The CDO to CDO podcast is hosted by Chris Knerr, Chief Digital Officer of Syniti.
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Chris Knerr (00:23):
Hi, I'm Chris Knerr, Chief Digital Officer of Syniti, a world leader in enterprise data software, and we're partnering with Chief Data Officer Magazine, MITCDOIQ, and the International Society of Chief Data Officers to bring you this series of interviews with global thought-leaders in data. Today, I have the pleasure of talking with Suzette Kent, formerly CIO at the U.S. Federal Office of Management and Budget, and a CDO Magazine, 2020 global data power woman selected by CDO Magazine for being a key influencer and pioneer shaping the landscape of data and analytics. Welcome, Suzette.
Suzette Kent (00:57):
Thank you, Chris. I'm glad to be here today.
Chris Knerr (01:00):
Glad to have you, and looking forward to our conversation. So, maybe if we could jump right in, your last role was as CIO of the U.S. Office of Management and Budget, that's a different role than I think many folks in our audience have had before. So maybe just as a starting point, you could give us a general overview of what that role entails, the size of the organization, your accountabilities, the mission statement, and then maybe double-click from there a little bit as it relates specifically to data and analytics.
Suzette Kent (01:33):
Certainly, Chris. So, the role is referred to, especially across the executive agencies, as the Federal Chief Information Officer for the United States that sits in the Office of Management and Budget, and the responsibilities are for technology policy for the executive agencies of the United States. So how federal agencies use technology to deliver on their mission and services to citizens. That means policy, many times that may mean certain sets of priorities and activities, and there's also a responsibility for oversight. So ensuring that if there are certain administration priorities that have been defined, there are laws and statutes that are to be met, and there are certain types of information reporting and transparency responsibilities ensuring that agencies are doing those.
As it specifically relates to data, one of the most exciting things that I had the opportunity to lead as the first ever federal data strategy, and that is a 10 year, call it the North Star, of how we want to use federal data both inside and externally. So, whether that's industry research, academia or other types of things, but with that, define a set of practices and action plans to be implemented at federal agencies, we also implemented the first ever chief data officers. This group will be very interested. Some agencies had them, some agencies didn't, and there wasn't a consistent set of responsibilities.
So as part of one of the laws that was passed, we created the chief data officer role with a set of common roles and responsibilities, establish those across all the federal agencies and created a Chief Data Officer's Council. So that group can work together collaboratively to do things that a single agency might not be able to do and promote not only the elements of the federal data strategy, but laws like foundations of evidence-based policymaking and other components of agency specific mission that has to do with sharing data.
Chris Knerr (03:56):
Well that wow. That's impressive and fascinating. I want to drill into a couple of elements of the data strategy, because that's something that I'm particularly passionate about, but just what you're describing, it's in an enormous scale, right?
Suzette Kent (03:56):
Chris Knerr (04:13):
So just in broad strokes, what the size of the budget and the size of the organization spanning all these agencies that we're talking about?
Suzette Kent (04:24):
Yeah. So, if we look at individuals that are identified in the technology and data area across our government enterprise, all of our federal agencies, that's about 87,000 people, and our annual budget is $92 billion, so meaningful and very significant, and like I said, that is across the entire executive branch of the U.S. government.
Chris Knerr (04:51):
Suzette, that's fascinating with respect to the data strategy and also the achievement around creating some inter-agency cooperation. I'm interested in the data strategy in particular because that's something that I do in my professional career. Are there a couple of features of that data strategy you think that the audience would find particularly relevant specifically from a data and analytics industry standpoint?
Suzette Kent (05:19):
Absolutely. I want to start with one thing as I answer your question, all the things that I'm talking about are available on strategy.data.gov and resources.data.gov. Individuals who are currently CDOs probably went through parts of this journey in their respective companies or as they looked at what their long-term plans are, but what we did was to define a long-term strategy that looked many years out decade, and then broke that down into the foundational elements that we needed to focus on year by year, by year in certain practices.
