons learned is to look for something like that where you can show actual business value very quickly. This information also funded some strategic corporate initiatives for IT the following year. Now this is just real quickly our organization just so that you know when I refer to the different groups what they represent. We have a data governance office which is very small just a couple of us and our role is to facilitate and coordinate the whole program. The Data Governance Council is made up of all business representatives for all the lines of business in the company. There's probably about 25 folks. We done at the bottom we also have business subject matter experts. So those are additional people that those data stewards can tap. We have IT subject matter experts and data folks as well. The new group that we started a couple years ago is this data quality services team that does a lot of our active profiling and you'll see they're very involved with keeping the dashboard up and running. So that's just a very quick view because the council plays an integral role with helping you with your metrics. Now anything we do in the data governance program has to affect one of these or more of these corporate drivers. So we're always looking is this going to increase revenue? Are we decreasing our costs and complexity? Or is this really a compliance or a risk issue that we need to deal with no matter what? And so if you keep grounding yourself and asking yourself how does it fit in? This will also help set you up for how to deal with the metrics and categorize them in one of these buckets. So early on, we weren't very good at quantifying metrics but what we did was we went out and we looked at industry publications and here there's an example from Harvard Business Review. We also talked to marketing and said, if we can give you better information to do better marketing campaigns, how will that help you? And in fact, the marketing person we sat down came up with this very conservative estimate at the bottom. So if we assumed we had a million mailings and the average price of our loans is $4,000,000, she said it was very, very conservative. If we just said we improve our rate by half a percentage point, we are gonna result in $50 million more in loan volume. And she was very comfortable with that. It was very, very conservative. And I'll keep saying that. One of the things we make sure we do is you always wanna go with a very conservative number so nobody can question where that number came from. And I'll give you some more examples of that. So at least when we showed this information to upper management, they could all say, yeah, that makes sense and not their heads even if you couldn't quantify it at the time. Here's some examples of managing cost and complexity. I won't go through them all but for us, a lot of these examples, they ring true to our organization and upper management knew exactly what we were referring to. And I included some examples of compliance initiatives. Again, most people won't give you too much trouble with those because they know that you have to do that. We have to, we're regulated by the federal government, the Department of Education for our federal loans and there's things that we need to do to make sure we're in compliance. So go out there and at least interview and talk to your business areas and they'll help you come up with these lists and that's how we started. One of the things we did when we were designing the data governance program was to develop a maturity model for our data. And this was based on Gwen Thomas' data maturity model and we adapted or modified the levels to suit us so those levels make sense to our organization. But this was a wonderful way to then tag our field. So first of all, remember all those enterprise data fields I talked about, we did in another session, project rather. Those all immediately became a level two because they were identified and they were enterprise data and then as we would go through and work on the names and the definitions, moved it up to level three if we identified and went through all the process of what are the issues associated with that field that would be a four and five is that field we fixed all the issues associated with it. It's now a level five. So all of this is kept in our data modeling tool. We're able to generate reports and at any point in time I can show you how many we've done and how they've moved from level to level. Again, a great metric that you can use for upper management. Another thing that we found was that we were doing a lot of work on different things not really getting credit for it. So we started tracking our time by these different buckets because in addition to the data standardization we were doing a lot of master data management support or helping on project teams and we wanted to track our time. So we could show where our time was being spent. So I'm gonna talk about our issue reporting database. So this was in addition to that, we're like, okay, that works really well for tracking the issues but we need a way to track all this other stuff we're doing. We do have an issue tracking database. It's just an access. One thing you have to think about is how to set up your issues and we've gone to like by year. So it's like DG2011-001 would be the first issue for 2011 and then do the same thing for 2012. And as you can see, we've done 184 major issues. 76% of those have been closed. So again, in the 70s, about three fourths of our work we're able to accomplish. We have lots that are in progress and several that are open meaning they're waiting to be looked at. There are some that we also put in a tracking status that I'm not showing here that we're asked, even though it's like an IT implementation is required or changed to a business process, as the data governance office, we track that and report it on it later. As we were going through data governance, about 2009 we hit funding to work on data quality and we said, okay, those same three corporate drivers that we use for data governance will apply to data quality. And here's a sample of how we represent that and I'm gonna walk through kind of how we get to these slides. So this is just a summary slide. I like the graphics, I left it in. This poor guy's working all the time to capture the metrics and it is a lot of work and you have to always think about how to update it and be able to show it to management. And as I said, just a reminder, use industry publications, work your way to where you can quantify the benefits. So when we started data quality, we said, well, it's gonna sit underneath the data governance umbrella. And again, this is where the council comes back into play because they're gonna prioritize and rank anything that we're gonna work on. So they feed us the issues and they prioritize which ones we're gonna oversee and resolve. And our whole goal was to develop data quality as a competency just as we did for data governance. Now we worked with Danette McGill Ray and this is her 10 steps methodology which would take a day to go through to do it justice. But at least I put it here so that you would know we did have a methodology behind what we did and it's important to have a framework just like for our data issues, we have a framework so that every issue is treated the same and ideally you're trying to resolve the root cause issues and fix the problem and prevent it from occurring again. Now, this is when we got training. So up until now we did a lot of grassroots efforts and as you can see, we're moving along with metrics. Now we're gonna learn some techniques and the techniques highlighted in yellow is basically what we followed. Now the anecdotes, that's pretty simple, right? That's just collecting the stories and sharing those stories. But that worked really well because everyone in our company understood or knew about that particular issue or at one time when something happened and everybody heard about it. We also did tracking of what was the uses of the data and you'll see that for the business value calculations. How is that data being used currently? And you might always also ask yourself how could it be used in the future? My favorite technique is the Five Ys because it's very simple and it worked really well for us and we continue to use that going forward. That ranking and prioritization number five, I mean that's what we're asking the business to do, the council when we meet, they're consistently and constantly ranking and prioritizing our work and we're probably on our fifth or sixth phase of rolling out business value metrics. And then what you need to do is quantify the cost of low quality and you're gonna see how we do that both so we show potential value, here's the value, we could say the organization by cleaning up this data and now in the past year we're tracking actuals as well. So let's dive into some examples of each of these. The anecdotes, what sits behind each anecdote is probably a one to two page paper that summarizes, well I should say, it's the details of the actual situation and what occurred and what, by the data being wrong, erroneous, like what was the issue and then we take that and we take all the details and we rolled it up into number two which is really summarized examples. And so that was very easy for management to look at but then we said we can do better than that especially when you have to present to a CIO, any C level individual and so we further summarize those two page papers into these couple of bullet points but for us when our management looks at that they know exactly what we're talking about. So this is more here just to show you, think about it easy way to show that information. Now the five Y's is, like I said, a very simple technique to use. Now we do student loans, well if there's a lot of, we call them bower benefits, there's certain programs we offer the bower if they're good with their payments. So for example, if they pay the first 24 payments on time we might give them a percentage off their interest rate or we give them other incentives. So of course they benefits the bower so we started to look into these fields because it turned out some of that data was wrong you would ask well what is that, why is that an issue? And in this case, of course the business like servicing area would say, well that means the bower benefits is inaccurate and therefore the bower's not getting credit for what they've done or whatever. Okay well why does that matter? Well they might tell you well it matters to us because then we have to get on the system, go and find the issue and then fix it. And they keep digging okay well why else does it matter? Well it turns out that those bower benefit fields they have to be there and be valid in order to go through this process where we actually push the loans to the Department of Education that's called the PUT process. Well if we're not able to put the loans through that increases our funding costs and so we're actually losing revenue because it's costing us more money to do that. Okay so now not only there is a cost to fixing the bower benefits but there's a decrease in revenue that you can quantify by having that data that needs to be fixed. And those are the kinds of conversations when we would sit down with the business area and you just need to keep drilling down and down and down as you go. So when we did the data quality program just like with our data governance program we did a pilot project and I highly recommend doing that. It really helps you test your roles and responsibilities, helps you identify the framework that you're gonna use, you know how everything's gonna work. So we met with the data governance council and that was the first time we asked for what would be the top fields that you would want us to actively monitor. Well of course they came back with a pretty good big list so one of the first things we did we had time boxed that we wanted to get this pilot done within a couple of months. So we looked at anything that was really complicated like looking at three different fields together and we didn't feel like we had the expertise yet to do that we kind of discarded or it