Aneesh Chopra, former Chief Technology Officer of the United States and co-founder of Hunch Analytics
Aneesh Chopra is co-founder of Hunch Analytics and former Chief Technology Officer of the United States.
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Aneesh Chopra is co-founder of Hunch Analytics and former Chief Technology Officer of the United States.
As an Assistant to the President, he designed the National Wireless Initiative, helped launch Startup America, and executed an “open innovation” strategy across the government built on private sector collaboration – opening up data, convening on standards and staffing “lean government startups.” He is the author of the book, “Innovative State: How New Technologies can Transform Government” focused on how the country can tap entrepreneurial problem solvers to address challenges in health, energy and education markets among other public and regulated sectors.
In 2011, Chopra was named to Modern Healthcare’s list of the 100 Most Influential People in Healthcare (#39) and in 2008, to Government Technology magazine’s Top 25 in their Doers, Dreamers, and Drivers issue. Chopra earned his master’s degree in public policy from Harvard University, and his bachelor’s degree from The Johns Hopkins University. You can find out more about Aneesh at this site.
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Michael:
(00:04) We’re all interested and what happens to the government. We read it on the news, we’re bombarded by all the things the government is doing right wrong. But what that leaves out is that there are pockets of innovation and the government can be a powerful force for positive change. It really is possible, and today, on episode number 90– Vala what episode?
Vala:
(00:32) 94.
Michael:
(00:32) On episode number 94, we’re going to be talking with a guest who will teach us about data and analytics in relation to innovation in the government. I’m Michael Krigsman with my fabulously friendly co-host, Vala Afshar. Vala how are you today?
Vala:
(00:53) Michael, I’m wonderful and I’m super excited to hear from an incredible gust today, so please, do the introductions please.
Michael:
(01:01) And Vala I’m particularly happy that I will ask you for the right episode number because I would have blown it.
Aneesh:
(01:28) That’s my college graduation year, so I’m glad it’s 94 that’s great.
Michael:
(01:14) And we’re here with Aneesh Chopra, who has had a distinguished career. Aneesh welcome and why don’t you share a little bit about your background.
Aneesh:
(01:26) Well first of all thank you so much for having me. I’m real thrilled to be part of today’s discussion. I most recently had the pleasure of serving as the President’s Chief Technology Officer in the first time. It was actually the first time the US government established such a position.
(01:43) Prior to that I had the honor and privilege of serving Virginia’s Secretary of Technology under then Governor Tim Cane, now one of our two US senators and before that I spent nearly a decade in the private sector on the healthcare side, focused on banking, and technology and advisory service at a firm called the Advisory Board Company and prior to that at the Morgan Stanley.
(02:11) So I’ve had a lot of fun thinking about healthcare, the implications between the public and the private sector and then joining the public sector to see who could do a better job of catalyzing breakthroughs to really help us deliver a better health care system, and along the way to find ways to find the lessons, and help education, energy and other markets.
Vala:
(02:32) That’s incredible, Aneesh, can you talk a little bit more about your primary role as the first ever Chief Technology Officer for the United States?
Aneesh:
(02:43) So President Obama, when he was candidate Obama used to in every major speech talk about this technology innovation gap that he’d seen between the public sector and the private sector. And much of what he spoke about was the need to have leadership to close that gap and to make sure that we could do two things.
(03:04) One obviously grow the economy and two, solve some of the big challenges that we face as a country. And when I took the responsibility as CTO, he’d asked me to basically advocate policies that would accomplish both of those things, by closing the technology innovation gap, can we infact grow the economy and solve big problems.
(03:29) And so we turned that homework assignment from the President into a very focused effort on what I later called on the open innovators toolkit. A set of ideas that we could execute from Washington that can be replicated at all levels of government and they briefly focused on four specific things that we spent our time doing.
(03:50) One was opening up more government data. Two, was working in the convening role of government to lower barriers to entry and encourage the adoption industry wide standards, allowing more entrepreneurship and innovation in regulated sectors.
(04:08) Thinking about the role of challenges and prizes to find non-traditional problem solvers getting beyond the belt-way bandits that don’t really respond to government calls of support. And then last but certainly not least; I’m a disciple of Eric Ries’s book, The Lean Startup. We thought about how to apply the principles of lean start-up and tackle from very specific processes in government, when a new product development launches in the government.
(04:32) So those four aspects Vala were the heart and soul that we focused on in my role as CTO.
