 Hi, I'm Peter Burris and welcome to another Wikibon action item. One of those pressing strategic issues that businesses face is how to get more value out of their data. In our opinion, that's the essence of a digital business transformation, is the using of data as an asset to improve your operations and take better advantage of market opportunities. The problem with data though, it's shareable. It's copyable. It's reusable. It has, it's easy to create derivative value out of it. One of the biggest misnomers in the digital business world is the notion that data is the new fuel or the new oil. It's not. You can only use oil once. You can apply it to a purpose and not multiple purposes. Data you can apply to a lot of purposes which is why you are able to get such interesting and increasing returns to that asset if you use it appropriately. Now, this becomes especially important for technology companies that are attempting to provide digital business technologies or services or other capabilities to their customers. In the consumer world, it started to reach ahead. Questions about Facebook's reuse of a person's data through an ad-based business model is now starting to lead people to question the degree to which the information asymmetry about what I'm giving and how they're using it is really worth the value that I get out of Facebook is something that consumers and certainly governments are starting to talk about. It's also one of the basis for GDPR which is going to start enforcing significant fines in the next month or so. In the B2B world, that question is going to become especially acute. Why? Because as we try to add intelligence to the services and the products that we are utilizing within digital business, some of that requires a degree of or some sort of relationship where some amount of data is passed to improve the models and machine learning and AI that are associated with that intelligence. Now some companies have come out and said flat out they're not going to reuse a customer's data. IBM being a good example of that when Ginny Remediate, IBM Thinks said we're not going to reuse our customer's data. The question for the panel here is is that going to be a part of a differentiating value proposition in the marketplace? Are we going to see circumstances in which companies keep perhaps products and services low by reusing a client's data and others sustaining their experience and sustaining a trust model, say they won't. How is that going to play out in front of customers? So joining me today here in the studio, David Floyer. Hi there. And on the remote lines, we have Neal Raiden, Jim Cabela, George Gilbert, and Ralph Fino. Say guys. Hey, how are you doing? All right, so. Good morning, good morning. So, Neal, let me start with you. You've been in the BI world as a user, as a consultant for many, many number of years. Help us understand the relationship between data, assets, ownership, and strategy. Oh, God. Well, I don't know that I've been in the BI world but I've been in the BI world. Anyway, as a consultant, when we would do a project for a company, there were very clear lines of what belonged to us and what belonged to the client. They were paying us generously. They would allow us to come into their company and do things that they needed. And in return, we treated them with respect. We wouldn't take their data. We wouldn't take their data models that we built, for example, and sell them to another company. That's just, as far as I'm concerned, that's just theft. So, if I'm housing another company's data because I'm a cloud provider or some sort of application provider, and I say, well, you know, I can use this data too. To me, the analogy is I'm a warehousing company. And independently, I go into the warehouse and I say, you know, these guys aren't moving their inventory fast enough, I think I'll sell some of it. It just isn't right. All right, that's a great point. Jim Cabela, as we think, though, about the role that data, machine learning play, training models, delivering new classes of services, we don't have a clean answer right now. So, what's your thought and how this is likely to play out? I agree totally with Neil, first of all. If it's somebody else's data, you don't own it, therefore you can't sell it. You can't monetize it, clearly. But where you have derivative assets, like machine learning models that are derivative from data, it's the same phenomenon, it's the same issue, a higher level. You can build and train or should own your machine learning models only from data that you have legal access to. You own or you have license and so forth. So, as you're building these derivative assets, first and foremost, make sure as you're populating your data lake to build and do the training that you have clear ownership over the data. So, with GDPR and so forth, we have to be doubly, triply vigilant to make sure that we're not using data that we don't have authorized ownership or access to. That is critically important. And so, I get kind of queasy when I hear about some people say, we use blockchain to make the sharing of training data more distributed and federated and did data, whatever. It's like, wait a second, that doesn't solve the issues of ownership. That makes it even more problematic. If you get this massive blockchain of data coming from Hither and Yacht, who owns what? How do you know? Do you dare build any models