turns out we maybe didn't have the data to actually go profile against then meaning we didn't have access to those systems or it would take time to get that all set up. So we ended up coming up with a list of 10 fields that we were gonna look at. Well that actually was a pretty good number because in reality when we looked at what the business rules would be it evolved into actual 22 business rules because the business rule will also vary you'll have a separate one of the different source system or if you might be looking at three different things about a field so it would turn into three different business rules. And what we wanted to do is find out from the business who really cared about those fields. So this is a sample of a spreadsheet that we sent out to the council when we prioritized here's the top 10 we asked them do you care or not about these fields? So by line of business area they just check the box. So that would tell us who we needed to interview and get more information from. So let's say if the collections area didn't care about this field we weren't gonna interview them, right? So we used this to kind of drive the interviews and know which business areas were gonna be involved. Now one of the things we had to figure out is well what are those costs to you know if you have poor quality data? So we looked at some industry lists Larry English and David Lotion and Danette has stuff in her book as well. We took a look at all those lists and we said of these things what makes sense for our company? What are the things we wanna track about the quality? Then our my chief data steward is great because she's worked in the servicing center she's also been our controller so she's a very good finance person. And it turned out one of our meetings she said well you know a lot of this looks very much like our general ledger when we roll our costs up for the company some of this stuff ties in pretty nicely. So a good lesson learned there we looked at that we picked the categories that tie to our general ledger so then any business area they all report their finances according to that chart of accounts, right? So they knew what those buckets represented so that worked out really well. So we had to define each of the business value categories and then also come up with questionnaires to interview these folks. So this is easier to follow so there's an example so we have lost or missed opportunities in the line of business and we would give that a description and we map that to what business value category in this case it was a funding impact. And then we took that category and if you look down at the bottom it'll show that funding impact went under the revenue generated bucket, right? And then next example, work around costs and decreased productivity, okay? So that's poor data quality causes, manual workarounds, they have to correct the data, et cetera, we defined everything that tied to staff costs and staff costs go under costs avoided. So we did that for all of them and we also had a section for intangible benefit. So now we had the framework for how we're gonna capture all that information. We used that to come up with these questionnaires and it's just a word document that we would use. But one thing we did and maybe because some of us had been there a while so we know the business area fairly well even if we're in IT and then with my chief data stewards help we actually looked at each field and what the business rule was gonna be and tried to guess say okay we think this is gonna impact funding. We think there's work around costs associated with this. We think they use this in marketing campaigns so it's gonna impact them. And we would populate this questionnaire with the categories that we thought we're going to apply and the ones we thought were maybes but we needed to ask so that when we went into the meetings we were prepped and ready to go. And so you can control the meeting and the discussion and the questions that you're asking. And then we would fill that out and then add any like follow on action items who we needed to contact because sometimes maybe if you were looking at address fields we might work with some third party vendors and they would have to tell us well we send that out to Novus or somebody and they'd have to go and find out what the cost of doing that was. One of the other things we did and they'll be the next slide I'll show it more graphically it'll be easier to follow but in the beginning we had the business areas sort of checked do they care or not. Well we started to realize that if we wanna determine who the approver or the data owner or whatever you wanna call them will be for a field or a business rule we need to know more. We need to find out whether it's a low, medium or high priority. And so we defined what those meant. So of course high we said okay if this data's wrong it's a complete failure or it has major implications it could be something where we're gonna be out of compliance and we need to know immediately. Medium okay there is an impact it's not as significant as a high but we have to take a look at that and then low is okay there's some minor impacts but it's not a big thing and so actually if resources are tight those are the ones that are gonna be looked at last. One developing the calculations we needed to look at some results of what the data looked like and also set up these criteria ranges. So for each business rule that we're gonna have does it fall into we came up with these green, amber and red ranges. Now the reason we picked green, amber and red is that our PMO for all our IT projects uses green, amber and red to indicate how the projects are doing. So everybody knows what those colors mean. So green everything's okay you're falling within the range you're right on target. Ambors well you failed but you're not that bad yet and then red is totally unacceptable. So what happened now since the pilot when we send out that original survey to ask the business areas they will now mark instead of just checking the box we have them mark high, medium or low and then for us we will take the highest ranking and that's what the business rule gets. So in this example enrollment period begin date the check for that it got a high ranking because there were several business areas that marked it as high. The high also tells us that let's just say the originations area cares the most about that field or they marked it as high. They are then tapped as being the approvers and having to follow through on that field. So that's one improvement we've made to the process. Another thing that we realized is that it's very important to do some monitoring of the field before you get together with the business so that you can actually show them what's out there so you've got some real facts to look at. So we'll run the profile. So we've got the business rule done, we'll run it. We can tell them how many records we ran against how many were successful, how many weren't and that helps them determine those status criteria ranges on the far right. So in this example it's a relatively innocuous field you know the ranges aren't as high so really not until it gets to 84% or less doesn't get marked as red. Now we have some other business rules having to do with, you know, paying back the loan and the term counters and if they're in the negative, one record is the negative number, it goes right to red and somebody's working that. But we've actually put in code to fix, there was a problem with that and that actually is one of those anecdotal stories that everybody knew about and they actually put in code to fix that and we have not seen one since we've been, you know, running this. So that gives you an example and then for all of those we're also showing intangible benefits. Because they are important, it's just another reminder, you know, it can affect a reputation of the company or poor customer service even if there's not a direct quantifiable calculation. So we went ahead and just this is just to follow up with the pilot. For the pilot we ended up going back having more meetings because as we were learning the process and it took us more meetings and it does now a lot, a lot we can accomplish in a single meeting now that we've got our process down and we're much better at it. But we just followed up with everyone in emails. Now this is the important thing is the spreadsheet and it may be hard to read so I'll just walk through an example and this will help you understand the five why scenario. This is a fictitious numbers but let's say our business rule is state code is invalid. So you can, we're meeting with the servicing area and we'll say okay well what happens if state code's invalid? Well the letter may not get delivered so it gets returned. Well why is that an issue? Well it could be a compliance issue because if we have to send somebody like a privacy notice to some other compliance letter they're not gonna get it. So there's, we won't even talk about that right now. And then we'll say well what happens if it's returned? Well we have to have somebody in the servicing center look it up, they may go to the white pages, they may have to pull the original application trying to get this information. And that's when you start digging. Okay that's when you say okay well where is that done? Which group usually does that? Oh well that's one of our servicing groups. Okay are they in Pennsylvania or Delaware? Most of that's all done out of Pennsylvania. Now what we did was we went to HR and got our hands on the list of all the salaries by geographic location for our company by level. Okay so now because they said Pennsylvania I can go to the Pennsylvania grid and look at salaries and we'll say okay well what level person usually does that work? Well it's usually anyone from a level three to five. Okay so right there I'm gonna take level three because it's the lowest most conservative number. And we add into the salary, we add in our percentage that gets applied for benefits. So that number includes all of that and we'll break it down to what the cost is per minute so done by hour then per minute. And so we'll say in order to do that how long does it take them to make that change? Well it's usually four to six minutes. So what I'm gonna plug in there is my four minutes. So the overall cost of making a change to one record is very small but if you have thousands and thousands of records that have a problem the numbers add up pretty darn quick. So that's what we did. We have a spreadsheet, let's see. Okay so we have a tab for each business rule. On the tab for that business rule is all the calculations, whether they're revenue and we sort of haven't divided up if they're revenue generated or cost reduction. And then we have a tab, one tab on the whole spreadsheet that's four intangibles. And we keep those in one place. Okay so everybody with me so far. So now we have, we have 95 tabs because we have 95 business rules. On the first tab though, so the first tab now would be 96, we have sort of the summary area because we run the metrics on a weekly basis. So but we don't, we update our dashboard only once a month. So but once a month we have to like put all the numbers in and you don't wanna be going from tab to tab to tab. So for business rule one, I have a guy in my group who's great with Excel and he linked all those calculations to this front page. So for business rule one when it runs let's say there's 10,001 errors. All you have to do is go into that column D that says number of errors identified and put 10,001. It automatically calculates and it'll show me all the value for potential costs avoided and potential revenue generated. So that all happens very automatically, right? And then what we've started to collect, you'll see down at the bottom right is actuals. So when we need to work the issues and we've asked folks on the council, so if it's a business area doing the fixing of the records or we have to open up a service desk ticket with another group to make the changes, one of the things they bring back to us is the metrics of how many they fixed. So when they say, okay, out of this 100, we fixed all 100, that's