>>from around the globe. It's the Cube with digital coverage of biz ops Manifesto unveiled. Brought to you by biz ops Coalition. Hey, welcome back your body, Jeffrey here with the Cube. Welcome back to our ongoing coverage of the busy ops manifesto unveiling its been in the works for a while. But today is the day that it actually kind of come out to the to the public. And we're excited to have a real industry luminary here to talk about what's going on, Why this is important and share his perspective. And we're happy to have from Cape Cod, I believe, is Tom Davenport. He is a distinguished author on professor at Babson College. We could go on. He's got a lot of great titles and and really illuminate airy in the area of big data and analytics. Thomas, great to see you. >>Thanks, Jeff. Happy to be here with you. Great. >>So let's just jump into it, you know, and getting ready for this. I came across your LinkedIn post. I think you did earlier this summer in June and right off the bat, the first sentence just grabbed my attention. I'm always interested in new attempts to address long term issues, Uh, in how technology works within businesses. Biz ops. What did you see in biz ops? That that kind of addresses one of these really big long term problems? >>Well, yeah. The long term problem is that we've had a poor connection between business people and I t people between business objectives and the i t. Solutions that address them. This has been going on, I think, since the beginning of information technology, and sadly, it hasn't gone away. And so busy ops is new attempt to deal with that issue with a, you know, a new framework. Eventually a broad set of solutions that increase the likelihood that will actually solve a business problem with a nightie capability. >>Right. You know, it's interesting to compare it with, like, Dev ops, which I think a lot of people are probably familiar with, which was, you know, built around a agile software development and the theory that we want to embrace change that that changes okay on. We wanna be able to iterate quickly and incorporate that, and that's been happening in the software world for for 20 plus years. What's taking so long to get that to the business side because the pace of change is change on the software side. You know, that's a strategic issue in terms of execution on the business side that they need now to change priorities. And, you know, there's no P R D S and M R. D s and big giant strategic plans that sit on the shelf for five years. That's just not the way business works anymore. Took a long time to get here. >>Yeah, it did. And, you know, there have been previous attempts to make a better connection between business and i t. There was the so called strategic alignment framework that a couple of friends of mine from Boston University developed, I think more than 20 years ago. But, you know, now we have better technology for creating that linkage. And the, you know, the idea of kind of ops oriented frameworks is pretty pervasive now. So I think it's, um you know, time for another serious attempt at it, right? >>And do you think doing it this way right with the bizarre coalition, you know, getting a collection of of kind of like minded individuals and companies together and actually even having a manifesto which were making this declarative statement of principles and values. You think that's what it takes to kind of drive this, you know, kind of beyond the experiment and actually, you know, get it done and really start to see some results in, in in production in the field. >>Well, you know, the manifesto approach worked for Karl Marx and communism. So maybe it'll work. Here is Well, now, I think certainly no one vendor organization can pull this off single handedly. It does require a number of organizations collaborating and working together. So I think a coalition is a good idea, and a manifesto is just a good way to kind of lay out. What you see is the key principles of the idea, and that makes it much easier for everybody. Toe I understand and act on. >>Yeah, I I think it's just it's really interesting having you know, having them written down on paper and having it just be so clearly articulated both in terms of the of the values as well as as the the principles and and the values, you know, business outcomes, matter, trust and collaboration, data driven decisions, which is the number three or four and then learn responded Pivot, It doesn't seem like those should have to be spelled out so clearly, but obviously it helps to have them there. You can stick them on the wall and kind of remember what your priorities are. But you're the data guy. You're the analytics guy. Uh, and a big piece of this is data analytics and moving to data driven decisions. And principle number seven says, you know, today's organizations generate more data than humans can process. And informed decisions can be augmented by machine learning and artificial intelligence right up your alley. You know, you've talked a number of times on kind of the many stages of analytics Onda how that's evolved over over time. You know, it is you think of analytics and machine learning driving decisions beyond supporting decisions, but actually starting to make decisions in machine time. What's that? What's that think for you? What does that make you? You know, start to think Wow, this is this is gonna be pretty significant. >>Yeah, well, you know, this has been a long term interest of mine. Um, the last generation of a I I was very interested in expert systems. And then e think more than 10 years ago I wrote an article about automated decision making using, um, what was available then, which is rule based approaches. But, you know, this address is an issue that we've always had with analytics and ai. Um, you know, we tended Thio refer to those things as providing decision support. The problem is that if the decision maker didn't want their support, didn't want to use them in order to make a decision, they didn't provide any value. And so the nice thing about automating decisions with now contemporary ai tools is that we can ensure that data and analytics get brought into the decision without any possible disconnection. Now, I think humans still have something to add here, and we often