Tech Unknown | Episode 2 | Season 1
Featuring guest Kirk Borne and host Tamara McCleary
“When you start looking at connecting points, those intersections across the different siloed data collections and siloed departments, you might just be amazed at what you could discover.”
– Kirk Borne, Principal Data Scientist & Executive Advisor, Booz Allen Hamilton
Most businesses have siloed departments that are pursuing digital transformation independently, creating “islands of innovation” throughout the enterprise. This episode discusses how to unify these efforts into a cohesive strategy to develop an intelligent enterprise.
What could your business do if you had total control over your data? In NASA’s case, a data transformation is what made the Hubble telescope and countless other amazing discoveries possible. Our guest this episode, Kirk Borne, was there when it happened. He began his career as an astrophysicist, combining datasets from multiple fields in unprecedented ways.
Now Kirk’s work is more down to earth. In this episode, he shares how businesses can “democratize data” across the enterprise. And he explains how data transparency plus intelligent analytics can lead to new efficiencies, better customer experiences, even entire new business models.
“The key is this concept of the culture of experimentation, that you allow people to experiment with data.”
– Kirk Borne, Principal Data Scientist & Executive Advisor, Booz Allen Hamilton
Listen to learn
- How to break down data silos to drive innovation
- The advantages of “mission engineering” versus “system engineering”
- How data democratization drives improved customer experience
- How enterprises can prepare for the future of data digital transformation
About our guest
Kirk Borne is a Principal Data Scientist and Executive Advisor at Booz Allen Hamilton. He’s a global speaker, consultant, data literacy advocate, and an astrophysicist.
“The success of the organization is the mission, not individual systems.”
– Kirk Borne, Principal Data Scientist & Executive Advisor, Booz Allen Hamilton
Did you miss our first episode?
Explore the future of analytics with Timo Elliott: Click here to listen.
Episode 2: Transcript
Tamara: You know, most businesses have siloed departments, right? How many of you can completely relate to me on that piece? And these siloed departments are really looking at digital transformation independently which we all know doesn’t work and it creates islands, right, islands of innovation in these disparate parts throughout the entire enterprise. This episode, we’re gonna discuss how to unify and bring together these efforts into a cohesive strategy to develop an intelligent enterprise. And I’m so excited to introduce for this episode, Kirk Borne. And Kirk is actually a principal data scientist at Booz Allen. He’s a global speaker, consultant, and astrophysicist, space scientist, Big Data and data science advisor, TEDx speaker, researcher, blogger, data literacy advocate, worldwide top influencer since 2013. Whoo, Kirk. Kirk Borne, thank you so much for joining us.
Kirk: Tamara, thank you so much. Wow, is that really me you’re talking about?
Tamara: Well, you know, you’re actually the first rocket scientist we’ve had on the show and you have a degree in astrophysics. I have to say, you know, what did you learn from your studies that carries over into Big Data?
Kirk: Well, I’d say, first of all, the science of data, data science, is truly a scientific process, and so getting a degree in science certainly trains one to think that way. And so one of the ways that I like to think about that is sort of the opposite of what sort of traditional analysis, data analysis might be. So data analysis often is you’re given a set of questions and you analyze the data to find the answers. But real science, real exploratory science and therefore data science is about finding the right question in the data. So it’s not answering the given questions that you have been handed, but to find the questions that you should be asking and that could be in many different forms. For example, if you see an interesting pattern, an interesting trend, you see maybe some emerging clusters in the data, some new segments, some outliers, some anomalies, all these things should raise your curiosity as a natural human, you say, “Wow, why is that? What is this doing? Why did this happen?”
So that inquisitiveness, those question generation aspects of the data sciences is what really turns me on because that’s literally what I did as an astrophysicist that we would collect data from the stars and the galaxies and the universe and, you know, we try to answer some questions that we had but those questions were really based upon a hypothesis we had of how things worked in our universe. And when we collect the data, sometimes it confirms your hypothesis and sometimes it doesn’t. That’s the same thing we do in data science across many different disciplines, not just astrophysics. So anyway, I just find the two are pretty strongly parallel and they’re not so far apart as you might think.
Tamara: So what was it for you about data science that really attracted you and grabbed your attention and said, “That’s what I want to be”?