now going in and it pre-populates and all those calculations and we're showing that on the dashboard as well. That one's the tougher one, right? Because you have to follow it and you have to be diligent about keeping track of the metrics and we haven't found a way to really automate that any better other than through the service desk tickets. Okay, so we wanna present all this information so we wanna make it easy to look at and drill down though. So we developed a dashboard and basically the important thing is you can see it at a very high level or you can drill down as you go and I'm gonna go through, I think the next thing, best thing is to go through the chart and you'll see things a little bit better. When we started, conceptually, we said, oh, wouldn't it be nice? We'd like to look at the state of data quality and in my mind, I was thinking of more like a heat map so you could see where the dots fell and the green, red and amber and we knew we wanted to track business value that was always on our radar and then also, because of the data governance program and I know how important it is to keep showing value, we said we're gonna do the same thing for the data quality program. We wanna track like the initiatives and where our time's being spent. The reality was, we really, on the right hand side, when we went to implement the tool that we were using for the business intelligence piece, we couldn't, for example, we couldn't get the heat map to work so we went with something else. And the number of, so at the top and we're gonna go into these a little bit more, but state of data quality, so that shows you at a glance, how many of our business rules are in green, amber and red, you can click on those and drill down and I'll show you another way that, like any dashboard, there's multiple ways to get somewhere. The business value, we're showing, you can see the projected versus the actuals, there's quite a bit of difference. The number of DQ issues on the bottom that includes not only the active monitoring, but the one-time kind of ad hoc checks that we do. And then the number of data quality engagements, we update this, we'll keep it updated all year long, in fact, so at the end of the year, I think this might have been done at the end of the year, but I've got the numbers covered up anyways, but we're tracking the certain things so everybody can see how much work went on in other areas. That's a better view, it's easier to see. So this is the February copy and you can see the number of DQ engagements actually looks different, that's because this is just for 2012, it doesn't show everything we did for 2011. So this works for most upper management that kind of like, oh, I just want to see where's the business value or how many of our fields are in amber or red? But for the next group of folks, usually our data stewards that are drilling down and looking at this stuff, this is the next lower level. What's, I think we've covered most of this except the current week and the previous week and they love to see the trends. So we show them whether the trend is up or the trend is down, okay? And then that tells them, you know, geez, I really haven't had time to go look at this in the last week or two and the trend, I mean, it's going down, I better do something about it. And we're constantly struggling with, you know, getting people to have enough time to work on some of this because this is just one of the roles that they play and they have full-time jobs doing other things but once in a while the crisis situation comes into play or an issue, like maybe something with addresses comes in through the compliance, you know, the customer address to the group and so it gets, then it gets attention. And then the other area fields are the things that we've already talked about. This gives you more detail on the things that we're tracking on the program performance and some of the things that we do, we provide consulting support to projects so we're tracking that. We may actually implement projects. In other words, we do all the business rule development and kind of take charge of the whole thing and we treat those projects separately than just, you know, helping out a business area or providing consulting. We can, we do training on the tool so a couple of my folks, if there's a new area, like a new finance group or somebody on the enterprise data warehouse team that wants to be trained, we'll do that. We support the tools so we're constantly doing the upgrades and just everything that's associated with that in addition to the ongoing monitoring. Just like for data governance, when we had an issue tracking database for that, we have one that we're tracking for the data quality issues as well. And this allows us to generate reports. We have a website where we put all our reports, we slice them and dice them. You can see all the DG issues, all the DQ issues, all that are opened, all that are closed, all that are work in progress, any way you want to look at it. And it helps us stay on track and so we don't lose track of what we need to be working on and following up on that kind of thing. Everything we're, well no, it's kind of like a, it depends. I think most of the issues will show up in the bottom left quadrant. That's when I said there's ad hoc ones and then the ongoing monitoring. We tend not to create an issue if it's like a project related thing. We keep another spreadsheet for the stuff on the right hand side.  Yes, yes, and you guys, yes. So we have a whole data, well it's actually the same database and that we can separate out though our data governance issues from the data quality. And in fact when I was talking about DG 2012-005, well the DQ issues are DQ. And to use the, because when we started out, we weren't that fine grained and then you find after years and years of doing this, it's hard to keep track. So by putting the year in the ID for the issues has really helped. So think about something like that over time is helpful. Sure. A good data governance issue might be helping to define the business rules for data coming into our corporate information system where they're asking which source system trumps the other. So it really has nothing to do with quality. It's more a business rule driven thing or it's standardizing a field like we have some examples now that we have data that we're getting from another system that's outside our organization. So for the enterprise data warehouse, we've got apples and oranges. How this field is defined in our systems is really a month end versus the system we're using. It's a cycle date and the cycle date isn't necessarily month end. So the council will work together to derive what the definition should be or is it two fields. So it's not, doesn't necessarily have a quality issue per se. Does that help? Okay. Everybody always wants to see this. What does our infrastructure really look like? So we have, we use Dataflux. That's our data profiling tool. We run against, and we actually do most our querying against our enterprise data warehouse, which is in Natisa. And we go against the staging tables. So the data is exactly how it appears in the source system. That just made it very easy for us to do like one stop shopping. We don't have to have access to all these systems and the nightmare with security and all of that. So we've been lucky most of the 80% of what we're looking at, we can look at through the EDW staging tables. So the tool runs against that. We store the results of those runs in Oracle database. And also then the Excel spreadsheets we talked about that has the business value calculations. Our dashboard was developed with OBIEE, Oracle Business Intelligence. So that's basically what we have in place. Very quickly, we ended up creating this graphic to quickly show the business, the data stewards, what it is we were expecting of them and what the process looked like. So behind that could be a couple of pages of detailed action items for them to do, but basically just to walk them through what we've been talking about. They're gonna prioritize and give us the information. The data quality service this team is gonna do the monitoring. And then it's brought to the council to look at and then based on these monitoring results, if it's green, it stops there, right? Nothing else needs to be done. If it's amber or red, they're notified and I'm gonna show you on the next page how we do that. And then it's up to them to go in and look at the data, figure out if they can determine root cause analysis, what's really wrong, get the current records fixed, maybe there's something has to be put in place to prevent it from happening again and communicate through the whole thing. What we do, this is really nice, it's an automated email that goes out to any approver who has a business rule that shows is amber or red, okay? They'll get a notification and automatically gets sent to them and says, by the way, you might wanna take a look. One of your field's business rules needs to be looked at. And we decided as a team, we wouldn't send it to anyone if it was green. They could just assume that, because nobody likes getting emails all the time, you stop looking at them. So I included this as an example, and then we also put together this quick little chart for the approvers to kind of tell them what their responsibilities were. We always do kickoff presentations where we've got presentations for the data stewards or the IT folks and part of those presentations is what's expected of you? What are the roles and responsibilities and in this case, kind of accounts for what do we expect you to do with the business rule when you're looking at it? And if they're not the ones to fix it, they are the ones ultimately responsible for it. Okay, so it gives them all that information. So going forward, and I think we're just about on time, you know, my focus is gonna be doing more and more of the actuals. We're trying to get the data cleaned up and fixed so that we can show more value to the company, which we'll continue to do. That's where the root cause analysis and then tracking of all the fixes needs to be. And then continue to expand. Since we do a lot of project work, especially the loan acquisition area, we bring deals of loans onto our systems, finance, many areas in finance, you know, they ask us to help with just data reconciliation. We're actually using in our collections area as well now. They do a lot of manual work, or for example, we provide a service to schools where we're tracking their cohort rates, which is like basically their default rate by segmentation of students because they'll lose their standing with the Department of Ed and they won't be able to offer federal loans to their students. So we will actually profile the data and the files that come in from the schools. And now we took a very manually intensive two week process that now is automated and kind of runs 24 by seven that says, okay, these files that came from X University have some bad data and we need to let them know and they need to resend to us in order to get that rate put out. Dataflux, Dataflux is the tool we're using. Any other questions? Well, the tables that it's, okay, so if anyone can hear, he asked why do we use the warehouse and why not check closer to the source? So we're checking on the warehouse in, well, we have these staging tables, they're exact replication of the source and in fact, we run the profiling tool to verify that. So it's checking A to A or A when A went to B, it's still A. So in a way it is the source and it was a way to cheat an easy way where we could go to one place, get all the data we needed because we're looking at multiple sources without going through security and then worrying about, even though it's a read-only tool, someone's saying, well, this is our busy time, it must be you guys that are running, they're slowing us down, that kind of thing. So it's kind of just, it has worked out really well. So if you have that opportunity, that should work well for you. Any other questions? I know it was like drinking from a fire hose. Good luck with your metrics. Let me know if anybody has any questions. Thank you.