Because what we realized across the federal agencies is that agencies were at different points, both in the tactical skills of the individuals in the data facing roles but also the culture, and the culture of how agencies prioritize use of data, what the management team thinks about data, how they curate their data. What, for your listeners, might be interesting is looking at the full scale of the strategy and the 40 practices that are in the strategy, but then look at what was in the year one action plan.
Those were the foundational steps. It was around organization, it was around skill sets, it was around curation of data, it was around developing an ethics framework, and the most important thing that was number one in the first year action plan was clarity in the mission facing organizations about the questions that they want to answer and the business outcomes that they wanted to drive through using data.
So that ensured that it wasn't just a side project with a set of practitioners in one place, it was embraced by the agency, they were using it to drive real results that mattered either for their mission, for citizens, for government efficiency or those types of elements. That has been some of the more exciting things that have happened, and why people have gotten excited about the activities is, while we're making investment in the hard skills and the understanding of the culture, results were being delivered as well, and agency saw the value that they were getting.
Chris Knerr (07:48):
Well, that's fascinating and so important. I want to just pick up on a couple of things that you shared that are very much along my own lines of thinking. I think too often when we talk to data folks, we sometimes get lost in the data and we forget that the purpose of the data is to drive a valuable outcome. So the fact that you were able to really focus on that thematically as one of the foundational elements of the data strategy, I think is very significant and I just want to highlight that.
Second thing you said that I want to just highlight is building that organizational and data culture, which is a very difficult thing. As a leader in a technology company that works in the data space, I think too often people feel like there's a panacea of some tech solution that's going to solve all data problems that are in fact, over the past 20 years, my own experience has been that, the human part of the equation is actually the most difficult by far. There's lots of awesome technology, technology does cool things, if you don't have an organization that knows how to use the toolkit, then all those investments in technology tend to not be worthwhile. So just to highlight two of those points, I think that's really significant.
The third thing, and let me turn this into a question. So you mentioned some foundational work around data curation. So one of the things that I think, and I'll just say for myself, I know how to ask the question, but I don't have a good understanding of it. Maybe you could give us some flavor of what the federal government's role is holistically in collecting and curating data, and you mentioned that curation because I'm aware that the federal government is a machine when it comes both to the production and consumption of data, and part of what you've done is to start to create a wrapper and a strategy around how that works and how we can get better outcomes from it, to your point. But maybe you could just share a bit around the scope, maybe double-click on one domain that you think would be interesting for people.
Suzette Kent (09:59):
So, Chris, I'm going to throw two of those domains out to think about just as I answer your question. So, things that maybe many of the listeners are familiar with in their day-to-day life. So think about information that you may see from the census, what our population looks like, who is where, and then all the weather data that we have from NOAA, and the fact that in its mission statement, NOAA has a public commitment to making data available. So when you see the weather every day and you're trying to ... do I need to take an umbrella or not? Or am I going to do this thing? Think about those in your mind.
When you look across the entire federal agencies, the scope of what they do is so different. So, as I comment, I'm going to hit some of the common things, but recognize that there are so many different missions, but some of the consistent things is that agencies are responsible for secure collection of information and secure management of that. Much of it is actually defined by law. The government is different than, say, private sector institution where somebody chooses to do business with that institution, or they make a choice to engage, and they have some choices about whether they provide information or not.
The federal government, as they collect data, has to ensure everything's consistent with U.S. law, with privacy done in an ethical way that aligns with American values. That is an agreement with its citizens. So, as that happens for every agency, whether they're collecting information on farmland in USDA, or they're collecting information about energy uses at Department of Energy or ... I can keep on going. The government's role is to securely collect that information for a specific purpose and use it for the specific purpose that is agreed upon with that constituency, the small business owner, the citizen, when we take Social Security and tax information.
So, the interaction is very different, because some of those participants don't have a choice, but the things that are consistent with your private sector, listeners, is that there is an expectation of privacy, there's an expectation of the security of that information and the way that we use it has to be consistent with the manner in which we've agreed to collect it. That may sound pretty basic. Hopefully, lots of folks are saying, "Yeah, that makes sense." But when we think about the applications of AI and automation and things that we can learn from data and infer for better policy, for finding cures for disease, for improving our energy and transportation grids, we have to make sure that those new things that might not have been part of the idea when we collected the data are consistent with those agreements, or we have to go back and readdress those agreements.