Kirk: Well, going way back to my youth, I received a gift when I was nine years old, a Christmas gift from a relative which was an astronomy book and it was very colorful, beautiful drawings, and it wasn’t very technical obviously for a nine-year-old. But I was so inspired by the things I saw there. I said, “I want to do this for a living. I want to study this stuff. I want to learn what this is all about.” And so I already set my mind to being able to, you know, reach that position where I went to graduate school, got a Ph.D. in Astrophysics at Caltech, the number one astronomy and graduate school in the world, and all those pictures I saw in those early books when I was in school were from Mount Palomar Observatory which is run by Caltech and I was able to be there. So that was a great inspiration and got it done.
So, did that for many years actually…and you speak specifically about data science, there was a transformative moment in my life about 20 years ago. I’d be happy to share if you want a little bit of a story there.
Tamara: Yes, please.
Kirk: So for this to make sense, I have to sort of reveal sort of my age because literally I, you know, 20 years in astrophysics, I was doing fine things in that profession, collecting data, analyzing data, doing my astrophysics research but my day job which paid the bills, I was working at NASA, the space agency, and the day job was operating and building and running data systems for scientists to access all of the data that comes from NASA space missions. And so my day job was data and my night job was data, I was surrounded and living with data all the time. And about 20 years ago, we got this new dataset that we were collecting the datasets from the different scientists who were doing experiments on these NASA space missions and they send their data to us to preserve it in basically a digital library and one of these datasets came to us which by itself was more than twice the size of the other 15,000 datasets that we had combined.
And so basically in one day, we went from less than one terabyte of data capacity at the data center to requiring two more terabytes of capacity to handle this new thing, so just adding one more to the 15,000. And well, we didn’t ultimately collect that dataset because it was just too much strain on the system in those days. But I said to myself, “Well, what can one do with this much data?” I mean, I never even imagined this much data before. And so a friend of mine told me about this thing called “data mining,” you know, I said, “Gee, that’s an interesting combination of two words, data, and mining.” And that, I never…this was, literally, 22 years ago. And so I started looking into that and discovered this field called machine learning which is basically these mathematical algorithms that learn patterns and data, you know, recognize patterns, build predictive models, classification models, clustering models, all kinds of stuff from data. And since I was an astrophysicist, I had a million years of mathematics in my training so I love math and that’s…so machine learning was math and I said, “Boy, this is really cool stuff.”
And so I was sitting in my NASA office one day, an email comes over my line here, and the email is about a lunchtime talk on data mining. All right, this is pretty interesting. We had lunchtime talks every single day, sometimes two or three every day when I was at NASA, that was not unusual but what was unusual was the fact that it focused on this very topic I was questioning, data mining.
So a talk was given by an IBM researcher. So I say, “I’m gonna go to this and see if I can learn something.” And so I went to this talk and I swear to this day that she was the, I don’t remember the scientist’s name but she gave the best presentation in terms of the way she delivered her presentation that I’ve ever seen. I mean, I learned so much about public speaking in that one hour because here’s what she did. She spent the first half hour filling the blackboard with equations. after she had filled the blackboard with mathematics, she stopped, turned and faced her audience, us, and said: “I’m now gonna tell you about our summer internship program at IBM Watson Research Center.” And you could have heard a pin drop in the room like, “What? Here we’re having this very mathematical conversation and she’s gonna tell us about their summer intern program?” You know, I was like, “What?” I mean, the transition was just like, “What’s going on here?” And so, anyway, so she, again, she was very, I’m sure, very deliberate and clever and pausing at appropriate places, I call it the pregnant pause. So after she said that, she said, “Yeah.” She was probably reading our minds, that’s probably what was going on. Anyway, she said, “Yeah, we teach this stuff to high school interns in our summer program in inner-city New York.”
Kirk: And I said to myself, “Yeah, right. You’re teaching this, which Ph.D. scientists in the room right now are quandering over, to high school kids?” she said, “Yeah, we teach this stuff in the context of basketball. These kids love basketball. Street basketball, that’s their passion. Before school, during school, after school.” And then when what we do at IBM that, at that point in time, they had this software package called Advanced Scout which IBM used to basically sell to the professional basketball association. Every team owned this software which basically predicted next play during a game based upon the entire play-by-play histories of all the teams. So with, you know, depending upon the players on the court, how many players, whether it was a fast break or a pass or layup or whatever, time left on the clock, the score differential, the teams, all these factors were included in the model and they were able to predict next best play for the offense and what play to expect if you’re on the defense side.