It's useful to come up with a function that fits the data so we can use that function to interpolate or extrapolate or just make other predictions or forecasts relevant to this data set right here. But sometimes finding the right function to fit can be a little bit of a challenge. Like if you look at this thing right here, you might think, oh, I'll do a linear regression. And although you can, the linear regression doesn't quite seem to work, right? I mean, there's no one point that really captures it perfectly, right?  I mean, this one's pretty good right here. But just looking at it, it doesn't really feel like a line. It feels like the data's got some more curvature to it. And you might be like, okay, well maybe it's like a parabola or something like that. In which case you might get something like the following. But the problem is a parabola eventually will have to bend back down. This one kind of wants to go up like this. So if you really want to find the shape that kind of best fits it, we need a higher degree polynomial. Maybe something that kind of like levels out in the middle that spikes up again. This looks a lot like maybe y equals x cubed or something like that or some other odd monomial function. So maybe we try to make a fit there. Well, if we have zero right here and zero right here, the origin of our graph is gonna be about right here, which doesn't seem to be about the vertex of the parabola here. There's gonna be some type of shifting going on here. So we have to accept the fact that this monomial could be shifted in some kind, in some degree. And so the general formula we probably wanna use would be y equals a times x minus h cubed plus k. So we accept the fact that there could be a shift left or right, up or down shift. And then this scalar right here will compensate for any vertical or horizontal reflections or stretching or some kind. You will notice the in behavior it's pointing up on the right and pointing down on the left. So this already tells me that my coefficient a should be positive. I can see that. So we wanna find essentially the vertex of this graph right here. Where it was like the middle of this cubic, which in the untransformed version would correspond to the origin, that seems to be about right here, I would say. And so try to estimate that here. x-cornered seems to be about a one. I'll go with that. And then the y-coordinate, if we bring this over, it seems about to be zero, right? Okay. So I'm gonna go with just like eyeballing this thing here, one comma zero, I want that to be my vertex. And so with regard to my function then, I'm gonna get y equals a times x minus one cubed plus zero. That's great. You don't even need the plus zero there. So we still have to determine this coefficient a, right? So what we're gonna do is we're gonna look for a point on the graph, which seems like it matches up with the graph and we're gonna plug that in there. And so you could use an x-intercept or y-intercept if you wanted to. The y-intercept gets a little bit problematic because it looks like it's going off the screen and this is really steep. So we're not gonna go with that one as well. We could try the x, as I'm sorry, the y-intercept would be over here. What am I talking about? This isn't the y-axis.  This is the y-axis right here. Notice this is x equals zero. So we could try to go with the y-axis. It's really close in height to this point. So I think for a better fit, we're gonna be better off looking for a point elsewhere. And so maybe if we grab something like this right here, this seems like a pretty good point. So we have x equals negative two and then the corresponding y-coordinate over here. So it's a little bit below negative 20. Here's a negative 40. So maybe we say something like negative 25. That seems feasible. And so then what we're gonna do is we're gonna plug in for y, negative 25. We're gonna plug in, well, we don't know a for x. We're gonna get a negative two and then we need to compute this thing right here. So we got a negative two minus one. So it's gonna give us a negative three. When you cube that, you're gonna get negative 27a have a negative 25. And so then solving for a, a would look like negative 25 over 27, negative 27. So it's just double negative makes it a positive. And so putting this all together, my function I'm estimating, this would be y equals 25 over 27 times x minus one cubed. This seems like a fairly good estimate of the cubic function that matches this data. And again, this isn't perfect. I just kind of eyeballed this one. Computer software and statistical analysis, linear algebra, some more advanced techniques than your typical college algebra could be used here to find a cubic curve of best fit. Now here, I'm just trying to talk about how one could find a cubic curve of good fit. And we're just gonna eyeball it here. If we want a better, as soon again, computer software is necessary, but I wanted to demonstrate how a human could just sort of use their intuition to find and some college algebra here. They could find a polynomial that fits the data fairly well.


Snapshots from 1998, a season of greatness. On May 19th, the Yankees were hosting the Orioles when Bernie Williams hit a go-ahead free-run homer. Tina Martinez was drilled on the next pitch from Armando Benitez, sparking a massive brawl. When the dust finally settled, Tim Reigns homered on Baltimore's next pitch, sending the crowd into a frenzy. The Yankees won the game 9-5.


We're going to move to our next session now in which we are going to start considering different responses to the COVID-19 pandemic, and I believe we will next hear from Professor sacks on his chapter for reasons for the Asia Pacific success in suppressing COVID-19. Great. Thanks very much Lara, and thanks for that wonderful presentation we just heard. I think one of the most dramatic and in some ways puzzling aspects of this past year is the hugely differing outcomes of this pandemic in terms of the nature of its transmission and mortality rates. And I will just put up one picture if I might, which is the death rates per million population in the world as of yesterday's data. And what you can see is the world experience with this pandemic has been vastly, vastly different with the highest death rates in Western Europe, the United States and Latin America, and much, much lower death rates in Asia and in Africa. So, this raises huge puzzles, and they're certainly not solved by any means. What accounts for the fact that the world's richest regions, the United States and Europe did so badly, usually when we look at a map the hardest hit regions are the poorest regions. Here it's exactly the reverse. It is the richest regions, the regions with the full hospital systems and so on, that ended up a disasterously, and to put some numbers on this as of yesterday, there were 541,000 deaths in the United States, cumulatively counted attributed to COVID-19. In China by contrast, there were 4,800 deaths. So roughly 1,000th, I'm sorry, 100th of the number, and with China having a four time larger population, that means roughly a 1,400th the mortality rate in China compared to the United States. In the UK, there were 126,000 deaths. So per million in the US, 1,600 per million dead. In the UK, 1,800 per million dead, whereas in China, 3 per million. In South Korea, 33 per million, a 50th of the US and UK levels, Australia 36 per million, New Zealand 5 per million. So what can account for these differences? Well, partly structure of demography and probably structure of social contacts day by day can explain some of this. Africa's population and South Asia's population is much younger, and we know that the mortality rates are far higher for people over the age of 65 than below the age of 65. If you do an age adjusted mortality rate analysis, you find out that you would expect all other things equal for Africa to have about 1,10th the mortality rate per person of the US and Europe. So, without question, age structure matters. Comorbidities matter a lot also. We know that overweight and obesity is a huge comorbidity with COVID-19, and the populations in Europe and especially the United States are overweight to a very high extent with much, much lower overweight and obesity in other parts of the world. But even accounting for these structural variables, one could add in urban and rural because it seems that transmission intensities are lower in rural areas because of lower density and also because of more outdoor contacts relative to indoor contacts, and even controlling for these structural variables, there is a dramatic puzzle with regard to the Asia Pacific region, which for our purposes is China, Korea, Japan, Australia, New Zealand, and the countries of ASEAN, the 10 countries, especially Vietnam, Cambodia, Laos and Thailand, which have had very, very low mortality rates per capita. Structural differences in demography do not account for the differences with Europe and the United States. Also, urban versus rural do not account for the differences. Comorbidities do not account for the differences. There's just, in addition to all of those clear structural factors, which do help to explain the situation in Africa, for example, something more is going on. And several of the papers in the World Happiness Report this year tackle that question. So what is that added that's going on? Clearly, in part, it is the policy response that the Asia Pacific countries were more pried to react to the news of a new SARS-like disease because they had confronted SARS in 2003. And they had had more preparedness for other emerging viruses since then, H1N1, though that was a global pandemic, there was more preparedness and response. The Mears virus, which hit in East Asia in 2014, 2015, again, primed the Asia Pacific region to more preparedness. So we see that the responses of policy early on were definitely better. We also see that the European and North American and Latin American countries did not learn from the experiences in the Asia Pacific region, even though this difference of performance was already apparent as early as last March. Because I started writing op-eds all the time saying to Americans, look, the situation is under control in the Asia Pacific. Why don't we do the same things? I can tell you it made almost no difference. I believe if any difference whatsoever, Europe and the United States did not learn from the Asia Pacific in this. So that raises its own puzzles about governance, about the public attitudes, about the culture of our societies that I don't think we can push aside, nor do I think we can definitively solve at this stage. But what do we know? We know that the Asia Pacific countries undertook what came to be called non-pharmaceutical interventions or NPIs. That included all of the usual behavioral steps of wearing face masks, physical distancing, stopping visits, self-isolating with symptoms, but it also involved a very intensive active surveillance system by government where the public health system was tracking each outbreak, looking at the backward contacts to where that infection case may have come from, and looking to the forward contacts of where to whom it may have spread with the idea of understanding the super spreading events on the one hand and stopping the further transmission of the virus on the other hand. So that so-called test, trace, and isolate system was put strongly into place throughout the Asia Pacific region, but it never got into place in Europe and the United States, rather shockingly. At the beginning, the systems were overwhelmed, the preparedness was low, but last summer, after the initial lockdowns, case levels had come down, and one might have thought and expected that our governments in the North Atlantic region of Europe and the U.S. would have said we now introduce, with this lower case load, comprehensive active surveillance means to stop the transmission because we see that in effect has what has been done in the Asia Pacific region. Stopping transmission does not mean zero cases, incidentally, it just means that you have near zero transmission in the community, but new introductions of the disease usually from residents returning from abroad, for example, or people who slip through border controls without symptoms, but then end up introducing new infections. Well, again, the Asia Pacific region did these things, but the U.S. and Europe have to this day never introduced systematic active surveillance. It's rather a catastrophe. I had a theory all during 2020 that the answer was Donald Trump, because we had a psychopath as president, but he went away and we still have not really solved this