Michael:
(04:42) Incidentally, we also are a real adherence to the principles of lean start-up and infact we had Steve Blanc and Alex Osterwalder on CXOTalk in the past. So what was your primary goal or your primary function as CTO as a change agent or how would you summaries that.
Aneesh:
(05:10)Well first and foremost at its core it’s advisory to the President. I reported directly to the President and that often meant ensuring three things. One, the he’d get the best information he had to make decisions that would end up in policies. So as we rolled out laws and thought about executive actions, how can technology, data, and innovation contribute to those?
(05:33) So number one it was the policy advice to the President. Number two, it was ensuring as we executed policies that we did as much as we could to promote inter-agency cooperation. Basically a lot of these initiatives required cooperation from department a, b, c, d and e, so we sort of had to create mechanisms to make sure that they were all on the same page.
(05:53)SoI often found myself leading these inter-agency efforts. And then last but certainly not least and part to close that innovation gap it was to do a better job at building a relationship with entrepreneurs and innovators.
(06:07) So it was an external outreach and engagement strategy. My successor Todd Park says it better than I do, which is,‘we can publish all the open data in the world, but if nobody knows how to use it, then it doesn’t really an impact on people’s lives.’
(06:21) So reaching out and cultivating these ‘broader communities’ who undertake these opportunities to innovate and put them into production. So those are three specific things Michael. It was advice to the President, coordinating as muchas possible to execute the vision and then working collaboratively with extra stakeholders to put the ideas in practice.
Vala:
(06:44) And now today you’re leading Hunch Analytics, can you talk to us about how Hunch relate to what you did as the US CTO and about this exciting initiative you’re leading.
Aneesh:
(06:58) Well in many ways you can think of Hunch Analytics as a chance to eat the dog food that I was (eating?). You know, my premise in a nutshell, my premise has been in service to the President. That the next decade of problem solving will largely be the result of better public private cooperation and the four techniques that I outlined whether it would be opening up data, engaging in standards, responding to challenges, and thinking about starting up lean start-ups.
(07:28) These all had a public/private interphase dimension. So part of the opportunity that I see now is to take advantage of these real available data sets, and to do my best with some of the more sophisticated analytical techniques that are on the market today, in combination to build products and services that will make health and education markets more productive.
(07:51) So just as an example, in September we launched a website called veteranstalent.io as a proof of concept and collaboration with Workday, LinkedIn, Monster and many other stakeholders in the public and private and academic elements to map the unemployedveterans skills gap.
(08:12) And to see if we could taking advantage of open datasetsdo a better job of improving matching skills. The skills people have coming out of the military when they’re looking for work against the skills that are in demand by job postings that match.
(08:27) That have different job titles and different industries, but there underlying skills might be more adjacent; can we do a better job of giving people a shot at the American dream. And so that small example is one that we focused on and this is what I’m doing at healthcare.
Michael:
(08:42) And I saw a blog post by the wonderful Leanne Levensaler from Workday describing what you’re doing.
Aneesh:
(08:52) Yeah, so this is an example of public/private cooperation. So here’s a small little minor example, one of the policy initiatives that we champion in the first term, the president launched something called the veterans job bank, and it was a lightweight open Internet standard that any employer in the country could adopt to say, I’d like to make a veteran hiring commitment and to signal that commitment in a job posting, basically by appending some metadata to the job posting on the Internet.
(09:23) And all the internet job boards all for free, voluntary offered employers the chance to tag this job. Well Workday is in place in hundreds if not thousands of organizations around the country, and in that collaboration, Workday launched a simple service that makes it easier for their own customers to participate in this initiative without much friction.
(09:47)So you see this policy idea stakeholder participation, entrepreneur like those who work at Workday, bringing these ideas to life. And so I hope this sort of the recipe for how we will fix a lot of these big challenges.
Vala:
(10:02) As a CMO certainly you know working towards you know developing more of a precision marketing culture and ecosystem, I certainly and my team value data. But I would like your perspective, could you share with our audience because we have lots of business professionals, your view in terms of the importance of data, whether it’s for private public or any other endeavor.
Aneesh:
(10:29)So my broad view is that we are entering an era where the power of recommendation engines, which is really one manifestation, multiple sets of data, feedback groups and other basic approaches to data management can manifest themselves in the way we live our lives better.
(10:47)We’ve seen marketers with very strong success, think about how customer audiences given their own analysis might benefit from service A and B. And we’ve not seen that same thinking or approach in some of these regulated sectors like healthcare or education.