Anyways, so she was telling these stories and I say, “Wow, that sounds really interesting.” Then she said, “Yeah, once the kids see this, once these students see this application of all this math stuff to something that they love and they’re passionate about, which is basketball, they buckle down and they learned the math and science like they’ve never seen it before.” So…
Tamara: Because it’s relevant to them. There’s that personal connection…
Kirk: With a personal connection.
Tamara: …for them at that point.
Kirk: So anyway, so if she had stopped there, I would have had what I was looking for, that hook. But she said something else after this which totally changed my life, and then, that’s not a joke. So she told all of the story and she said, “What’s important is to realize that this does really transform these students,” So she said to me, not to me, to the team there that these students come from high schools in the inner city of New York that the typical graduation rate in those high schools was less than 50% but she said that the students who come through their program have a graduation rate of 97%.
Tamara: Wow. Okay.
Kirk: And I literally remember to this day, that was 21 years ago, I remember to this day the thought I had in my head when she said that. I said, “If this stuff, this data mining and machine learning stuff has this much power to change people’s lives, I have got to do this for the rest of my life.”
So the space that I’m entrenched in is B2B enterprise and you look at these behemoths, these businesses and, you know, what you do see and I know I’m going to toss this out there. We all use the word “silo,” but come on, who isn’t out there and doesn’t know what I’m talking about? We have these islands, right? Now we have islands of innovation, right?
So there are people in various departments working on digital transformation in their own little area using big data and intelligent analytics for sure, but there’s no organization-wide initiative to unite them. And we know this is where the secret sauce is, right, the unification, but we’re still siloed. So, how would you advise businesses to start breaking down those silos and go about this more strategically?
Kirk: Well, I think the key thing I think about when I think about these islands and these silos is it’s the intersection, the points where they intersect where the discovery happens, where the innovation, that the new insight happens. So if you think just sort of like broadly that I’m gonna, you know, tear down the silos and force things together, that sounds vague, it doesn’t sound very strategic, it sounds sort of sloppy. But if you think about, okay, let’s say we have customer data, for example. We may have customer data in the sales department. We may have customer data in the customer complaint department. We may have customer data in the social sort of network channels that we monitor and interact with. So we know about our customers from many different dimensions, okay, whether they buy online, whether they buy in the store, etc.
And if we start saying, “How can we connect the information and knowledge we have about customers across these different dimensions, these different channels? Can we discover some new insights? For example, customers who would like certain kinds of products, at certain times of the year, certain times of day, certain geographic regions.” All of a sudden, you start combining these pieces of information, so connecting the dots. So that’s really what I’m thinking about, these islands of innovation are like, you know, big collections of dots. And you do a lot of connecting inside that island, but if you can start connecting it across the islands that, like, all of a sudden, you realize that, “This segment of my population really likes this kind of thing at this time of year,” and maybe, who knows? I mean, I’m just making things up here. It might be related to, for example, sporting events or seasonal holidays or whatever. There’s something more to the story that you’re not seeing because you’re not connecting the dots across those different groups, and so it’s the connection and integration, those points of intersection where you start gaining greater insight.
And I think back to my days in astrophysics. When I was coming up in graduate school, there was a very siloed approach to astrophysics. I mean, people probably don’t realize this if you’re not in astrophysics but it was very siloed. I mean, there were theoretical astrophysicists who basically their observatory was their brain. They’ve actually thought about things deeply and created new theories and models. There were optical astronomers, which many of us think of when we think of telescopes, you know, so the big glass telescopes on tops of mountains but there are also telescopes that look at infrared radiation. There’s telescopes that look at X-ray radiation, and radio rad-,… So all of these different branches of astronomy were siloed at that. I started looking at combinations of data from different types of observatories, infrared, and radio, and X-ray, and optical. And I would go to conferences and I was the only person in the room out of hundreds of people who was doing that. And I was like I felt really awkward because no one else was doing those combinations.
But when people started doing that, all of sudden, these amazing discoveries started happening in the field because all of a sudden, you realized that this thing that looked totally boring in one dataset was actually quite interesting in this other dataset. And then when you put the two things together, all of a sudden, you had this, what you previously thought was a boring thing being a totally unbelievably interesting thing. And so you can imagine products and customers, events, and services, all kinds of things in a typical business, when you start looking at those connecting points, those intersections across the different siloed data collections and siloed departments, you might just be amazed at what you could discover.