problem. So, definitively, we're waiting for the magic of the jabs of the vaccines rather than the effective combination of personal behaviors and active surveillance measures, so we still don't have that kind of control system and Europe is back in lockdowns, rather than the active surveillance systems. So, my paper just to come to the conclusion because I'm mostly puzzled, rather than having definitive answers, is to understand both the top down differences of policy performance, and the bottom up differences of societal behavioral actions. And YouGov has given us wonderful data all through the pandemic to understand what people are doing. And suffice it to say in these figures that you can't really see closely, these are behavioral responses where red is the Asia Pacific region and higher means more active response. So you can see that the red squiggles, which are the Asia Pacific countries during the past year, did much more control, whether it's wearing face masks, whether it's quarantining, and so forth, then did the countries in Europe and the United States tracked by the pandemic. Similarly, in the US and Europe, there were many more demonstrations. This is for up to January 2021 demonstrations against wearing face masks or against lockdowns. There was one recorded in Auckland, New Zealand. That's all in the Asia Pacific region. But Europe and the United States had many, many protests. And this is a cultural matter, great resistance to the kind of systematic control efforts that were undertaken successfully in the Asia Pacific region. I've also asked people what they thought about the compliance of citizens in the country and that's shown by the red dots. And the countries with low compliance, by and large, are in the Americas and Europe, whereas the countries with high compliance tend to be in the Asia Pacific countries. So compliance by the public was quite different. Sociologists, cultural students of comparative culture have looked to various measures of individualism as a possible explanation for this different performance. And indeed, if we relate these indexes of individualism to wearing face masks in public, for example, we find significant variation, correlation of higher individualism, meaning lower wearing of face masks, more resistance as well to answering questions of contact tracers on the grounds that they are invading privacy, and so on. One last piece of data that I think is interesting is that if you correlate the measure of compliance or adherence to anti COVID policies, which I showed in this previous graph if you correlate these red dots across country with the score on scientific literacy of 15 year olds across countries that's part of the OECD education evaluation, you also find a very strong relationship that countries with higher science literacy, which tend to be the Asia Pacific countries are much better as well at adherence to public So I believe there's something to this that much of the American public simply doesn't get it. It's the same reason why there's a lot of vaccine resistance. This is susceptible to ignorance, fake news, infodemics that reflects a very poor scientific literacy. And this may be another warning sign for our societies, you may be rich, but if you don't get it, you won't behave properly. And I think that what we're seeing in Europe in the United States is weak policy making bad behavior by the public's and the mutual interaction in both directions. The politicians are afraid of the public, and the public doesn't trust the politicians. The result has been a year of ongoing pandemic, even though we could look back 12 months and see how to stop the transmission. We could have seen it in action, but we refused to look, and therefore we've had another year of agony, compared to what could have been contained already 12 months So let me stop there and turn it back to you, Laura. Thank you very much for that presentation, Jeff. We will continue this conversation with presentation of chapter three in the report by Dr. Woo. Yes. Great. So, it's my honor to be here and present our chapter, COVID-19 prevalence and well being lessons from East Asia. This is a joint work with Maiming Ma from Shanghai University of Finance and Economics, and from KDI School of Public Policy and Management. So after listening to Professor Seck's presentation, I realized we have some overlap with chapter four, but here we are going to provide you with more case studies in East Asia. Let me first summarize our main findings. Our COVID-19 data revealed that the five East Asian regions, mainland China, Hong Kong SAR, Taiwan, Japan and South Korea have much lower infection rates than the major Western societies, including the United States, the United Kingdom, Germany, France, Italy and Spain. And our quantitative and qualitative analysis shows that strong government response systems, as well as the combination of rigorous non-pharmaceutical and pharmaceutical measures, or contribute to the success in East Asia and the Pacific. Third, we find culture does matter. However, with timely and stringent government policies, COVID-19 can be successfully contained in countries with cultures quite different from those of East Asia. Last but not least, we find having stricter lockdown style policies could in fact considerably offset the decrease in happiness due to the rise in daily new confirmed cases. So first, we provide an overview of the COVID-19 situation in East Asia. And this figure 3.2 from our chapter compares the total case per 100K in five East Asian regions in the study, Australia, New Zealand and the other six Western countries, including France, Germany, Italy, Spain, United Kingdom and the United States. Here we see Australia and New Zealand appear in both panels, so they serve as the reference group. If you look at the vertical axis of both panel, it should be quite straightforward to conclude that those East Asian regions generally had much lower infection rates than the six Western countries. And most strikingly, by the end of 2020, the infection rates of the six Western countries were about 11 to 33 times on the rate of Japan and 340 to 992 times the rate of mainland China. What about Australia and New Zealand? They are indeed the outliers because they are considered Western countries, but they have the infection rates quite similar on to those of East Asian regions rather than those of the six Western countries. And we were also explored the potential reasons for that. Next, we look into the government responses, which we believe should definitely contribute to the low infection rates. We first compare three general dimensions, mobility control and physical distancing policy, testing policies and contact tracing policies in the five East Asian regions and the six Western countries. For lockdown style policies, we find during the initial outbreak, the first East Asian, the five East Asian regions implemented stricter interventions than the four Western countries, including Germany, Spain, the United States and the United Kingdom. And during subsequent waves, we find a few Western countries, especially France, the United Kingdom, and the United States still lacked timely government responses for testing policy. At the earliest stages of the outbreak, South Korea, Hong Kong, SCR, and Taiwan already implemented the most comprehensive testing policy, such as open public testing. And during subsequent waves, all the East Asian regions, except Japan, make testing available to the general public. In comparison, only three out of the six Western countries have similar levels of testing. For contact tracing, we find the four East Asian regions, except Japan, also made much more aggressive and consistent contact tracing efforts. What about Australia and New Zealand? We found Australian and New Zealand had quite comparable contact tracing policies, testing policies, and also physical distancing policies as the East Asia countries from the very beginning of the outbreak. So this may explain why they perform, why the outcome in these two countries were much better than the other six Western countries. Next, we also take a closer look into the government response systems in the five East Asian regions. In the chapter, we actually describe the strengths and specific policies taken by each region, but here, because of the time constraint, I'm going to summarize the key characteristics of the East Asia region. First, the response systems in the five East Asian regions have been under a national-wide directive, and there have been multi-sexual coordination, as well as central local government cooperation. And they all have very strong non-pharmaceutical interventions, including comprehensive mobility controls, extensive testing and contact tracing, mandated quarantine for confirmed cases, suspected cases, closed contacts, and inbound travelers, and they are enforced self-protection practice such as mask wearing. And for pharmaceutical interventions, or the five East Asian regions provide free treatment, and a few of them require hospitalization of mired cases. And we believe civic engagement or civic cooperation also contribute to the low infection rates. According to Hofstede's national culture model, the five East Asian regions have lower levels of individualism, higher levels of long-term orientation, and lower level of indulgence. And those cultural traits are highly related to responsible personal behaviors, including mask wearing, improving personal hygiene, and maintaining physical distance. However, we believe that with decisive and stranger government policies, COVID-19 can be successfully contained in countries with cultures quite different from those of East Asia, such as in Australia and New Zealand. So we tend to conclude government responses might be more important than cultural traits in this battle against the COVID-19. Finally, we look into the effects of infections and actions on emotions. Here, I'm going to briefly describe our data and measures because I think this might be of interest. So our data for mainland China come from nearly 34.5 million microblog tweets posted on the Chinese largest microblog platform Sina Weibo. It covers December 1, 2019 to April the 30th, 2020. And we apply a machine learning approach to generate a city-level expressed happiness index, which ranges from 0 to 100. For the other four East Asian regions, we collect data from Google Trends on relative popularity for well-being related topics, and the data cover December 1, 2019 to August 31, 2020. We derive a negative effect search index by taking the simple average of the relative popularity of six topics of negative effect, and we use this index as a proxy for negative emotions. About the results for mainland China, we find first, a higher number of daily new confirmed cases is associated with a lower level of happiness. And a higher level of daily new recovered cases is associated with a higher level of happiness. What about the policies? We find more stringent lockdown style policies by themselves contributes to a lower level of happiness. However, fortunately, stringent policies could significantly buffer the negative effect of a rise in daily new confirmed cases. For the other four East Asian regions, we find similarly, a rise in daily new confirmed cases is associated with an increase in negative emotions. But stringent policies is associated with a decrease in negative emotions. And also stricter government policies are able to moderate the rise in negative emotions due to the rise in daily new confirmed cases. So here is the main takeaway from our chapter, the timely and strong government policies in East Asia, not only helped to control COVID-19 effectively, but also protected people's happiness during this pandemic. So we hope this chapter can give some policy implications about how to control the pandemic and protect people's happiness. Thank you very much. Thank you very much, Dr. Wu. We've received a couple of questions that I will try to consolidate because we are tight on time. So I'll try to pose one or two for both Professor Sacks and Professor Wu. The first is perhaps an opportunity to summarize. We received a couple of questions acknowledging that America and now Europe are hit by a third wave and a request or an inquiry into what governments can do at this time to increase compliance and help contain the pandemic. From our chapter, we learned that non-pharmaceutical interventions are very important. So if there are resurgence in certain countries or regions locked outside policies like workplace closure or school closure, these policies should still be very effective in containing the virus. Wonderful. Thank you.