whatsoever from any of that data? That's a huge gray field, that area that nobody's really addressed yet. Yeah, well, it might mean that the blockchain has been poorly designed. I think that we talked in one of the previous action items about the role that blockchain design is going to play. But moving aside from the blockchain, so it seems as though we generally agree that data is owned by somebody typically and that the ownership of it, as Neil said, means that you can't intercept it at some point in time just because it is easily copied and then generate rents on it yourself. David Foyer, what does that mean from a ongoing systems design and development standpoint? How are we going to assure, as Jim said, not only that we know what data is ours, but make sure that we have the right protection strategies and sense in place to make sure that the data as it moves, we have some influence and control over it. Well, my starting point is that AI and AI-infused products are fueled by data. You need that data and Jim and Neil have already talked about that. In my opinion, the most effective way of improving a company's products, whatever the products are from manufacturing, agriculture, financial services, is to use AI-infused capabilities. That is likely to give you the most best return on your money. And businesses need to focus on their own products. That's the first place you're trying to protect from anything, anybody coming in. Businesses own that data. They own the data about your products in use by your customers. Use that data to improve your products with AI-infused function and use it before your competition eats your lunch. Well, let's build on that. So we're not saying that, for example, if you're a storage system supplier, since that's a relatively easy one, you've got very, very fast SSDs, very, very fast NVMe over fabric, great technology. You can collect data about how that system is working, but that doesn't give you rights to then also collect data about how the customer is using the system. There is a line which you need to make sure that you are covering. For example, call home on a product, any product, whose data is that. You need to make sure that you can use that data. You have some sort of agreement with the customer. And that's a win-win because you're using that data to improve the product, prove things about it. But that's very, very clear that you should have a contractual relationship, as Jim and Neil were pointing out. You need the right to use that data. It can't be underhand. But you must get it because if you don't get it, you won't be able to improve your products. Now we're talking here about technology products which have often very concrete and obvious ownership and people who are specifically responsible for administering them. But when we start getting into the IoT domain or in other places where the device is infused with intelligence but and might be collecting data that's not directly associated with its purpose, just by virtue of the nature of the sensors that are out there. And the whole concept of digital twin introduces some tension in all of this. George Gilbert, take us through what's been happening with the overall suppliers of technology that are related to digital twin building, designing, et cetera. How are they securing or making promises committing to their customers that they will not cross this data boundary as they improve the quality of their twins? Well, as you quoted Jenny Rometti in starting out, she's saying IBM, unlike its competitors will not take advantage and leverage and monetize your data. But it's a little more subtle than that. And digital twins are just sort of another manifestation of industry specific sort of solution development that we've done for decades. The differences, as Jim and David have pointed out, that with machine learning, it's not so much code that's at the heart of these digital twins. It's the machine learning models and the data is what informs those models. Now, so you don't want all your secret sauce to go from Mercedes Benz to BMW. But at the same time, the economics of industry solutions means that you do want some of the repeatability that we've always gotten from industry solutions. You might have parts that are just company specific. And so in IBM's case, if you really parse what they're saying, they take what they learn in terms of the models from the data when they're working with BMW. And some of that is going to go into the industry specific models that they're going to use when they're working with Mercedes Benz. If you really, really sort of peel the onion back and ask them, it's not the models, it's not the features of the models, but it's the coefficients that weight the features or variables in the models that they will keep segregated by customer. So in other words, you get some of the benefits, the economic benefits of reuse across customers in, you know, with similar expertise, but you don't actually get all of the secret sauce. Now, Ralph, you know- I agree with George here. I think that's an interesting topic. And that's the important, one of the important points. It's not kosher to monetize data that you don't own, but conceivably, if you can abstract from that data at some higher level, like what George is describing in terms of weights and coefficients and so forth, in a neural network that's derivative from the model, at some point in the