Hosted by the Data Transfer trance transparency coalition. Good morning, i am managing principal with unlimited and i am here representing act iraq. This provides an objective and trusted former government and industry executives collaborate to address key issues facing our government. It has been my pleasure to be a leader in this project. We have volunteers from dozens of companies and Government Agencies collaborating to discuss data challenges in a practitioner focused forum. White papers, panels, panels, conferences have been produced and made available to public. Check out the website for all of the output. With great pleasure, i introduced todays keening q keynote speaker. Mark dahms has had three Main Responsibilities in that role. He led the Statistics Administration which includes two of the nations preeminent data bureaus. These agencies collect information on the United States dynamic population and economy, publishing vital vital data to the citizens, businesses and leaders. The Census Bureau and the bureau of Economic Analysis combined have over 10000 employees and a budget of over 1 billion. The second responsibility was being a top economic advisor. He contributed to a wide variety of subjects including manufacturing, taxation, immigration, and education. His contribution was often about what data can be used to better understand the issue at hand. His responsibility was leading the Strategic Plan for data transformation. He detailed the Strategic Plan for the department making sure data was optimized to benefit business makers, policymakers and people. Prior to becoming undersecretary, he served on the department of commerce. He frequently met with Business Leaders across the country, listening to their concerns and insight and providing overviews of the u. S. Economy. Prior to joining commerce, he helped guide Monetary Policy and the Federal Reserve policy. He has done research in wages, manufacturing and other topics. He has a phd in economics from the university of wisconsin madison. Basically, throughout his career, he has either use data to answer question or made Data Available so others can do likewise. He is happily known as a fellow data geek. If that contradiction wasnt enough to convince you, he has three computers in his road bag. Join me in welcoming doctor dahms. Thank you very much. Thank you for inviting me. Were going to sit here for just a second and see if this works. If we can go to the first slide please. What i would like to do is talk for about 20 or 25 minutes and then open it up to conversation. The main thing i want to talk about today is data and what is happening in our country and what role the data transparency plays. This is a map of street closures in downtown d. C. Because of the popes visit today. The reason im showing this is ten years ago, producing producing this map would have been really hard. This map exemplifies the point that our previous speaker spoke about. There is a lot of data in standard format that making maps like this is a lot easier than it used to be. You see a lot more maps. There is a huge demand for geospatial information. People always want to know whats happening where and how that relates to other points geographically speaking. So if we think about this industry, we have seen a huge explosion of the tools in these maps and a huge explosion in the people we have the skills to make these maps. Its basically basically a trifecta. You have people with the right skills, and the right Software Tools and the data. All of that combined can produce Better Outcomes. In this case, you can now, in todays world, produce more and more maps to get the information to the people who really need it. When you look of these maps, keep in mind ten years ago making these things were really hard and today its a lot easier. Again, the concept behind making these maps is very similar to the concepts youve heard other speakers talk about. Lets go to the next slide. The summary, i have a picture of my cat if you really want to see it, oh great, i think we really are at this Tipping Point. As a society we are beginning to benefit from this. The thing that has happened over the last couple decades, we have seen a huge explosion in Computer Technology and Software Technology and munications technology. Now with those key parts in place, we can really take advantage of the huge explosion of data thats occurring. But how quickly will this happen . How quickly will we see the benefits question market depends on a couple things. First, it depends on how quickly we make really important data assessable. When i say assessable, you heard hudson speak in a different context about the data being in a standard format or inter operability. Its basically making data assessable and usable. Then also, what ive seen repeatedly in application after application, across a wide variety of data fields is not only do you need the data, you actually need people who know how to analyze data. That is something we are in relatively short supply. Ill talk talk about that a little bit more in a future. One reason people are getting so excited about data and you hear about all the time is you always see these graphs. These graphs always have a certain flavor to them. These graphs always have on the vertical access from measure of data volume. Sometimes its a word youve never heard of like petabyte or terabyte. Theres always some word that is some huge amount of data. Its always increasing. The horizontal access, we have time and these graphs always have the past and what is projected into the future. Then you see a line that shows how much data there is going to be. They always show that they are sharply curving up. The amount amount of data thats assessable and usable by people is accelerating. Usually the argument you here for this acceleration in the amount of data thats acceptable is first, all the