So, as the federal government curates the data, they are thinking about some of the basic things that are consistent around quality consistency source, but also looking at bias and ethical use alignment with law and what the agreement with the individual business or entity that shared that data actually is.
Chris Knerr (13:52):
Wow, that completely makes sense to your point, and that's also fascinating because I think then you're in a position in the government of a balancing act between the ... I'll simplify it by calling it fair use, right? So, I gave you data, it's collected for a certain purpose. My understanding is I have to give it to you, but you're going to use it in a narrowly defined way, yet more broadly in the industry, the whole promise of AI and analytics is that, we may be able to discover things that are very important outcome wise from a service's delivery standpoint or from a public policy standpoint, by triangulating different sources of data, and yet you, a little bit, are handcuffed because you've collected that data under the promise that you're not going to use it outside the narrowly defined premise under which you collected it. Is that a fair summary?
Suzette Kent (14:53):
That's a fair story or a fair way to look at it, but I'll give you an example on the other side, and these are real examples. When citizens share with the federal government where they live and where they work, that is for a specific purpose, right? The department of labor may have something, the census may collect something else, but if we're in an emergency, if there's a public health threat, if there's a weather threat, and you're trying to prevent loss of life, and you're trying to inform first responders, most citizens are comfortable with that information potentially being shared.
So, there's also questions, as you said, whether it's using advanced analytical tools or whether it is understanding that the government can operate as a broader enterprise for the benefit of citizens where those intersections are and following an ethical use framework that ensures citizen protection, but also alignment with law in the outcomes that we're trying to drive.
Chris Knerr (16:06):
So, sticking with this line of thought, and this is something that's come up in a number of my interviews, it often feels like the pace of technology development is so rapid that business's ability to understand how to use it, and then from your lens, the federal government's ability to regulate and create thoughtful frameworks that are both forward looking, but not lagging at the same time. How do you strike that balance in formulating policy, in particular as it relates to some of the ethics and privacy considerations that have been very paramount over the last four or five years?
Suzette Kent (16:48):
Yeah, it's a great question. It is a place where agencies are both building their talent and they're exploring the questions, and I'll go back to something you said at the beginning, and I say this to the CIO and the CDOs as well. There are so many things that we can do and technology will allow us to do, but that's not useful or helpful if it doesn't match the mission problem, the business problem that we're trying to solve, if it's not embraced by the business. So, we look at some of those things in the same way, are we achieving the outcome and just stay very, very laser focused on the outcomes and what ...
The ways that we have found success is pilots and initiatives where we are learning and then raising the bar and learning and raising the bar. When you asked the question about policy, many of the way that I have framed that, and when I've been discussing it both inside agencies and with Congress, when I was in the role, was developing guard rails, and then we narrow those or we expand those as we learn and understand more. We've been there because of what we formerly talked about laws, about commitments to privacy. We already have a lot of guard rails in place inside government. But as we build extended policy, we're layering on top of those more, and as we learn and understand, we continuously update the policy. That is part of the reason that we developed the strategy with an action plan to come out every single year.
Because every single year we will use the learnings and the feedback and use that to enhance both the content of the next year's action plan, but more importantly, the priorities of the items that are on that action plan. I would be remissed if I didn't say for my friends inside the federal government now, they've made significant commitments to building the talent. So, many of the things that they're doing, there's a recognition that what both business users, as well as practitioners, we need to continue to grow those skills in demand across the agencies. So, they're building, and then moving the ball down the field with the experiences and the feedback from those experiences.
Chris Knerr (19:37):
Yeah. That makes sense, and that's actually refreshing to hear, because, not to be rude, but one doesn't normally think of the government as being agile, but exactly what you just articulated, I mean, this is what we've done in industry as you know for years is sort of say, "Get away from big monolithic waterfall type programs and into a more agile space." So, even within the context of U.S. federal getting down to a one-year cycle, that's impressive. It's refreshing to hear that same concept of doing pilots and learning and then percolating those learnings back into the policy frameworks is current as well.