abstraction, where you should be able to monetize. I mean, it's like a paraphrase of some copyrighted material. A paraphrase, I'm not a lawyer, but you can, you know, you can monetize, you can sell a paraphrase because it's your own original work that's based obviously on your reading a Moby Dick or whatever it is you're paraphrasing. I agree with that, but there's a line. There was a guy who worked at Capital One. This was about 10 years ago and he was their chief statistician or whatever. This is before we had words like, you know, machine learning and data science. It was called statistics and predictive analytics. He left the company and formed his own company and re-coded all of the algorithms he had for about 20 different predictive models, formed a company and then licensed that stuff to CyBase and Teradata and whatnot. Now, the question I have is, did that cross the line or didn't it? These were algorithms that were actually developed inside Capital One. Did he have the right to use those even if he wrote new computer code to make them run in databases? So it's more than just data, I think. It's, look, it's a marketplace. And I think that if you own something, someone should not be able to take it and make money on it. But that doesn't mean you can't make an agreement with them to do that. And I think we're going to see a lot of that. Look, IMS gets data on prescription drugs and IRI and Nielsen gets scanner data and they pay for it. And then they add value to it and they resell it. So I think that's really the issue is the use has to be understood by all parties and the compensation has to be appropriate to the use. All right, so Ralph Finos, as a guy who looks at market models and handles a lot of the fundamentals for how we do our forecasting, look at this from a standpoint of how people are going to make money. Because clearly what we're talking about sounds like is the idea that any derivative use is embedded in algorithms. Seeing how those contracts get set up, and I got a comment on that in a second. But the promise a number of years ago was that people are going to start selling data willy nilly as a basis for their economic or as a way of capturing value out of their economic activities or their business activities hasn't matured yet generally. Do we see like this brand new data economy where everybody's selling data to each other being the way that this all plays out? Yeah, I think I'm having a hard time imagining this as a marketplace. I think we pointed at the manufacturing industries, technology industries where some of this makes some sense. But I think from a practitioner perspective, you're looking for variables that are meaningful that are in a form you can actually use to make prediction that you understand what the history and the validity of that data is. And in a lot of cases, there's a lot of garbage out there that you can't use. And the notion of paying for something that ultimately you look at and say, oh, crap, it's not, this isn't really helping me is going to be maybe not an insurmountable barrier, but it's going to create some obstacles in the market for adoption of this kind of thought process. We have to think about the utility of the data that comes that feeds your models. Yeah, I think there's going to be a lot of like there's going to be a lot of legal questions raised. And I recommend that people go look at a recent Silicon Angle article written by Mike Wheatley and edited by our editor-in-chief Rob Hoef about Microsoft letting technology partners own right to joint innovations. This is a quite a difference. This is quite a change for Microsoft who used to send you, if you sent an email with an idea to them, you'd often get an email back saying, oh, just to let you know, any correspondence we have here is the property of Microsoft. So there clearly is tension in the model about how we're going to utilize data and enable derivative use and how we're going to share, how we're going to appropriate value when sharing the returns of that. I think this is going to be an absolutely central feature of business models, certainly in the digital business world for quite some time. The last thing I'll know and then I'll get to the action items. The last thing I'll mention here is that one of the biggest challenges in whenever we start talking about how we set up businesses and institutionalize the work that's done is to look at the nature of the assets and the scope of the assets. And in circumstances where the asset is used by two parties and it's generating a high degree of value as measured by the transactions against those assets, there's always going to be a tendency for one party to try to take ownership of it. One party that's able to generate greater returns than the other almost always makes move to try to take more control out of that asset. And that's the basis of governance. And so everybody talks about data governance as though it's like something that you worry about with your backup and restore. Well, that's important, but this notion of data governance increasingly is going to become a feature of strategy and boardroom conversations about what it really means to create data assets, to stay in those data assets, get value out of them and how we determine whether or not the right balance is being struck between the value that we're getting out of our data and third parties are getting out of our data, including