It's now strangers and people I have to build trust with my company trust with coming in and carrying my product and Representing so how is that how is that made you up your game as a leader to start working with folks that you don't already have a Personal relationship when I worked at the restaurant like I said for the management before I became a manager there I was I got turned into he Nominated me to be the restaurant trainer So when I left pit stop I had trade every employee on that floor by the end of my 10 years So I was already used to taking a stranger and teaching them the way of how it's done here This and that learning to push them motivate them turn it into a game make it fun You know a lot of when I'm cooking I feel kind of like I'm in a video game So I do everything in a certain order. I've got all my pans lined up


minimal. Yeah. But nonetheless, the board has left me the best for your review. What do you want from us? I think it's just a, you know, a vote of which... Vote to endorse the the Whole Foods logo and liability language as presented. Second. All in favor. Thank you. I hope you'd like to take a little rest. Yes. And so, I'll move to adjourn. Second. You're going to also move your minutes. Minutes. All public hearings are now closed. All public comments. Move to adjourn. Second. All in favor. Thank you guys.


The House of Squib presents the finest in motion picture entertainment, Academy Award. The House of Squib, manufacturing chemist of the medical profession since 1858, bring you Academy Award. The pictures, the players, the techniques and skills which have won or been nominated for the coveted awards granted each year by the Academy of Motion Picture Arts and Sciences to each in his field for outstanding achievement. Each week, Squib on the Air brings you only the finest performances. Squib in your home brings you only the finest medicinal products, pure, effective, reliable. Squib, a name you can trust. Tonight's picture is Kitty Foil. Tonight's star is the distinguished RKO player, Ms. Ginger Rogers. And now, Ms. Ginger Rogers. Thank you. And good evening, ladies and gentlemen. My hope is that some of our excitement and pleasure in reenacting these Academy Award rules will be communicated itself to you through our performances. Also, it would be ungrateful of me if we were to do Kitty Foil tonight without thanking the men who brought Kitty to the screen. So thank you, David Hempstead and Sam Wood. Kitty Foil was written for radio by Frank Wilson with an original musical score composed and conducted by Lee Stevens. And our producer-director is D. Engelbach. Academy Award, starring Ginger Rogers in her 1940 Academy Award-winning role of Kitty Foil. I can't pop. I can't. I'm going too fast. I'm on a sleigh ride. A sleigh ride? Well, it seems my whole life was spent on a sleigh ride. First it was Winstafford, my mainline Prince Charming. But now it seems to be Mark Eisen, a good dependable trustworthy Mike. Mark, it was spring again in New York. And as Mark drove me home that evening, I had a hunch that this might be the spring of all my years. Here we are, Kitty. Oklahoma's hotel, home of lonely women. Kitty, wait. Yes, Mark. Kitty, will you take this ring? Mark? Well, you see, I've got lots of money tied up in this golden hoop, and there's no other way I can get any use out of it. Well, can't you find my finger, Mark? You, you did say yes, didn't you? I mean, it's, it's, it's all clear. There's no confusion. You understand what I asked you. You asked me to marry you, didn't you? Oh, that's it. Exactly. I got it. That's why I said yes. Kitty, when I'm getting around to that, that fellow in Philadelphia, that's all over, huh? All over. Oh, then we're getting married tomorrow. Meet me at the hospital tonight, huh? Midnight. We'll go straight to Greenwich. Yes, Mark. I guess this will be farewell to the Pocahontas.  Goodbye for me.  And meet me at St. Timothy's Hospital at 12. Smack on the dot. 12? Smack on the dot, St. Timothy's. Ah, good evening, Miss Foil. Good evening. My key, please. Thank you. Oh, you make up my bill tonight. Oh, you're leaving? I'll say I am, Joe. I'm getting married.  Right. When? What are you doing in my room? You sent for me. I sent for you? You sent back the ring. Kitty, remember what I told you? If you ever needed me or wanted me or would have me, send the ring back to me. Oh, I forgot. That wasn't what I meant. I sent it back because that was all. That was the end. Don't say that, Kitty. Oh, Kitty, there's no life for me without you. I want you. I need you. I love you this minute as I've never loved you before. But when it's too late, five years, too late. No, Kitty, it'll never be too late.  I'm asking that we leave together for South America and be together always. Oh, when? Don't ask me anything. Don't let me think. Oh, darling, at last it all comes true. And will we live happily ever after? Forever and ever. When will we go? We sail at midnight. I'll be at the pier with everything arranged. Now, will you meet me there? Pier 48, midnight? Oh, Kitty, I love you. Don't be late. Bye-bye. When? I... When? Kitty Foil, here we go again. Maybe this time it's for keeps. You're making a mistake, you know. You're still on that sleigh ride. Oh, marriage isn't everything. What is it anyway?  A lot of fine things come out of that little piece of paper, Kitty. A home, children. That's where Mark comes in again. And you'd be a lot happier with Mark and that little piece of paper than you ever could be with Win. You know what I think? I think you're wrong. I remember you using those words once before. Remember way back when you lived on Grisken Street in Philadelphia and Pop? What a grand guy he was. Ah, Kitty. I guess an old heathen like me never had any business trying to raise a daughter anyhow. I wanted to keep you from stubbing your toe. I thought you had all this junk rooted out of your mind. What junk? Cinderella and her blasted prince. Kitty, don't you see it's... Oh, it's no good. Don't argue, Pop. I love him. Judas Priest. You said it. You mean you want him, Harry? Winstraphan. Has he ever asked you to meet his family? Pop, he doesn't even know I love him yet. Big secret. But I've never worried much about his family because I've got a funny idea. I'm just as good as they are. Kitty, by Judas Priest, you're going to break your heart. It was so easy to fall in love with him. You were doing it from the start. Falling in love with your boss. But there's no getting around it. Those were probably the happiest days in your life. Crazy, but nice. Like the first time he took you to New York. I've never been in a speakeasy before. Best people in New York come here. And here we are, Mr. Strafford. My last bottle of stragg. Oh, thanks, Giorno. Oh, what's stragg? It's Italian brandy. Oh, I know. Say, if two people drink it together, they'll never drink it apart. When? Why did you bring me here? Well, four or five years ago, I got into this place of Giorno's. I liked it. I liked it because all of a sudden, I felt free here. Philadelphia, the main line, all of those things that border my life. They disappear the minute I enter that door. Well, I wanted to make a good impression on you, so I brought you where I thought I most likely could do it. Oh, well, I didn't mean to criticize you. Kitty, I've got an idea.  Me? Are you kidding? No, cross my heart. That's funny. You know, when I was a little girl, I used to read in the papers about the assembly in Philadelphia, cut out the pictures of the society ladies in their beautiful dresses, and use them for paper jobs. Ah, that's probably the best use they've ever been put to.  You leave that to me. My mother's on that committee. Oh, when?  No, but I'm still hoping. Is it a date, Kitty? I'm crazy, I know, but it's a date. Come on. Let's get our wraps and get out of this smoky den. Where are we going? I've got to go home pretty soon. We're going to Lake Pocono just for an hour. See the sunset. But it's already set. We see the moon rise. I'll get you home in time for supper. Come on, Kitty. It's not much of a drive, and you belong in Moonlight. Lake Pocono and wind's lodge in the mountains snug and comfortable, exciting, and a million miles from everything but Moonlight.  Funny how love makes a woman quiet and a man talkative. But Lancelot Mew is the little space, he said. She has a lovely face. God and His mercy lend her grace. The lady of Chalot. Isn't that beautiful, darling? Tell me about beauty, teacher. Gladly. As you know, it's a man's duty to instruct woman in all subjects. Now you pick the subject. No, I've only got a few minutes. But tell me where we are. We are in the Pocono Mountains situated in the state of Pennsylvania. No, but where are we really? In heaven? No. Tell me about. Well, first there was a man and there was a woman. Oh, what did the man and the woman do? Well, at first they just hung around. And one night a strange thing happened. What?  A man for the first time saw how beautiful she was. Looking into her eyes, he suddenly beheld all the wonders of life. So, immediately, he made love to her. Well, uh, didn't the woman object? No, she loved him, too. Why? Well... Because he was everything that she had ever dreamed of. Oh, my darling, I love you, Wim. It couldn't go on like that. It just couldn't. Who did you ever think you were? Kitty Foyle, that you could live rent-free and forever in paradise? Didn't you see it coming? Didn't you know the magazine was bound to fail? Couldn't you tell that he would say the things he said? Because, well, being him, he couldn't have said anything else. Did you have to hurt inside as if he'd stabbed you with a knife? Yes, Kitty Foyle, you did. Well, the magazine's done. It's folded. It's finished. Ten thousand my mother gave me spent. Now, what will you do? Me? Oh, that's right. I'm out of a job. Well, I might get one in New York. I can't let you do that. Why? Why not? Because it's just silly. Besides, it'd be too far apart. Well, it isn't China. Kitty, well, this is kind of delicate, but... Yeah, well, go on. Your father isn't very well, and you're all alone, and it's too much for you. I mean, I feel that I'm kind of responsible. It isn't your fault the magazine failed, and so... Well, until you get another job, I don't want you to have to worry about money. What do you mean? I'll just... just keep you on the payroll. It's only fair, you know, because... Just a minute. You don't need to worry about me, Win. I'm free, white, and 21, or... almost, and I'd love you from here on out or until I stop loving you. But nobody owes a thing to Kitty Foyle. Except Kitty Foyle. Kitty. Wait. Kitty! Two of Ginger Rogers Academy Award-winning performance of Kitty Foyle. Let's look in on the RKO lot where they are shooting the final scenes of Ginger's new picture, Sam Wood's production of Heartbeat. All right, boys. Here we are. Much like Alice in Wonderland, isn't it? Walking into a strange new world. Had you any idea it took so many skills and crafts to make a fine picture? Here's a young lady we might persuade to talk to us for a second. Oh, Miss. Hello. Hello. May I ask what your job is on this lot? Sure. I'm a makeup girl. Right now I'm working on Heartbeat. That's Miss Rogers' new picture, you know. Yes, I know. That must keep you pretty busy, doesn't it? I'll say it does. But it's worth it. Just wait until you see Ginger Rogers in this one. Well, I'm certainly looking forward to that. But tell me, in your job of makeup, I imagine the smile is pretty important, isn't it? Yes, sir. The smile is everything. Well, same in my business.  