Suzette Kent (20:21):
I want to add some [crosstalk 00:20:25] really important. It is really important. I joined the federal government from private sector, from almost 30 years in private sector, and it's very ... Think about the concept of something that you're setting some 10-year aspirational goals, whether that is in quantum computing or whether it's in our use of data, there are so many things that we don't know along that pathway, and we don't have a crystal ball. The way that we make progress and meaningful progress is through those steps and moving more quickly, and it was very different.
I think if you were talking to some of the agency CDOs, and I would encourage you to at a later point in time for this same type of thing, they would share with you their stories of what they learned from pilots and focused initiatives and how that informed their broader strategy. That's the way, again, we continue to deliver meaningful results and make real progress.
Chris Knerr (21:30):
Yeah. Now, that's very, very interesting addition and a perfect segue to slightly different direction that I wanted to go on. So you mentioned quantum computing, and so we just talked about real-world delivery and the importance of agile on that. So, I guess my view is that, from a service's delivery standpoint and taking that learning and surfacing it back into policy that's ideal, there's also a very important role that the public sector plays that can't really be done in the private sector, which is around basic research. If you look back at part of what's made our economy extremely successful over the past half century, it's investments that have been made in fundamental research that ended up being commercialized.
So, I'm sure you can't be too specific about what some of those things may be, but I think it would be helpful to understand, is that vision still there as it relates to data and analytics to do super secret basic foundational research? It's not agile, it's expensive, it takes a long time, but that it pays dividends for the country and for our economy over a generational periods.
Suzette Kent (22:58):
Yeah. Chris, that commitment is there, and I would say, as there's this much energy going into it as ever, and funny, I chose the word energy, so when you look at, for example, the Department of Energy and our footprint of national labs, many of the things that are going on in this space in the national labs are incredible. Some of the most exciting things that I had the privilege of seeing and being a part of are happening in those spaces and driven with data. I'll share a couple of things that are public. One of the national labs put out some information on analysis that they were able to do with massive data sets, took a week to run, but new insights into some of the things that have to do with how coronavirus behaves in the body.
I mentioned NOAA already, but their big data project is, one of the most exciting that I had the opportunity to work directly with that team, and what they realized is that if they make more data available and there were certain sets of actions that they were taking, it becomes more usable by industry, by academia, by other places across our national infrastructure. So, those are important learnings, and specifically around quantum concept, but just say advance high-performance computing, what we're also recognizing is that data and applications to work in those environments, actually need to be handled differently from a technology perspective, and that's another important place where both are federally funded research centers or national labs and public private partnerships with certain academic institutions.
In some cases, companies, Microsoft and DOE also announced a partnership around use of data and AI. Those things are critically important to continue to advance at scale. So, I would say it's alive and well, and some of them are more public than you might've thought.
Chris Knerr (25:31):
Yeah. Well, that's really good to hear. I guess in a way I would expect that excitement around working with large data sets and some of the computational stuff as it relates to analytics. You have smart people with a lot of tech prowess and compute power. They're going to do that stuff, which is awesome. I want to maybe just double click on one thing, and I'll use a particular example that I'm very familiar with because I have a life sciences background. To me, one of the most foundational challenges in the data world is the problem of data interoperability.
If I double click on, for example, electronic health records, it feels to me as a life sciences person that that's just a complete disaster for lack of a better way of putting it, and that it's been seated to private companies, which are ... they're excellent companies, they do good work, but they're also profit seeking companies that are not really in my view, pushing forward interoperability standards for something that ... It has a critical services delivery outcome to U.S. citizens, and it also has a critical impact on our economy.
So, like just for that or for something else, should we be doing more from a basic research standpoint to drive data standards and critical domains and drive data interoperability sort of create more Rosetta Stones, if you will, that we would all like to exist. Some of that might actually negatively impact our business a lot, right? Because a lot of what we do is clean up industry problems, where there are issues of data interoperability. So, I'm curious in ... it's a boring area if you will, but it's super important. I think for the data studio audience, I'm really curious if you have a perspective on that in particular.