will need to examine how that decision is being made and maybe even have the ability to override it. But in general, I think, at least for, you know, repetitive tactical decisions, um, involving a lot of data. We want most of those I think, to be at least, um, recommended, if not totally made by analgesic rhythm or an AI based system, and that I believe would add to the quality and the precision and the accuracy of decisions in in most organizations. >>You know, I think I think you just answered my next question before I before I asked it. You know, we had Dr Robert Gates on the former secretary of Defense on a few years back, and we were talking about machines and machines making decisions, and he said at that time, you know, the only weapon systems that actually had an automated trigger on it, We're on the North Korea and South Korea border. Everything else, as you said, had to go through some person before the final decision was made. And my question is, you know what are kind of the attributes of the decision that enable us to more easily automated? And then how do you see that kind of morphing over time both as the data to support that as well as our comfort level, Um, enables us to turn Maura Maura actual decisions over to the machine? >>Well, yeah, I suggested we need data and the data that we have to kind of train our models has to be high quality and current, and we need to know the outcomes of that data. You know, most machine learning models, at least in business, are supervised, and that means we need tohave labeled outcomes in the in the training data. But, you know, the pandemic that we're living through is a good illustration of the fact that the the data also have to be reflective of current reality. And, you know, one of the things that we're finding out quite frequently these days is that the data that we have do not reflect. You know what it's like to do business in it. Pandemic it. I wrote a little piece about this recently with Jeff Cam at Wake Forest University. We call it Data Science quarantined, and we interviewed somebody who said, You know, it's amazing what eight weeks of zeros will do to your demand forecast. We just don't really know what happens in a pandemic. Our models may be have to be put on the shelf for a little while and until we can develop some new ones or we can get some other guidelines into making decisions. So I think that's one of the key things with automated decision making. We have toe, make sure that the data from the past and you know, that's all we have, of course, is a good guide toe. You know what's happening in the present and and the future as far as we understand it. >>Yeah, I used to joke when we started this calendar year 2020 is finally the year that we know everything with the benefit of hindsight. But it turned out 2020 the year we found out we actually know nothing and everything way. But I wanna I wanna follow up on that because, you know, it did suddenly change everything, right? We got this light switch moment. Everybody's working from home now. We're many, many months into it, and it's going to continue for a while. I saw your interview with Bernard Marr and you had a really interesting comment that now we have to deal with this change. We don't have a lot of data and you talked about hold, fold or double down and and I can't think of, um or, you know, kind of appropriate metaphor for driving the value of the biz ops. When now your whole portfolio strategy, um, needs to really be questioned. And, you know, You have to be really well, executing on what you are holding, what you're folding and what you're doubling down with this completely new environment. >>Well, yeah, And I hope I did this in the interview. I would like to say that I came up with that term, but it actually came from a friend of mine who's a senior executive at gen. Packed. And I used it mostly to talk about AI and AI applications, but I think you could You could use it much more broadly to talk about your entire sort of portfolio of digital projects you need to think about. Well, um, given some constraints on resource is and a difficulty economy for a while. Which of our projects do we wanna keep going on Pretty much the way we were And which ones, um, are not that necessary anymore. You see a lot of that in a I because we had so many pilots, somebody for me, you know, we've got more pilots around here, then O'Hare airport in a I, um and then the the ones that involve double down there, even mawr Important to you, they are, you know, a lot of organizations have found this out in the pandemic on digital projects, it's more and more important for customers to be ableto interact with you, um, digitally. And so you certainly wouldn't want toe cancel those projects or put them on hold. So you double down on them, get them done faster and better. >>Another. Another thing that came up in my research that that you quoted, um, was was from Jeff. Bezos is talking about the great bulk of what we do is quietly but meaning fleeing, improving core operations. You know, I think that is so core to this concept of not AI and machine learning and kind of the general sense, which which gets way too much buzz but really applied, applied to a specific problem. And that's where you start to see the value and, you know, the biz ops. Uh, manifesto is calling it out in this particular process, but I just love to get your perspective. As you know, you speak generally about this topic all the time, but how people should really be thinking about where the applications where I can apply this technology to get direct business value. >>Yeah, well, you know, even talking about automated decisions? Uh, the kind of once in a lifetime decisions, uh, the ones that a g laugh Li, the former CEO of Proctor and Gamble, used to call the big swing decisions. You only get a few of those, he said. In your tenure as CEO, those air probably not going to be the ones that you're automating in part because you don't have much data about them. You're only making them a few times, and in part because they really require that big picture thinking and the ability to kind of anticipate the future that the best human decision makers have. Um, but in general, I think where they I the projects that are working well are