Tamara: I think that’s so interesting because when you look at these giant organizations, sometimes they’re so big, the left hand doesn’t know what the right is doing and, you know, we started talking at the beginning of this episode, you were talking about asking the right questions of the data whereas a lot of people are just looking for the data to magically manifest answers to questions they didn’t even ask and that doesn’t happen. And I’ve heard you talk about before that, you know, artificial intelligence and data strategies should focus on outcomes first, primarily, and I wanted to know if you could just drill down to a level of specificity what outcomes do you mean specifically?
Kirk: Well, there are outcomes that are deliberate, okay, so you have a mission statement on goals and objectives for your organization, maybe your entire business. And so if you can create metrics that measure performance against those outcomes, then those are the kinds of things you’re doing, but there are also other outcomes which are maybe a little bit more sort of dynamically evolving, okay, like customer satisfaction, customer experience, you know, things like this, so which, you know, maybe a little less hard to figure out, “What is that?” But, you know it when you see it. And so outcomes are not, you know, “Did I build the…” This is something I learned at NASA, “Did I build the right system?” versus, “Did I build the system right?”
Okay, you know, okay, so we built maybe some user interface for our online store or some help desk or something in our business, so we built the thing to some specification but is it really what our customers wanted? Is it really fulfilling the needs of our customers, the people who are actually, you know, providing our financial security as a business? And so did you build the system right or did you build the right system? And so it’s really about discovering those places where maybe you didn’t really understand the requirements in what success looks like until you see it. And then you say, you know it when you see it. Let me give you an example, if I may. So this…
Tamara: Oh, please, yes.
Kirk: So as part of digital transformation, I mean, that’s a pretty broad term in itself, there’s another term that I like to sort of weave in there which is that’s sort of the democratization of data, which sounds kind of scary but it really basically empowers people in all levels of the organization to do the kinds of things that we’re talking about. I mean, within the context, of course, of regulation and compliance of who can look at what data, of course, I’m not saying Wild, Wild West here. But within the context of who’s permitted to look at what allows people in all parts of the organization to look at different datasets and do some of these combinations to see if maybe they see something interesting that maybe they wouldn’t otherwise have done because it was not in their job description, so to speak.
And so there’s this company, an airline in Europe that’s basically sort of the economy airline that most Europeans like to use. And they have this data democratization policy that…I heard the president of this airline speak at a conference and he said he empowers everyone in his organization to look at their customer data, to look at their operations data, and see if they can find insights that will improve their business. And so he said, “We don’t have front office, back office anymore, everyone is a data scientist,” and I said, “Boy, that’s really interesting.”
So there was a person who, basically the back office, traditionally back office database engineer whose job it was, was to create the manifests for all the flights, that is the passenger lists, who’s flying on the planes, the seat assignments, you know, food allergies, etc., etc., etc., including who’s in their customer loyalty program, the frequent flyer programs, whatever, all kinds of information. That person basically, that job was to create that manifest for the flights every time.
Well, now that they have this empowerment of looking at the data, this person noticed one day, looking at this flight manifest, that one of their frequent flyers, one of their very most loyal customers, she was actually flying on a flight with her child, her daughter, 10-year-old daughter using one of the companion tickets, one of the free companion tickets that loyal customers get. And so that was interesting but what was really interesting that the person noticed was the birthdate of the daughter which was entered in the database coincided with the date of the flight.
Tamara: Oh, no.
Kirk: And so normally, so the story goes, the story goes, and again, maybe I’m embellishing this but the story goes is that in a normal siloed work environment, that person might say, you know, back office database engineer might say, “That’s interesting” and go on with their job, right? But they didn’t, they were empowered to do something with the data.