customers. So with that, let's do a quick action. I'm David Floyer. I'm looking at you. Why don't we start here? David Floyer, action item. So my action item is for businesses, you should focus, focus on data about your products in use by your customers to improve, help improve the quality of your products in infuse AI into those products as one of the most efficient ways of adding value to it. And do that before your competition has a chance to come in and get the data that will stop you from doing that. George Gilbert, action item. I guess mine would be that in most cases you want to embrace some amount of reuse because of the economics involved from your joint development with a solution provider, but if others are going to get some benefit from sort of reusing some of the intellectual property that informs models that you build, make sure you negotiate with your vendor that any upgrades to those models, whether they're their digital twins or in other forms, that there's a canonical version that can come back and be an upgraded path for you as well. Jim Cabela, action item. My action item is for businesses to regard your data as a product that you monetize yourself or if you are unable to monetize it yourself if there is a partner like a supplier or a customer who can monetize that data and then negotiate the terms of that monetization in your relationship and be vigilant on that. So you get a piece of that stream even if the bulk of the work is done by your partner. Neil Raiden, action item. It's all based on transparency. Your data is your data. No one else can take it without your consent. That doesn't mean that you can't get involved in relationships where there's an agreement to do that, but the problem is most agreements, especially when you look at a business consumer are so onerous that nobody reads them and nobody understands them. So the person providing the data has to have an unequivocal right to sell it to you and the person buying it has to really understand what the limits are that they can do with it. Ralph Finos, action item. You're muted Ralph, but it was brilliant whatever it was. What it was, and I really can't say much more than that. Yeah, but I think from a practitioner perspective and I understand from a manufacturing perspective how the value could be there, but as a practitioner, if you're fishing for data out there that someone has that might look like something you can use, chances are it's not. And you need to be real careful about spending money to get data that you're not really clear is going to help you. Great, all right, thanks very much team. So here's our action item conclusion for today. The whole concept of digital business is predicated in the idea of using data assets in a differential way to better serve your markets and improve your operations. It's your data. Increasingly that is going to be the basis for differentiation and any weak undertaking to allow that data to get out has the potential that someone else can through their data science and their capabilities re-engineer much of what you regard as your differentiation. We've had conversations with leading data scientists who say that if someone were to sell customer data into an open marketplace, that it would take about four days for a great data scientist to re-engineer almost everything about your customer base. So as a consequence, we have to tread lightly here as we think about what it means to release data into the wild. Ultimately, the challenge there for any business will be how do I establish the appropriate governance and protections, not just looking at the technology but rather looking at the overall notion of the data assets. If you don't understand how to monetize your data and nonetheless enter into a partnership with somebody else, by definition, that partner is going to generate greater value out of your data than you are. There's significant information asymmetries here. So it's something that everybody, every company must undertake an understanding of how to generate value out of their data. We don't think that there's going to be a general purpose marketplace for sharing data in a lot of ways. This is going to be a heavily contracted arrangement but it doesn't mean that we should not take great steps or important steps right now to start doing a better job of instrumenting our products and services so that we can start collecting data about our products and services because the path forward is going to demonstrate that we're going to be able to improve, dramatically improve the quality of the goods and services we sell by reducing the asset specificities for our customers by making them more intelligent and more programmable. Finally, is this going to be a feature of a differentiated business relationship through trust? We're open to that. Personally, I'll speak for myself, I think I will. I think that there is going to be an important element ultimately of being able to demonstrate to a customer base, to a marketplace that you take privacy, data ownership and intellectual property control of data assets seriously and that you are very, very specific, very transparent in how you're going to use those in derivative business transactions. All right, so once again, David Floyer. Thank you very much here in the studio on the phone, Neil Raiden, Ralph Finos, Jim Kabilis and George Gilbert. This has been another Wikibon Action Item.