(11:10) Just to give an example, if you’re I think near the end of the enrolment process for the second round of Obamacare, if you were to shop for health insurance today, you’ve got a wonderful array of places that you can pick from. But there are a lot of recommendations engines that say, you know Aneesh, if you shared your medical history file with me, your blue button file, which is an open dataset that every individual is entitled to access, which I can get from every hospital, doctor, or insurance company and will electronic copy my own data.
(11:42) So if a service said, if you share that data with me, I’ll personalized recommendations, only to show you the plans were your doctors are in the network, as an example. That value add would mean that more precise message as a marketer is something I hope would lead to better – I call them conversion rates, but actually choices are going to improve people’s lives.
(12:05)On the education side, and this is the more depressing thing that we noticed in the data analysis that we had done on veterans. If you take a look at the decisions veterans had made in choosing higher education institutions, there is a group that dramatically underperformed on the selections of institutions to attend for skills acquisition, far less than the general population in terms of giving the skills that they have and the opportunity for economic advancement. They have made choices to enroll with institutions that have not been nearly as helpful to get those folks the skills they need to compete for those jobs in the future.
(12:45) So this idea of using data to be more precise in carrying a message, hopefully a message that will improve the welfare and the well-being of the customer in question, is an opportunity throughout the private sector, but especially in the health and education markets, where we can benefit from these kinds of smarter recommendation engines.
Michael:
(13:05) Aneesh, you know the power of data as a kind of glue to bind together the sources of assistance with the people who need that assistance is very clear as you are describing it. However, how does one choose and make decisions about the types of problems that are best solved by the use of large-scale sets of data and the associated analytics?
Aneesh:
(13:40)So this is where the public-private spirit really comes to for, and I would look at this in really two-dimensions; top-down and bottom-up. So let’s start with top-down.
(13:53) On the occasion where society is actively seeking innovated solutions that could combine datasets to help achieve a particular objective, there are specific initiatives and in fact we at challenge.gov which is a platform we stood up in the first term. Every federal agency that is so state – your question was which industries or which topics hold. Every federal agency has the right to issue a challenge or a prize to the public sector, often in combination with datasets that are freely available to encourage folks look for opportunities.
(14:34) So you find in this example Michael, people who wouldn’t normally think to solve a particular problem, hearing about this challenge and then responding, and one small example of that. One of the departments was interested in the issue of infant car seats. It turns out half of the American public installs these infant car seats in a (sell-out way?) that is not properly installed. It turns out that a database at the Department of transportation, all the locations in America, anyone can go to validate that they installed the seat correctly.
(15:06)So in response to this sort of challenge, which is how do we reduce the number of people who have got accurately installed car seats and we’ve got this dataset. Over a weekend, someone who wasn’t in that line of work built an iPhone app effectively that allowed you to figure out based on your GPS location, where the nearest place that you can go to find and get your car seat checked – so a small example.
(15:29) So top-down is agencies seeking out entrepreneurs and innovators to help solve problems, but I’m more bullish on the bottom up, and the premise behind bottom-up is if the default setting in government is open, that is to say every dataset shall be made publicly available. And if anyone chooses to build products and services reusing that information, well God bless them, this is America and they have every freedom and right to do that.
(15:56) Then I would personally invest in a company called to declinement corporation and I’m sure there are other investors in that as well. They had repurposed census data and weather data among other datasets to create whole new models for crop insurance. And those with more specific kind of climate changing insurance policies you might call them, were so effective that Monsanto recently bought the company for nearly $1 billion.
(16:23) So this bottom up ideais the data is yours by default. You’re an American, this is yours and you’re entitled to it and it has created opportunities. McKinsey estimates that this is between three and $5 trillion opportunity for economic value creation and that just needs seven or eight regulated sectors in the economy where new sets of data are being made available.
(16:45) So top-down and bottom-up. We want to encourage entrepreneurs to go directed on some of these issues and we want to see what they can do bottom-up.
Vala:
(16:53) So a few weeks ago Aneesh we had Vivek Ranavadive, who is the owner of the Sacramento Kings, and Vivek talked about fast data, he talk about regardless of the size of data to deliver insight to users whether it’s fans in his scenario or his line of a business owners. Fast data so that we could have rapid decision and actions and as you focus for example on the education space and we have student retention software. But as you talk about recommendation engines and predictive analytics and the importance of getting to that student in that first, second, third semester so that they’re on the right path to finish on time and on budget. Do you see organizations taking advantage of analytics to help improve student success and if so are there some examples that you can talk about.