you know what I call the low hanging fruit ones? The some people even report to refer to it as boring A I so you know, sucking data out of a contract in order to compare it Thio bill of lading for what arrived at your supply chain. Companies can save or make a lot of money with that kind of comparison. It's not the most exciting thing, but a I, as you suggest, is really good at those narrow kinds of tasks. Um, it's not so good at the at the really big Moonshots like curing cancer or, you know, figuring out well, what's the best stock or bond under all circumstances or even autonomous vehicles. We made some great progress in that area, but everybody seems to agree that they're not going to be perfect for quite a while. And we really don't wanna be driving around on, um in that very much, unless they're, you know, good and all kinds of weather and with all kinds of pedestrian traffic. And you know that sort of thing, right? >>That's funny. Bring up contract management. I had a buddy years ago. They had a startup around contract management, and I'm like, and this was way before we had the compute power today and and cloud proliferation. I said, You know how How could you possibly built off around contract management? It's language. It's legalese. It's very specific. He's like Jeff. We just need to know where's the contract and when does it expire? And who's a signatory? And he built a business on those you know, very simple little facts that weren't being covered because their contracts from People's drawers and files and homes, and Lord only knows So it's really interesting, as you said, these kind of low hanging fruit opportunities where you could extract a lot of business value without trying to, you know, boil the ocean. >>Yeah, I mean, if you're Amazon, Jeff Bezos thinks it's important toe have some kind of billion dollar projects, and he even says it's important to have a billion dollar failure or two every year. But I think most organizations probably are better off being a little less aggressive and, you know, sticking to what a I has been doing for a long time, which is, you know, making smarter decisions based on based on data. >>Right? So, Tom, I want to shift gears one more time before before you let Ugo on on kind of a new topic for you, not really new, but you know, not not the vast majority of your publications. And that's the new way toe work, you know, as as the pandemic hit in mid March, right? And we had this light switch moment. Everybody had to work from home, and it was, you know, kind of crisis and get everybody set up well you know, Now we're five months, six months, seven months. A number of companies have said that people are not gonna be going back to work for a while. And so we're going to continue on this for a while, and then even when it's not what it is now, it's not gonna be what it was before. So, you know, I wonder and I know you, you tease. You're working on a a new book, you know, some of your thoughts on, you know, kind of this new way. Uh, toe work and and and the human factors in this new, this new kind of reality that we're kind of evolving into, I guess. >>Yeah, This was an interest of mine. I think back in the nineties, I wrote an article called Ah Co authored an article called Two Cheers for the Virtual Office. And, you know, it was just starting to emerge. Then some people were very excited about it. Some people were skeptical and we said to cheers rather than three cheers because clearly there's some shortcomings and, you know, I keep seeing these pop up. It's great that we can work from our homes. It's great that we can accomplish most of what we need to do with a digital interface. But you know, things like innovation and creativity and certainly a a good, um, happy social life kind of requires some face to face contact every now and then. And so you know, I think we'll go back to an environment where there is some of that. We'll have, um, time when people convene in one place so they can get to know each other face to face and learn from each other that way. And most of the time, I think it's a huge waste of people's time to commute into the office every day and toe jump on airplanes. Thio, Thio give every little mhm, uh, sales call or give every little presentation. We just have to really narrow down. What are the circumstances, where face to face contact really matters and when can we get by with digital? You know, I think one of the things in my current work I'm finding is that even when you have a I based decision making, you really need a good platform in which that all takes place. So in addition to these virtual platforms, We need to develop platforms that kind of structure the workflow for us and tell us what we should be doing next and make automated decisions when necessary. And I think that ultimately is a big part of biz ops as well. It's not just the intelligence oven, a isis some, but it's the flow of work that kind of keeps things moving smoothly throughout your organization. Yeah, >>I think such such a huge opportunity as you just said, because I forget the stats on how often were interrupted with notifications between email text, slack asana, salesforce The list goes on on and on. So, you know, t put an AI layer between the person and all these systems that are begging for attention. And you've written a you know, a book on the attention economy, which is a whole nother topic will say for another day. You know, it really begs. It really begs for some assistance because, you know, you just can't get him picked, you know, every two minutes and really get quality work done. It's just not it's just not realistic. And you know what? I don't think that's the future that we're looking for. >>Great totally alright, >>Tom. Well, thank you so much for your time. Really enjoyed the conversation. I gotta dig into the library. It's very long song. I might started the attention economy. I haven't read that one in to me. I think that's the fascinating thing in which we're living. So thank you for your time. And, uh, great to see you. >>My pleasure, Jeff. Great to be here. >>All right, take care. Alright. He's Tom. I'm Jeff. You are watching the continuing coverage of the biz ops manifesto. Unveil. Thanks for watching. The Cube will see you next time.