Government Data efforts which are very heard a lot about today and to if you think about the private sector, the private sector is gathering and processing more information than ever. What gets people really excited about the future is how much data will be able to be gathered those lines will just be really, really, really huge. So theres a huge amount of information. Whether its information from the government or private sector or coming from somewhere else like the scientific community. What id like to talk to you about is why do we care . Why is this so important . Basically, when we look at data, we want something from it. The previous speaker spoke about how we want more light into how government works. We want better information on how government is spending its money, for instance. Im an economist, thats my background. Were not just asking can we have more insight and how it works but how will this affect our country . When we talk about this data and outcome whether or not its better for our gdp growth or are citizens, i would like to present a simple model. Something with three little steps. On the start with the last one first. Its the data outcome model. How do we go to data to get these outcomes that we want and were just going to simplify it. The acronym of that is bones which is pretty cool. Id like to start at the ends and go up to the beginning. At the end, we want Better Outcomes. It usually falls into three buckets. The first first one is what i call smarter government. We heard the previous speaker speak about government being better and more efficient and better able to meet its mission with less resources. That is a huge goal given how Big Government is. We have the federal government which is trillions of dollars and the state and local governments. Working in commerce, we do a census of state and local government i want everyone to think about a number really quick. Have have any state and local governments are there question theres 50 states, about 3000 counties and a lot more after that. The number is 91000 local governments out there. There is one federal government and 91 state and local governments, 91000. So were talking about federal and state and local level. From an economists perspective, we want our businesses to benefit from this data. They can benefit in two ways. They can use the data themselves to be more efficient, they can be more competitive. To as representative by a lot of people in this room, there are businesses and companies that are represented here. This is an industry that is very important and its growing. The u. S. Has a comparative advantage and we run a surplus. These are jobs that pay really well. This is an industry that we really want to support. Finally, as also we heard earlier, the benefits of data, always try to think of it in these three buckets. Either we want better government, more competitive businesses or we want more informed citizens so we have a better idea of whats happening to the world and whats happening to our governments. Thats what we are really striving for. Thats the outcome that all of us are looking for. How do do we get there . Well we get there with the analysis of data. Theres all this data out there but how do we analyze it . We have Software Tools. If you look at the map, that we presented at the beginning, there is a company that has a lions share of the market and did a tremendous amount of the work to make Geospatial Data useful. Its now now you really easy to make these maps. Now that im unemployed, im trying to learn the first 30 minutes was a lot of fun and then after that it got a little frustrating. Maybe they can hire somebody to do that. Second you think about computer hardware. If you think about Storage Capacity and cloud capacity, these are things that are now at commodity. Ten years ago the stuff was a lot more expensive and the prices have really fallen. Finally the point i was making before, Human Capital. Human capital is an economic phrase. It means the skills of our work force. Not just this guilt of our scientists and programmers but people who really understand whats going on. If we got got all the Financial Data across all the Government Agencies in a standardized format, you still need people who know how government actually works to really make sense of that data. If you can can get really large data sets, all put on my statistician hat for a minute, the bigger the data set the more correlations youre going to find just by chance. As we get more more data, we will have more more correlations. How do you filter those out to really figure out whats going on . That requires subject matter expertise. When i talk to people in healthcare, and i talk to people in the private sector, when i talk to people in criminal justice, you can have the best Data Scientist but that has to be coupled with knowledge of whats happening in the industry. An example of this is when you just need that common sense. Im going to tell it data jacks. A data joke. They are are three statisticians. They are out hunting. Its a Beautiful Day and their hunting. They see this buck 100 yards away. The first attestation gets out his rifle and went for the shot. He squeezes the trigger and the bullet goes 5 feet to the left of the deer. The other statistician goes. He looks at his rifle, takes a shot in the bullet goes 5 feet to the right of the deer. The third statistician picks up his rifle and says it looks like we had it. [laughter] so that gives you an example of how you have to look at data and understand it but you also have to understand the subject to know whats going on. Okay, so, the ultimate goal here is to get Better Outcomes. While there we are talking about better government or making our business better or our citizens more informed. We have to analyze data. What we also need in the data transparency collection has been great at this. We need the