So I said, “Wow. That is amusing and interesting. What can we do about this?” So, here she, and, again, I’m elaborating here, but not knowing all the details. Here she contacted someone in the flight crew or, you know, through channels, and ultimately ended up getting this information in the hands of the flight crew for that particular flight. And so they went out and bought a small cake, maybe $5, a little cake, and during the flight, they came out and sang “Happy Birthday” to the little girl with the cake on the plane and they see… This corporate president who was telling this story, he said, “You would not believe the unbelievably positive social sentiment that exploded across Europe for our airline that day when people posted all these videos of everybody on the plane singing and how wonderful this airline is to celebrate this little girl’s birthday.” He said, “For all of a $5 investment, the positive customer sentiment and engagement that they got out of that was just unbelievable.” I mean, there was just no ROI, you can say, a $5 investment for a continent-wide celebration of what this airline did that day.
Tamara: Right. You know, it’s interesting what you’re talking about, and so, here we are talking about, you know, connecting islands of innovation and intelligent enterprise and what’s really interesting is what you brought up is something so uniquely human…
Kirk: And simple.
Tamara: …a birthday and a birthday cake, you know?
Kirk: It’s simple. [crosstalk 00:31:07] It’s not some neural network that did this, it’s the humanity of the person working with the data who saw it.
Tamara: That is absolutely the best use case ever. You know, I wonder, how do you think businesses can unite… I mean, we’re talking data, right? This is your specialty. How can they unite frontend data like customer interactions with backend data, the metrics that verify performance?
Kirk: Well, I’ve actually been writing about this lately. I talk about the yin and yang of data. I mean, there’s, data comes in on the frontend, then we have metrics on the backend that measure the performance of the analytics that we carry out against those data. And so it’s really a balancing act because, you know, you sort of think you know what you’re collecting your data for and you design metrics against those expected outcomes, but then there’s different outcomes, like the one that I just described, that it didn’t start off planning to have some amazing, positive customer sentiment on social media that day, so who knew that they were supposed to be measuring social media metrics that day, right? So you never really quite know for sure if you’re measuring the right thing or you’re collecting the right thing. And so you sort of like follow that tightrope of where you’re measuring and performing against data and then you tweak things, you know, to get it right.
So I think the key for me in all that is this concept of the culture of experimentation that you allow, you know, people to experiment with data. And then, again, it’s within the confines of compliance and regulatory restrictions on usage of data. That’s not, you know, I’m not saying go outside the bounds of that, but within the allowances you have to use and integrate data, then do it. Again, I go back to what I said earlier about integrating or finding the intersections. So for example, just connecting the dots between different customers, events, behaviors, outcomes, the different data sources that you have and see if there’s some kind of connectivity there that, you know, some kind of fabric that materializes, if I could say it that way. They’re not just isolated points, there’s some connection between these points that tells you a story.
Tamara: And that’s it, right, connecting those dots to tell a story. You know, what’s the role that a digital platform for data can play in connecting, you know, the islands of innovation we’ve been talking about?
Kirk: Well, the platform for me is that heavenly place where you’re curating these data and I always tell people that, all my years at NASA, we were managing data systems, basically running a digital library, and so I was channeling my inner librarian. I mean, I really found I loved it. I mean, being able to collect the data, annotate it, label it, basically creating the equivalent of a catalog, data catalog, to enable scientists to discover and find data that is relevant to their questions and use cases. And it’s that librarian role where you know what’s in your collection, you know what’s there, so when someone comes in and say they’re looking for something, not only can you direct them to it but you can say, “Hey, you might also be interested in this. And people who looked at that also looked at that.” Well, it’s starting to sound like a recommender engine, right? And it really sort of is.
Kirk: And so on this platform, you can collect all these data but what you also collect are the metadata, who’s using it, what were the use cases they used before, what were the outcomes that they got from it, what reports or documents or tables or graphs or whatever were generated from this. And so if people start annotating and tagging the data and that platform becomes that data curation place where those tags and annotations become part of the data collection, it’s metadata, which… Metadata sounds like a funny word to some people and sometimes people think it’s just sort of like date and time or something like that but metadata can be contextual and content-based. It could tell you about what’s in the data and how it was used and when it was used, and why it was used, and who used it, so it’s semantic and contextual, content-oriented.
And once that extra information is now in there, so metadata is just other data about your data, so now that you have other data about your data, you can start connecting the dots across the metadata. Find me other cases where people used data for fraud analytics for this particular type of claim or this particular type of event, and you’d be amazed what you can discover when you start adding those extra dimensions to the data and where can you possibly do that, where can you possibly integrate all that but a platform?