Suzette Kent (27:40):
Yeah. So, Chris, that is a complex area. I'm going to say a couple of things to frame my comments. I wouldn't necessarily say the government seeded things to private sector, I would say private sector move faster.
Chris Knerr (27:59):
Yeah, yeah. Yeah.
Suzette Kent (27:59):
Because there's always been electronic health record initiatives. Those, at some sometimes, have not moved at a pace that meets the needs, meets the demand, and that's my opinion as a citizen and speaking as a citizen.
Chris Knerr (28:18):
Suzette Kent (28:19):
Then again, I'm pretty impatient with everything and I expect all my digital experiences to be fantastic, as I mentioned, I came out of financial services and the bar is, let me say on the video here, way up here, right, off the screen. But in the environment that Americans live in, when you can tap on your phone and your food drops at your door or you can have your windshield changed in your driveway from a crack, people expect instantaneous data-driven high quality services. So, I agree with you that there are areas and very specific things where consistency of data improves outcomes and especially the things that are, where there's a distributed delivery model and a distributed interpretation.
I'm going to go back and use the weather example just because it is easier. It's very finite observations, but it means something different, whether you're in New York City or Washington, D.C. or California, but those observations may come from a central point, the consistency has been established for many years and how that is shared. I think we've seen in the health system because of many of the things going on, that need and that drive has become more prevalent again. I would point to some other things around, say, for example, food quality, the United States has some of the highest food quality in the world and our standards and some of our ... the way we share information has been very consistent there at a basic level. But if you get down into what people call organic or non organic, and some of those other spaces, there's less definition, and those are paths that will go on.
So, that's a long-winded answer to say, yes, there are areas where we should have and should focus on more consistency, again based on the outcome that we're trying to drive. But as you note, very importantly, there's a philosophical kind of question in there around what is the business opportunity for private sector versus where should the government be an operating entity. I think those are some questions that we continue to see pretty heated debates about.
Chris Knerr (31:06):
Yeah. That's an excellent answer. Thank you. It's indeed a very complicated space and hard for the government to move as fast as private sector's going to move, yet at the same time, it seems like there's something there in this domain in particular that I'm very familiar with and I'm sure in other domains as well, where maybe pushing a little harder and a little faster just on some interoperability standards. Which again, it's a mechanical thing, but a very important thing. If you go back to the dawn of the internet, some of those standards have proven to be particularly significant and punch above their weight in terms of positive outcomes from a technology standpoint. So, certainly an interesting thing to think about.
Let me shift gears if I may, one more time. So, we've talked a lot about scope breadth complexity of all of the government's activities as it relates to data collection and data curation. So, if I put my private sector hat on now, now put yourself in the seat of a CDO for a commercial company, what should I know as the CDO of a commercial company? What assets am I probably not using that I should that are available for free or for a nominal cost in terms of this vast wealth of data that's collected and been curated by the government? How can I improve my own business outcomes by better using that data, what am I leaving on the table because I'm not paying attention?
Suzette Kent (32:47):
Yeah, that's a fantastic question. I love when I look at some of the data sets and I look at the industry and economic value that they drive the number of jobs, the number of companies that either reuse or repurpose that data, aggregate it with other things, because as an entrepreneurial side gets really excited. So I'll start with a couple of real basic things, and I'll use the federal data strategy as the starting point, be very clear first and foremost, on what are the things I'm trying to solve for, and then reflect that against the fact that there are more than 300,000 publicly available government data sets right now on data.gov. You could go look at them while we're talking. Depending on the industry that you're in, those could be very helpful or very insightful.
They might help identify gaps for new products or ways to augment existing products for those to be better or more specialized to the market. Everything that I talked about with the federal data strategy, those resources are available online as well. One of those first year action plan items was a one-stop application for research and for gaining information. The other thing that I think might be a suggestion is look at some of the publicly available information that the CDOs at the agencies are sharing, and they're sharing about what they're doing and how they're using data, because I think that's going to create business opportunities for many companies that do work in that sector to be able to support those, to augment those.