Everyone enjoys the minty taste of squib dental cream. It's so cool and fresh. Yes, I know. And did you know that the polishing agent in squib dental cream is one of the safest, softest, yet most effective known dental science? Well, I'm glad to know. And did you know that pure squib dental cream has to pass more than 100 separate tests before it touches your brush? It's just one more evidence of the scientific care that goes into the making of this quality dentivus. Use squib dental cream regularly. Taste, feel, and see the refreshing difference. All for Part Two of tonight's picture, Kitty Foil, starring the Academy Award winner, Miss Ginger Rogers. You thought you were off the sleigh ride, Kitty Foil, when you took your shattered little pieces of pride and went to New York to ring the Liberty Bell? But you were wrong.  You were still on that sleigh and you had a ticket in your heart for the entire trip. Molly, where are you? For heaven's sake, what's going on here? I don't know. I got home and they started coming. Somebody's nuts. I know the delivery boy like a brother right now. You should see what's in the bathtub, water lilies, and look at these. A hot house we got, a million dollars with the flowers, and they're all for you. For me? Well, who are they? How did you ever find me? I just followed my heartbeat. Here, your presence. Shall I go outside while you dress or can I just close my eyes? What do you mean, dress? We have a date tonight for the assembly. Only ours is going to be right here in New York. Oh, when? You remember. I hope you like this dress. Oh, go let me. Oh, when? Kitty, Kitty, I forgot to tell you. What? How much I love you. Oh, how much do you love me? If I said as much as you love me, would that be enough? Darling, if that were true, there wouldn't be any love left for anybody else in the whole world. Our very own private assembly ball. At the Ritz. Here, I've got something for you. What is it? A ring, darling. Wow, art. But it's beautiful. It was my great-grandmother's.  The snake eating its tail, you see? Oh, I see.  My great-grandmother and me and you and those that come after us forever. It's our family. When? Why are you giving it to me? Kitty, will you marry me? I can't. You love me now only because we're not in Philadelphia. I'll fix that. Boys, would you play the sidewalks of New York, please? What's the idea? Oh, that's our song now. We're New Yorkers, both of us. Oh, darling, no mainline. No Philadelphia. Just you and me, me and you. Dear God, please don't ring the alarm clock for just a little while. Let it go on just as it is now. Just as it is now. Oh, he heard me. It's Sunday morning. Wedding bells, darling. Wedding bells for us. And so you were married, Mr. and Mrs. Winwood Straff of the 6th. Remember you read it over and over when Win wrote it on the register of that little hotel in Gretton, Green? There's no use denying it. Those two days were just about perfect. And then you went back to Philadelphia to tell Win's family. Hello, everybody. Win, dear. Come in. You remember Kitty Mother? Nice to see you again, Miss Foyle. Thank you. You remember my grandmother? Jessica, Uncle Edgar, Uncle Kenneth? Oh, glad to see you again, Miss Foyle. Well, the fact is the name isn't Foyle anymore. It's Straford. Kitty and I have been married. Oh, why don't you say something? Or is she so beautiful she's taking your breath away? You will have to forgive me, my dear. I just wasn't prepared for such news. You'll understand, don't you? Oh, of course. It is a surprise. Shall we sit down? Will you have tea? No, no, thank you. When were you married, dear? Monday. I see. We thought, naturally, that Win would wait a year. But you understand, of course, that above everything else we want you and him to be happy. That's first and foremost in our thoughts, isn't it? Oh, yes, of course, my dear. Well, I don't want to seem rude, but would somebody mind telling me what you're driving at? Mother, you see, honey, I promised I wouldn't marry you for a year.  Prepare me for what? You know, some school, some good finishing school. School? Are you kidding me? No, no, my dear, let's keep calm. Now, look, let's get a few things straight around here. I didn't ask to marry the Straford. The Straford asked to marry me. I married a man, not an institution or a bank. Oh, I've got a fine picture of your family conferences here. All the Strafords trying to figure out how to take the curse off Kitty Foil. Buy the girl a phony education and polish off the rough edges and make a mainline doll out of her. Oh, you ought to know better than that.  I haven't got that much time. Fine words, Kitty Foil. Back to New York in your old job. It's not living, though, when every time you hear a knock on the door, you hope it's somebody you know isn't going to be there. When every time you walk down a street, you think you see him coming through the crowd.  Poor Mark, he would pick Gionos. It's a matter, Kitty. Don't you like Gionos? Good evening. Oh, give us something kind of special, huh? Maybe Straga? Yes. Yes, two of them. Kitty, there's something I want to tell you. Mark, you knew I was married. Yes. Well, I'm not anymore. I got my decree today. Here you are. We haven't had any calls for Straga in a long time. Mark, I can't stay here. Mark, it's no use. All the time we're together, I keep thinking of him. And you're too nice to be pushed around, and it's only fair for me to tell you. I see. Let's say goodbye here, Mark. I'm a pretty good doctor, Kitty. But seeing you, I wish I'd specialized in heart trouble. You were ghost-ridden and haunted, Kitty Foyle. You ran until you couldn't run anymore. And then one day, an item in a Philadelphia paper. Oh, Mr. and Mrs. Devere's Gladwin announced the engagement of their daughter, Ms. Veronica Gladwin, to Mr. Winwood Strafford VI.  You learned. And time kept on doing business at the same old stand, five years of it. Then they sent you down to Philadelphia to open a branch of this store. You were afraid to go, afraid of all the things it might bring back. But nothing happened until the afternoon of your last day, about half past four. I think I'd better have some perfume, too. Oh, yes, madame. Are you going to the opera or dancing? I'm going to the assembly tonight. I see. What flowers are you wearing? Oh, I hadn't thought yet. I'm glad you mentioned that, because I usually have to buy them myself. Oh, in that event, I suggest sank-sit perfume with chameleons. Oh, call, Mrs. Strafford. Oh, thank you, Mrs. Strafford. What couldn't be... Hello? Oh, hello, Win. Oh, you don't have to go to New York again, do you? But Win, in the five years we've been married, we've never gone to the assembly together. Very well. I'm sorry, but I'll have to leave right away. May I send the perfume to the house with the other things? No. I'll make a selection some other time. The assembly. Tonight. This is Win Strafford. Is that all, Mrs. Boyle? The package is ready to go, Mrs. Strafford. Yes, that's... No, wait. Here's something else. Wrap this up. This ring for Mr. Strafford. You sent back the ring. Kitty, remember what I told you if you ever needed me or wanted me or would have me send the ring back to me? That wasn't what I meant. I sent it back because that was all, that was the end. Don't say that, Kitty. But it's true, Win. Look, you'll have to go. I'm sailing at midnight. I'm asking that we leave together and be together always. Pier 48, midnight. Yes. Don't be late. Well, darling, don't be late. And will I live happily ever after? Forever and ever. Forever and ever. Cab, Mrs. Boyle? Oh, yes, please. You're going to be gone long, Mrs. Boyle? Yes, permanently. We don't get many pretty girls here at the hotel. You might as sorry to lose you. Oh, Sam. Yes? I think that a young man will call for me a little after midnight. Yes, ma'am. Be quite excited, I think. Very insistent. Yes? I want you to tell him that I admire him very much and that I always will... Just a minute. I better write this down on my pad. You admire him very much... And tell him that I'll never forget him. You will never forget him and... And tell him that I'll always love him in a very special way. You'll always love him in a... And then tell him that I'm being married tonight. You're getting married tonight? Hey, what is this? Where to, Miss? St. Timothy's Hospital, please. I've had enough of sleigh riding. Goodbye, dear. But here's where I get off. Hey, medical science moves with incredible sweetness. Even before the miracles of penicillin were completely charted, work was going forward in a scientific laboratory on another similar substance called streptomycin. What is streptomycin? It is a powerful drug science has taken from the earth itself by painstaking and complicated extraction processes. What does streptomycin contribute to the conquest of disease? It is effective against certain deadly germs that even penicillin doesn't conquer. With streptomycin, as with penicillin, squib scientists are working in the forefront of progress, cooperating with other scientists all over the world in the effort to discover and prove all that streptomycin can do and to make it available as soon as possible to every doctor. For the squib idea for nearly a century has been described ceaselessly in the cause of health. And through its endless quest for perfection, squib has earned its reputation everywhere as a name you can trust. Sleek, another great picture. The House of Squib will present Academy Awards starring one of Filman's most distinguished actors, Mr. Paul Muney, in his Academy Award-winning performance in the life of Louis Pasteur. Next week, it is Academy Awards starring Mr. Paul Muney in the life of Louis Pasteur. Ginger Rogers appeared in Kitty Poil tonight through the courtesy of RKL Radio Pictures, producers of the Spiral Staircase. This is Hugh Brundage, bidding you goodnight until next week at the same time when the House of Squib invites you to join us for Academy Award. This is CBS, the Columbia Broadcasting System. The Columbia Broadcasting System.