Aneesh:
(17:47) Well that’s really a growing field. I remain an advisor to a company called the Advisory Board Company, and they have been active in the higher education space, addressing issues of student retention is a growing area for them as they serve a larger base of higher education institutions.
(18:05) And that’s wonderful and I hope the private sector continues to drive that kind of activity. There’s a policy question Vala, embedded in what you’ve just said. And the policy question is, who has the right to access sensitive data. So the key to any of this – you referenced Vivek talking about fast data, feedback loops matter.
(18:28) And the reason for that is you want to know for any given population of students, students who did this, this, and this are higher likely going to drop out. If you had the full loop, if you had a database of 100,000 student actions and you had transaction data that would give you the ability to analyze these patterns, then you can come up with a predictive model, that could be a plot against this particular new piece of data. So, this individual just had this grade and this behavioral issue would appear in their student records, others with that background had gone down this path and we recommend that you better leave.
(19:13) So we needto have access to a larger body of data. We’re struggling with this issue of bulk access to data, for purposes of developing those predictive models. So that’s work in progress in protecting privacy’s.
(19:27) There are institutions and organizations working on these problems and then there’s the individual real time dataset, which is just to say, whether you’ve formed an opinion and developed an algorithm because you’ve worked on a larger base of data, I’m going to give you my particular student record access.So you can take my student record access, apply it against this larger learning raft that you’ve established and spit back any valuable information.
(19:57) ArnieDuncan and I in the end of the first term, launched something called My Data Initiative. And the premise behind that very similarly to the blue button initiative and green button initiative in health and energy markets was that the student is entitled to machine minimal access to that performance data. Not because they are going to look at it, but you and I can log into a student portal.
(20:19) That’s interesting, but because you want to tap that feed into this predictive model that might be available on your own that is that you buy it direct to the consumer or the University might offer it because they have embedded that in the services that they launch.
(20:35) There’s lots of business models, but the key is you want to understand the feedback loop for a large body to understand patterns and then you are going to want specific individuals that is set to run against the pattern to provide those recommendations. So it’s a public-private interface, you have got to have the right policy for them. Am I entitled to that data, can I share it with a third partiy, how does it work for privacy and security concerns? That’s the public sector side. The private sector side is having come up with the algorithms, the predictive model and then providing back that actionable service.
Michael:
(21:09) Aneesh, we have a question from Twitter from (Arthuran Khan?), who is wondering how much of this government data has actually been released. I guess another way of saying it is to what extent is the government, are people in the agencies releasing this data, and to what extent are people in the private sector making use of it?
Aneesh:
(21:31) So this is at the heart of ecosystems, which is to say it is true, in 90 days or so following the Presidents first inaugural. My colleague (VavekKundrha?) stood up the website data knockout. So we had a technical portal that could both publish and market the use on datasets. And on one measure, you can count hundreds and thousands of datasets that have been made available just on data.gov, not counting the datasets that have been made available at state local governments and now increasingly around the world with international open government commitments.
(22:09)Sothey are sort of measure that you can qualify. I don’t know if that’s a useful frame, but at least it’s a quantifiable point. The next layer of this is to measure impact, which is to say are these datasets you know, valuable.
(22:29) And here you’ll see spikes perhaps the most visible spike was in I believe the Spring of 2014, Medicare – the centers Medicare and Medicate, published for the very first time, spending data on the top procedures in America for every doctor and hospital. So you could look up any doctor in America and say, what was the most frequently build service that they made to Medicare.
(22:59) And this was I think 1 million downloads in its first few weeks of availability. So clearly one of the most popular data sets, and the premise was media outlets were consuming it, others were analyzing it and it lead to a lot of articles about it, what was exactly going on in the nations Medicare system for good or bad.
(23:23) So I think it’s better to mention this notion of measuring kind of demand or value for datasets. I think the number – I can tell you that 2% are the most valuable, but my presumption is a subsets of the datasets on data.gov that would be value quite well in the public and private sectors.
(23:40) And the last dimension is, how many folks are building apps? I don’t have a kind of industry wide analysis but Mckinsey in 2012 had published a report, just on the healthcare side, over 200 new products and services were born in the first few years of this open health data movement. So that is just one secretariat with one particular focus area, one moment in time.