data itself. The building blocks. We hear about data and i use the word integrity often. You have to know where the data comes from. There is a lot of junkie data out there. Agencies take pride in themselves producing high quality data about our people and our population. There are real questions out there about data and what we actually know about it. Often when youre looking at complicated questions you have to get those types of answers. Them are going to talk about common formats and standards because you want to reduce the cost of combining all these data sets. Its one thing, you have to make your eta sets easy to find. Last time i looked at it will there were about 114,000 different sets. Theyre just exposing in terms of size and number. How can we make this something that weve been working on and can find. The ability to merge data comes back to the standards. Combining data is where you get the real value. You can have a single data set and ill go through examples but its when you combine data from here to here and put those things together. Thats where you get the big picture. Think about the map. There were roads that were closed and it was a map of dc. Its combining those things to present a good visual representation about how it is going to affect the commute and how it will affect their day. So thats a simple model. I think about all this data stuff because theres so many words out there and people talk about data. They put things in different buckets. Lets go into these a little bit more. In the first bucket, in terms of data accessibility, lets talk about what we done at the department of commerce and why you may actually care about that what you probably dont know, let me get through a couple examples. First, we have noah. Those are the folks who monitor our climate and our ocean and our fisheries. They monitor solar activity. Just there weather data alone is about 30 tb a day. They have this problem of how do you get 30 terabytes of data out the door . They are working with a private sector in reviewing creative ways of doing that. That is is a huge physical challenge they face. How do you make this data assessable . Its a huge problem that weve talked more about later. Then we have ba. They are the folks that produce the gdp. If you think about our current account assets. What are interactions with the rest of the world and how does it affect our status with the rest of the world. They go back in time and produce a lot of detail over time. Its really just a tremendous amount of information. Its really hard to get to. How can we make that information easy to get . You may have a question about Consumer Spending in a specific category. How can can you, as a data customer, quickly find the information without going through hundreds of pages of documentation. Thats a big challenge. Let me talk about the Census Bureau. That is the major source of our information about our people. This is where good data comes from again. They care about everybody in our country. When you see data from a lot of these privatesector sources, you always have to question, how representative is that data of the country . If someone is in public polling, if you have a pollster who doesnt have access or cant use cell phones which is a big issue, those numbers can be quite skewed. Think about the last election. There were a lot of polls that were actually off. When youre actually off. When youre looking at data about people and youre looking at it from these privatesector sources, theres a huge question about the quality of this data. What can we do to make the data easier to find. If you want to know whats happening to your community, what does your Community Look like . Lets say youre moving to the d. C. Area. What does the Smallest Church look like . How does that compare. What are the people like question my two they have characteristics you are looking for question you should be able to find out but it can be really hard. Then we have the patent Trademark Office. When youre an inventor, you have to look at the patent database. Right now now a lot of that data is very unstructured. Its not machinereadable and not all the data that the Patent Office has is out to the public. Theres an analysis at the Trademark Office where they are sitting on a bunch of data that hasnt even been opened yet. They all have very common themes. If you want to get the data out, there is people who find it and can use it. Lets talk about the analysis of data. Again, i mentioned one mentioned one of the biggest constraints we have in the country and for those of you in the private sector you might have a hard time finding these people who have the skills that looking at this data. So we looked at that and theres a lot of people in our society, over 10 million who are kind of data in their daytoday jobs. 10 million are very data intensive. We expect that number to grow over time. We need to develop more people with these skills who can actually look at data and make the right inference from it. Look at these big data sets it goes well beyond excel. I know the department of commerce, finding people who had the ability to take large data sets and do something with it, that was really hard to do. When i talked to my friends, salaries are really high for this. We have to do a better job of educating people to get them into the pipeline to be able to do this type of stuff. As i said, smarter government. We could talk about smarter government all day. If im speaking from an economic point of view about where we are really going to move the needle, where data can help, it really helps where there is a lot of uncertainty and we dont know stuff. We have all been, had experiences for the healthcare sector are self. Sharing information across the Healthcare Industry is very difficult. Precision medicine is impeded by the ability to share information about our dna, for in