Tamara: So who do you think the key stakeholders in the organization are that should be helping drive these data initiatives? And by the way, Kirk, who do you think needs a seat at the table?
Kirk: Now, you’re getting…
Tamara: We should have been really majestic music in here.
Kirk: Yes. The music swells.
Tamara: Right? Trick question.
Kirk: Okay, I’m gonna give you a trick answer.
Kirk: The trick answer is the CEO. Over 100 years ago, one of the primary executives in the C-suite with the seat at the table was the CEO, the Chief Electricity Officer. Why? Because electricity was a new thing. People didn’t know what it was about. It was disruptive to the business. It changed the way they thought about their business and how they powered their business and ran their business and there were new revenue streams and new, you know, new costs and risks and benefits and it’s like they needed this person to sit there to help manage all of those fears and things. And I think about analytics and data and AI in the same way, the new electricity, I mean, we’ve seen the hype cycles, right? Five, six years ago, data’s the new electricity.
Kirk: And now it’s AI is the new electricity and, you know, pretty soon, it’s gonna be blockchain is the new electricity, then it’s gonna be quantum computing is the new electricity. And so every organization will have a different person who’s in that managing of the fear and benefits and risks role and that could be the chief analytics officer, chief AI officer, chief algorithm officer, chief data scientist, I mean, the list goes on and on, chief product officer, chief marketing officer. And I don’t want to say it’s any specific one of those because it depends upon your organization but, again, it’s got to be someone who is, got some kind of handle on risk and benefits, costs and revenues associated with this thing, the culture change that’s gonna come with it because people will fear it. There’s change in digital disruption.
Tamara: Yes. And that’s real, by the way, Kirk. I mean, globally, I think the future of work is on everyone’s mind.
Kirk: There’s also the fear on the outside about, “What are you doing with my data?” The customers’ customers. You know, or the…GDPR, you think about nations, governments who are worrying about what you’re doing with the data. So, yes, so there’s all these issues both internally and externally. And algorithms, same thing, but about trust and transparency, explainability, all those kinds of things. So you can’t say that this person’s CDO or CAO is going to necessarily know all of those details. But they certainly need to manage those details. And so they have to be aware of what is sort of, you know, what is happening in the markets or right now, there’s all this concern about future of work, there’s this concern about automation and is it gonna displace jobs. But I think we all know, I think, that every industrial revolution does change the work that people do. I had a big laugh at a conference last week because people started saying, I was on an AI panel and people started saying the exact same things that I’ve been saying and they were reading my mind. I said, you know, 30 years ago, when sort of the PC, the desktop computing revolution started, the story was, “Oh, wow, we’re gonna be so much more efficient in our work, we’ll be working 20 hours a week and we’ll have all this relaxation time.” Well, I’m sort of wondering what happened with that because I’m not seeing it.
Tamara: I’m not seeing it either. I mean, like, where’s my relaxation time?
Kirk: It’s true, the amount of work that I do in a given week is now a factor of two different but it’s a factor of two in the other direction.
Tamara: Yeah. I’m not seeing the release of a burden for me yet, but I am looking forward to that. And what’s funny because what you’re saying is when I think of like the chief data officer and the chief analytics officer, it’s almost like they need to come to the executive table with their crystal balls and a little turban and go, “What I see for the future is…” because it’s almost as if we’re asking them to do the impossible and to know that which we don’t even know yet, which then kind of circles me back to the big data piece which is, you know, what are the advantages of using big data and intelligent analytics more strategically? I mean, what happens when we turn the islands of innovation into a connected continent?
Kirk: Wow, then you have mission success.
Tamara: Did you like that question?
Kirk: I did. Right? I had an aha moment recently that really moved me. So for years, I sort of learned the language of system engineering when I was at NASA, right? You’d figure out what are the user requirements for this thing you’re building, you know, what are the users expected to do, what do the stakeholders expect, and then you design the system to that, you have system requirements, you know, functional requirements, user requirements, and then you build to that. And I learned that language and I learned how to do that and I think of sort of data science that way, that what are the outcomes we’re trying to achieve in our business, and how we design products, and services and analytics and tools, etc., to get there. So I was all on board with this. I was having this conversation with a sort of senior professor that I know at the university who had quite a colorful career himself. He was the vice president of a very large corporation before that and he worked on the NASA Apollo program decades ago. And he said, “Kirk, you’re missing a point here.” And I said, “Well, what’s the point?” And he said, “Let me tell you. When we were in the Apollo program, we didn’t care about system engineering. The systems people cared about system engineering. We cared about mission engineering. And I said, “What the heck is mission engineering?” He said, “Mission engineering is when you measure your success and you design towards the success of the mission, not the success of the individual systems.” So, for example…
Kirk: …on the Apollo mission, there was the lander, there was the orbiter, there was the launch system, there was the recovery system, you know, there was the actual on surface of the moon operations system, there were all these different systems that had requirements and they were built and they worked fine and everything was great. But he said, “The mission, NASA’s mission was not successful until those astronauts were returned home safely to their families.” That is the success factor for the mission, getting those astronauts back home in their homes with their families.