(24:09) So I’m presuming that we are well into the thousands of public applications that have been built by the private sector for either for free or to be successful as a business.
Michael:
(24:22) You wrote a book called The Innovative State. Why don’t you tell us about the book, why you wrote it, the key premise. Why take your time to write a book. Why was it so important to you?
Aneesh:
(24:38) Well firstly in the bio of what I’ve done there was a small missing chapter which is the time between me leaving the Whitehouse and my launch of Hunch Analytics. I was a candidate for lieutenant governor of Virginia. I had felt and I still do believe that much of the problem solving that we need so desperately in this country will come from the States.
(25:04) Because the healthcare, education, energy markets, multi-national decisions have been largely been made. It’s up to the States to implement these permissions and they can do so in a way that maximizes entrepreneurship and innovation in these fields or they can do so in a minute that stifles it. So I made the choice to read in order to seek this opportunity.
(25:26) And when I was leaving I spoke with the President – it’s fairly common among aids to say goodbye to the President and to thank him for the time to serve. He had asked if I had prepared essentially a final memo that summarized the big learnings – I was planning to do that anyway, but to get a specific assignment fitted easier around lessons that can be learned and applied more broadly.
(25:49) And I realized in preparation for it, that I had gone to the Kennedy School and I think that might have been our first question might have been from my future colleague at the Kennedy School (unclear 26:04).
(26:06) But as a student at the Kennedy School I hadn’t necessarily been trained on the techniques that we ultimately used in closing that innovation gap so I though let me prepare this note for the President. We ultimately published it called the Open Innovators Toolkit on the Whitehouse.gov/open portal.
(26:24) And I thought that there may be a chance to tell this story more broadly. It’s been the case in the last several decades that the federal government appears to have weakened it’s position relative to the private sector in the use of technology, data, innovation to solve problems and so this gap seems to be widening. But if you look back at American history since our founding, we‘ve had more periods of time where the government has been innovative and actually has had the capacity to solve problems. This isn’t some new concept that we’re trying to introduce.
(26:57) So I thought, let’s tell the story and put in context. America has been an innovative state and it can be again if we approach these new principles and take advantage of these newer technologies to connect in new ways with people and release some clever ways the datasets that are so valuable.
(27:13) So my hope in the book was to really frame up this opportunity to encourage folks to see this – not as an ideological war, left versus right, but a collective vision that we move the country forward and I hope it’s a formula for public sector innovation at all levels of government. So that’s what motivated me.
Vala:
(27:38) What are some other force multipliers that can you know make innovation more pervasive in government.
Aneesh:
(27:47) So there’s three private sector lessons that I learned in my preparing for the book that are critical to realizing this vision. One is you mentioned is the notion in force multipliers. The second is the notion that you should tap into the expertise in frontline workers. And then last but not least, you need a cultural focus on making sure that the organization is open to the idea of collaborating beyond the internal walls of the enterprise.
(28:17) Procter and Gamble’s commitment to opening up its research and development pipeline through a program called Connect and Develop really taught me how a CEO can move the ship if you will in a new direction, and to get everyone on the same page we should value external ideas.
(28:37) From amazon we learned about the importance of empowering frontline workers and offering a chance for them to contribute their ideas and their concerns in a manner that I can inform service delivery. And then of course multiplier in many ways was the learnings I got from Facebook. Sam Berg and I were on a panel of dealing with jobs in the economy early in the first term and she asked her staff to prepare some studies.
(29:07)You know, I think Facebook had 3,000 employees at the time or something like that, and she said how many of you have the job title Facebook Developer? And the answer would come back, it’s a bit shocking. It was 35+ thousand and that was not because they were on Facebook’s payroll. It’s because they had opened up the platform and basically force multiplied their own employee base, so that if Nike or someone else wanted to have a Facebook developer on staff they could hire them and train them to plug in and build wonderful new products and services.
(29:35) So I thought I would formalize it, my goodness we’ve got 3 million civilian federal workers. At that ration, could you imagine if we had 30 million American helping to build government 2.0 and what would that mean to our ability to connect and meet the people where they are and ensure they get the services they deserve. It would be an unbelievable thing, so force multiplier to me Vala is about that principle in context with the others. The learnings of what an open innovation system looks like in the public sector.
Michael:
(30:05) You speak about the concept of handshakes and handoffs, which is a very trite glimpse into some concepts that are very rich. So maybe tell us a little bit about that.