In many cases, the power is uniting data from multiple sources to where some of the outcome is better than the some of the parts, and so as that maturity happens, I think there's going to be very interesting commercial opportunities, and I personally think about it, I'll throw one other out there, there's a whole spin on the new normal in the workforce, just like you and I, having this video conference of what does this new environment mean for the type of information that needs to be available because much information that individuals use are used in secure places, it's not widely disseminated. It is not necessarily structured in a way that is easily usable.
Some of the things that are basic operations, but we now have to think about it from a lens of a different type of working environment. I think the secure data sharing and more intense activities that are happening in a distributed work environment, we're going to create some interesting both challenges and opportunities.
Chris Knerr (36:22):
That's an excellent thought. Thank you. I actually, a few excellent thoughts in there, and I was actually more thinking of consuming data, but to your point with this whole vision, there are likely commercial opportunities to help out our government, which is always exciting work. Then I agree with your thought too. I think that the next normal and future of work and data sourcing, data storage security, that's all going to be very significant as well. Well, maybe in closing, any final thoughts that you want to share, major predictions for the next five to 10 years, projects you didn't get to talk about, advice for mid-career leaders, any final thoughts that you'd like to leave our audience of CDOs with?
Suzette Kent (37:16):
Yeah. That's a big one. I'll say, first and foremost, thank you for having me, for the conversation today, but tell your listeners they're part of what I think is a very important transformation for how we operate, not only our government, not me, not only our private sector businesses, but our government as well. The demand for show me the data, help me understand what's behind and operating in an environment that is consistently based on that solid foundation is part of what everyone who's listening now helps create. So they have a role in that culture, whether inside government or not.
It's a business imperative for all of us to be data savvy and data literate. I'm also spending some personal time with our different educational pathways and looking at what students are being exposed to in higher education situations and continuing learning. Sometimes as early as the high school timeline, when you look 10 years, I don't have a crystal ball, but when I look at folks who are forecasting, what are the most important roles in the next 10 years, almost every one of them, the majority of them has a component of data appreciation, hard level skills or data needs as a critical element of that.
That's one of the things that gets me really excited. I think as we strive for better outcomes, going back to that demand for data and being leaders in this space, not just in the technical capabilities of how, but the ethics, the applications, the exciting entrepreneurial ways of how new things might be done, discovered, whether it's on a path of personalization or ways that we can create business leaders, customers that not only are interested but can serve themselves and form themselves. Those become exciting things.
If any of your leaders is interested in some of the things I talked about on the federal data strategy, there's also a link on the website for a newsletter. So, when updates and components come out, you can take a look at that. Chris, you asked me a wonderful question about what's going on in our national labs. I would say there continue to watch those things. As always, like our conversation today, dialogues across practitioners, is really important for us to be able to lift not only the conversation, but the comprehensive abilities across everybody that's in our working environment. So, I appreciate CDO Magazine for the time. I'm glad you're a practitioner in this space and we'll keep working on that electronic health records.
Chris Knerr (40:54):
Suzette, that was an awesome answer to a big question. Thank you. Thank you very much. Can folks find you online, somewhere on LinkedIn, Twitter?
Suzette Kent (41:05):
I'm on both LinkedIn and Suzette K Kent on Twitter.
Chris Knerr (41:12):
Awesome. Okay. Well, thank you so much. I think for the audience, we got some really terrific wide region conversation. There are obviously a lot of tremendous resources and great work that's being done that I think I'll count myself among folks who were under utilizing those resources. I'm excited after the conversation to go check them out more myself. I love, Suzette, the focus on organizational and cultural and skillset development. I love the focus on business outcomes, I love you having helped bring some critical concepts like working agile and percolating that into public policy into the federal data strategy.
So, thank you very much again for joining me, I really enjoyed our conversation. For our listeners and viewers, there are a number of additional interviews on CDOmagazine.tech. Thanks for watching, hope everyone has a terrific day.
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