Tamara: You know, that’s really profound, actually.
Kirk: And I just was, like, really moved by this and he and I were standing in a parking lot having this conversation, we were just leaving some conference and we ended been standing in the parking lot for two hours talking about this and we designed the concept for a book that he and I keep, in fact, he called me yesterday about the book on this concept of mission engineering because it’s really mission success. And so strategic thinking is what is the overall mission of the business, what does the overall mission success look like for the business, not individual departments or individual islands of innovation as you’ve been calling it. So it’s getting all of the team together onto this continent to fulfill the mission of the organization. The success of the organization is the mission, not the systems.
Kirk, before we end our… I actually loved this chat with you. I have just three quick questions for you, totally off the cuff, and I think you’re gonna embrace these because I’m talking about the future. So, how do you see the field continuing to evolve in the next five years and really, to be more specific, what do you think you and I would be talking about if we were having this conversation five years from now?
I mean, I don’t know, obviously, but a short answer I like to give is more of the same in the sense that what do we see happening? We see a lot more integration and convergence, I think, of everything coming to the platform where you got the databases, the analytics, the algorithms, the products, the customer interactions. So we’re gonna see more sort of a platformification, if you will, where there are entire businesses as we know like ride shares and room shares, and other kinds of things where the businesses are run off of a platform. They don’t actually own the asset that’s being used, for example, on ride shares.
And so platformification, that’s basically the convergence of all kinds of technologies like the analytics and the cloud and the computing and algorithm, so we’re gonna see that. And consequently, we’re gonna see a lot more things that are as a service, you know, platform as a service, infrastructure as a service, algorithm, deep learning as a service, computer vision as a service. We’re gonna see huge amounts of development in those areas, and who knows what will come in those different areas, but we’re gonna see the ability basically to call a service or call an algorithm, an API.
Tamara: Yeah. And leave us with this one thing. What or who inspires you the most? What are you most passionate about when you wake up every day?
Tamara: Yeah, you didn’t get that question either but that’s on purpose. We really want to know from you.
Kirk: Well, it’s multidimensional. I don’t know if I could say a single thing. I mean, certainly, just loving my family and loving my life, for being able to follow the passions that I have which includes scientific discovery ever since that nine-year-old experience. It could be astrophysics or it can be something else, it doesn’t matter. And also I’m a…like I said, I sort of like curating knowledge so that, it gets me excited when I get on Twitter in the morning and I find new and interesting things to share with people, basically curating the knowledge of the world around data science and AI just through that platform, I get excited by that every day because it’s…my inner educator, I guess, is being fulfilled when I do that. And so anyway, I think that just reaching out to people and touching people, you know, through this and I love to see the excitement in people’s eyes and faces when I tell them these stories. It sort of energizes me. I’ve been energized by students in my classes who come in, especially in the freshman data science class, and in that freshman class, it’s typically students who say that they’re not very interested in math or science but they need a class to graduate, a science class, and data science looks more attractive to them than, say, physics or chemistry or biology or something, and by the end of the semester, they’re hooked on math and science. So that really charges my batteries when I see that transformation and people said, “Wow, I can really do this.”
Tamara: I see that you really care. Kirk, you inspire me. Ladies and gentlemen, Kirk Borne, Principal Data Scientist at Booz Allen. Thank you so much for this incredible conversation and for your time, your energy, and your humanity. I really appreciate your authenticity and transparency with us today and being willing to answer a lot of questions that you had no idea I was going to ask.
Kirk: Well, Tamara, this was a real pleasure to talk with you, thank you so much for making this opportunity possible.