Aneesh:
(30:20) Well if I try to think about the simplest way of expressing what an innovative state is characterized by and these are two principles that answer a lot of the questions I get about an innovative state.
(30:35) The first principle, this notion of handshakes is meant to signify that this is a by partisan collaborative effort. That is to say the left and the right have already shaken hands and authorized that these policy tools being made available. Both parties have said we want more government data to be released in machine beautiful form. We want the public sector to convene the private sector to lower barriers to entry and encourage the adoption of standards. Especially here, there’s health and energy and cyber security.
(31:02) We should tap in this problem solvers beyond the traditional beltway bandits and as far as authority now is available to every federal agency. And the principles of lean start-up are constantly referenced in all public discussions in how do we fix the way government works.
(31:28) So handshakes Michael are all about that core idea that this vision of innovative state is not ideological. It is infact kind of kind of by partisan support and one that requires to say that.
(31:07) The handoffs, the innovative state can’t do it on its own. It can’t just pass a law and say, okay, we’ve got more open healthcare data, great! We’ve fixed the countries healthcare challenge. We need handoffs. So this isn’t a press release on the announcement of a dataset, it is an encouragement in an active courtship of entrepreneurs and innovators and the public, private, non-profit academic communities to bring those resources from the government into products and services that help people’s lives wherever they are.
(32:21) And that’s the messy thing about an innovative state. You know there is infact HealthCare. Gov but there’s also an API. So Stride Health can consume the data, that is the list of health plans from HealthCare.gov and can build a partnership with Uber, so that specific plans that are better for drivers – Uber drivers that have back pain can be recommended as a personalized recommendations engine above and beyond of what they would otherwise do that if they just went to the HealthCare.gov site.
(32:55) The handshakes on opening up the data and the handoffs for Stride Health to build a product or service that can be partnered up with Uber. Value creation to the drivers, value creation to Uber, value creation for economic growth.
Michael:
(33:11) This is complicated because you have to do this it seems to me that you have the technology level, you have the political level and you have the cultural level and that all need to be aligned otherwise this isn’t going to happen.
Aneesh:
(33:33) Yes as a subset of the political level I would argue, business model level. So let me give you an example.
(33:38) In healthcare, the idea that someone should be treated earlier, you know when their condition can be dealt with perhaps in a more cost effective manner is better than waiting for them to show up in the emergency room (Lost transmission 34:00)
Michael:
(34:09) Well I think we’ve lost Aneesh
Aneesh:
(34:11) Our business model. Perhaps the insurance industry that’s on the hook might be willing to have him back (lost transmission 34:16)
(34:25) As a kind of high bar if they want to measure you know a response for their payback period. So the challenge (lost transmission 34:38)
(34:46) to encourage groups of doctors in hospitals and insurance companies to deliver at the right setting at the right time and in that process like if you invest a few dollars in the technology staff, and the data levels and the applications that are associated, I can actually realize (lost transmission 35:07)
Vala:
(35:14) Aneesh, we were cut off I don’t know if you can hear us. Can you hear us okay?
Michael:
(35:21) You know Vala I think that once again we are lost to batteries of technology. What do you think about that?
Vala:
(35:32) I think maybe only temporarily.
Michael:
(35:35) I hope so.
Vala:
(35:37) I’m not sure if Aneesh can hear us.
Michael:
(35:42) but while we’re waiting for him, you know the thing that is striking to me about this is that when he talks about innovation it is highly (35:52 transmission problem), right because he’s talking – there’s the data integration level. There’s the cultural to dimension, there’s the business model. So all of these pieces need to come together, and if you don’t have all of this coming together then as he said, well you may have lots of great data but there’s no one showing up. So what?
Vala:
(36:18) It’s also interesting and surprising to hear lean start-up principles apply in government, where through iteration and experimentation you know we can achieve velocity. And hopefully we can have Aneesh come back because my question would be, how do you implement a lean start-up culture in government.
Michael:
(36:44) That’s what Steve Blanc was talking about. He (phone rings) I bet that’s Aneesh. I’m going to take that, hang on a second.
Vala:
(37:01) The first time in 94 shows and Michael gets a call. I apologize for those of you watching the show and hopefully you can hear me but apparently we have some technical difficulties and hopefully we’ll be back on the air shortly. For those of you who are watching, today I published a 2015 most social CIO’s on Twitter via Huffington Post, some extraordinary CIO’s, many government CIO’s. infact Dr. David Blay was the first mentioned in the list of 100 most social CIO’s on Twitter. These CIO’s on average have I believe close to 57,000 followers and there are about 180 Twitter lists and they’re all active on a daily basis on Twitter.