My name is Mitchell Krugman, and I just want to say you talk about raising the minimum wage to $15, right? Yes. So what would you say to the people who say that it can't be done or shouldn't be done? Good. What would you say to them? This is what I would say, and thank you very much, Mitchell, for the question. Implicit in everything that I have said is a little bit of an understanding of history of how things happen, okay? If I was standing here with you 40 years ago, and I said, I think that the United States will become mature enough, racially, that in the year 2008, an African-American will be elected president of the United States. If I said that 40 years ago, people in this room would have said, you are nuts. See, you're young, and you don't know that. You take it for granted. Now, I'm not here with you. You like Obama. You don't like Obama. And by the way, Iowa played a huge role in that. But that happened. That happened because millions of people for decades, it wasn't just Martin Luther King Jr.'s, millions of people said, we are going to end racism and segregation, and obviously we still have a long way to go. When we talk about women's rights and women's struggles, when I was mayor of Burling, this is back in the mid-1980s, we appointed, you're ready for this, pretty radical, a woman police officer, whoa, how wild and crazy can you be, and you're laughing. And it's good that you're laughing because now it's a non-issue. It wasn't a non-issue in the 1980s that people with disabilities are now mainstreamed into public schools. You take for granted, didn't happen by accident. If we were sitting here 10 years ago, not 40 years ago, and somebody said, well, you know, I think gay marriage will be legal in every state in the country, and the overwhelming majority of young people will be supportive. Somebody here would have told me, you're crazy. That's what's happened. Why do I say all of those things? Because that is how change occurs, Mitchell. Change occurs from the bottom on up. Give you, five years ago, if I stood before you and I said, I believe in a $15 an hour minimum wage, you would have said, whoa, pretty crazy stuff, Bernie. Well, you know what happened? Because people in the fast food industry and elsewhere went out on strike, and I met with those people and I've marched with those people. And they said, you know what, we can't make it on seven and a quarter, eight or nine bucks an hour. And the American people said, yeah, we understand that. And what happened is Seattle and Los Angeles and San Francisco and other communities began to raise the minimum wage up to 15 bucks an hour over the next few years. That's how change comes. So I have knocked the slightest out that when millions of people stand up and say loudly and clearly that a minimum wage has got to be a living wage, yes, we will achieve that goal. Hello, Senator Sanders. My name is Noah Tang and I'm a future educator. I taught, a student taught in Chicago Public Schools last summer and I, many of the eighth graders I taught English to were reading and learning at a third grade level. What are you going to do to help the underprivileged and disadvantaged students and schools achieve their dreams of going to college? Well, Noah, first of all, thank you very much for going into education. And when we talk about the political revolution, one of the things we are talking about is changing our national priorities.  We in New England also watch the Patriots occasionally, okay? But you know what, Tom Brady is a great American hero. You are more of an American hero than Tom Brady is. And I want us as a nation. I want us as a nation to understand that education and enhancing our educational system and our intellects, that is a lifelong pursuit. And that starts with having a first-class child care and pre-K system. It starts by making sure we adequately fund public education and we attract the best teachers that we can. And it includes making public colleges and universities tuition-free. I want to see a cultural revolution so that kids in Chicago and in Burlington, Vermont understand that it is cool, really cool to be reading and thinking and talking about ideas. So to answer your question, Noah, maybe instead of giving tax breaks to millionaires, we adequately fund education from child care through higher education. Where's the mic? We got a mic over there somewhere? Yeah. It's an honor to be here, hearing you speak. I drove here six hours. From Springfield, Illinois. And if you remember at the Democratic debate, I was the guy as you were getting in the car, you were in the car and I reach in and I say, here's a yarmulke that says Bernie Sanders in Hebrew and English. The reason I came up here is something that happened just two days ago. And I want to talk about the ethics, I want to talk about the ethics of the press and in particular of associated press. And I want to out them here and now. I was in a Hillary Clinton town hall in Irvendale, Iowa. And I talked about the weed out hate initiative, an idea that gets righteous people of this country to teach and instruct people to weed out, to relay, weeding the garden with rooting out hatred. Yes. And by the time the associated press got done with that article, weed out hate became her idea in response to what do Americans do in response to fear. And the right wing press played ping pong with the left wing press and it was just an unbelievable thing. Welcome to my world. Let me just deal with, because you're raising an important question that has to be raised and something that I'm very concerned about. I had hopes, truly, that in this country which has suffered, and we're certainly not unique in this, it exists all over the world, the stain of racism for so many, many years, that what I see right now, and I'm seeing it with the attacks against Latinos and the undocumented in this country, I am seeing it in the last few days against the Muslim community in this country. What I am seeing is Trump and other demagogues attempting to divide this country to force or try to get the American people to hate somebody because of their religion or because of their country of birth.  That is unacceptable and as a nation, we must reject it. But this is the way that people have often won elections. Now Donald Trump thinks that low wages in this country are just fine. I believe, he says, having a low minimum wage is not a bad thing for this country. Donald Trump believes that our wages are too high. Donald Trump believes that we should give huge tax breaks to billionaires. So while they get us to try to hate Muslims or Latinos, giving huge tax breaks to billionaires and not raising the minimum wage, our job is to be smarter than that. Our job is to stand together and say, no, we're not going to fall for it. We're not going to hate Muslims. We're not going to hate Latinos. We're going to stand together as one people and create a country that works for all of us. OK, Phil, you got a question? OK, yeah. Senator Sanders, what's your personal take on public banks? As you know, North Dakota has the only public bank in the United States. And there's some students, one I know of, who has a 3% interest rate on their student loans and everything. Plus, public banks can also help build up our infrastructure and education on roads and bridges. I think it's a great idea. I think it's a great idea. And I think states like Vermont can look to North Dakota, which has had that bank since the 1920s, I believe, is that right? Started by the populace movement way back when. And it's what they do there is public money, tax money, and so on, that goes into the bank. And then they reinvest that money to make their economy stronger and to protect the interests of their own people. I think it is a very smart idea and I support the idea. OK, a couple of more questions. Where's the mic? OK, good. My name is Joyce Lewis. I love you forever. And I want to know, are you going to help Antrack by chance? Absolutely. When we talk about climate change and when we talk about energy efficiency, it's not only making our buildings more energy efficient. It's also taking a very hard look at the transportation system which uses a huge amounts of energy. Our rail system today, both in terms of passenger rail and cargo, is way behind Europe, Japan, and even China in some instances. So yes, we have got to invest in our rail system, move cargo much more energy efficiently than is currently the case and move passengers as well. I believe very much in Amtrak, you're welcome. All right, maybe last question. All right, OK, I think the question is to make sure that US foreign aid is used to help the people. Is that your question? OK, there was a time, we do spend a lot of money abroad. Too often what we are doing is propping up militaries and we're selling a lot of military equipment. I would like to see us be the country known around the world where when kids get an education or when they get health care, they could thank the people of the United States for improving their lives. So I agree with what you said. All right, let me just again thank all of you for coming out today. Thank you for your help in this campaign and welcome to the political revolution. Thank you.