(38:00) We have the CIO of the Whitehouse, Dr. Elisa Johnson and many other CIO’s, not only in government but also in education, healthcare, sports and entertainment and in other industries. So hopefully you get a chance to look at the new blog via Huffington and Slideshare and provide me feedback.
(38:18) Now I started with a list that was almost 300 strong so I feel bed because there are a couple of hundred more CIO’s that deserve to be mentioned as social and active on Twitter. I will add the name of all the CIO’s who I believe social on the comment section of my post and hopefully you can help add more to the list and we can create a community where we can all learn from these executives that are volunteering their time to share their knowledge and insight as part of our community. Certainly I use Twitter as a personal learning network and I take advantage of learning from these 100 CIO’s.
(39:58) Michael, I see your back on.
Michael:
(38:59) I am back on but Aneesh is not back on and here is where we rant and where we rave about Google Hangouts, because Google Hangouts kicked him out and then it won’t let him connect back in saying something about that’s it’s taking too long to connect to the server or who knows what.
Vala:
(39:19) Well I’m sure if Google knew they kicked out the former Chief Technology Officer for the US, they would feel bad and resolve the issue quickly.
Michael:
(39:29) You know what, if anybody is listening out there knows somebody at Google, whose affiliated with Google Hangouts, would you please connect me to them because I would really like to resolve these kinds of problems. Because you know when you have the first Chief Technology Officer for the US in the Hangout, it’s really really annoying when it kicks them out.
Vala:
(39:56) Especially Michael when we had our best questions left for the last 10 minutes.
Michael:
(40:03) Well we will invite Aneesh Chopra back and I think that Vala, since the guest of honor is not here I think the party is about to draw to a close.
Vala:
(40:17) Well it was an incredible 38 minutes before we had our technical difficulty. You and I are involves with technology every day and the only time technology is defect free is when it’s obsolete. So things happen and we’ll make sure we have a subsequent show to finish our discussions with the brilliant Aneesh Chopra.
Michael:
(40:39) But Vala I have to say that I don’t agree with that point that you just made about you know technology is only reliable when it’s obsolete, because I just want this to work. This shouldn’t be that complicated it just should work, and I don’t want to live on the bleeding edge. That’s not for this, not when we have a bunch of people – you know a lot of people watching.
Vala:
(41:05) It’s funny to hear one of the top technology analysts in the world say, I don’t want to live on the bleeding edge.
Michael:
(41:14) I didn’t say that no – no. I love the bleeding edge. Listen I look at all of these people where it’s one thing to talk about disruption, but it’s something totally else when technology swats your business process. And that’s what’s happening here Vala this process and technology that’s just disrupted it.
Vala:
(41:38) We will perfect this by show 100. We’re only 94 shows into this.
Michael
(41:45) I don’t think it can ever be perfected. No I didn’t say that. well the party’s over and it’s time for you and I to go party how’s that.
Vala:
You got it, well I hope you have a great Friday. Sorry to all our audience who are watching. We’ll make sure that we’ll try – well I don’t know what we can do, but certainly this was a first. Out of 94 shows our only call lost and abruptly ending a show so.
Michael:
(42:12) Well we say thank you to Aneesh Chopra and Absentia for taking the time for talking with us today. We had enough time with him, but we really got a chance to get into his mind and his thinking and his insights about data and especially how government data can enable the private sector to benefit, namely all of us.
Vala
(42:41) We certainly did. Michael have a great weekend and we’ll talk to you next show.
Michael:
(42:47) We’ll see you next week and thank you everybody for watch. We hope you have a great weekend and thanks again. Bye bye.
Companies mentioned on today’s show:
The Advisory Board Company: www.advisory.com
Morgan Stanley: www.morganstanley.com
The lean startup by Eric Ries: www.theleanstartup.com
Hunch Analytics: www.hunchanalytics.com
Veterans Talent: www.veteranstalent.io
Monsanto: www.monsanto.com
Procter and Gamble: www.pg.com
Amazon: www.amazon.com
Facebook: www.facebook.com
HealthCare.gov: www.healthcare.gov
Stride Health: www.stridehealth.com
Uber: www.uber.com