live from Boston it's the cube covering IBM chief data officer summit brought to you by IBM welcome back everyone to the cubes live coverage of the IBM CDO summit here in Boston Massachusetts I'm your host Rebecca Knight and I'm joined by co-host Paul Gillan we have a guest today John Thomas he is the distinguished engineer and director at IBM thank you so much for coming returning to the cube you're a cube veteran so tell our viewers a little bit about your distinguished engineer there are only 672 in all of IBM what do you do what is your role that's a good question distinguished engineer is kind of a technical execute a role which is a combination of applying the technology skills as well as helping shape by the inscriber gene in a technical way working with clients etcetera right so it is it is a bit of a jack-of-all-trades but also deep skills in some specific areas and I love what I do so you get to work with some very talented people brilliant people in terms of shaping IBM technology and strategy products for energy that is part of it we also work very closely with clients in terms of how do you apply that technology in the context of the clients use cases we've heard a lot today about soft skills the importance of organizational people skills to being a successful chief data officer but there's still a technical component how important is the technical side what is what are the technical skills that the cdos need oh this is a very good question Paul so absolutely so navigating the organizational structure is important it's a soft skill you're absolutely right and being able to understand the business strategy for the company and then aligning your data strategy to the business strategy is important right but the underlying technical pieces need to be solid so for example how do you deal with large volumes of different types of data spread across the company how do you manage the data how do you understand the data how do you govern that data how do you then mast are leveraging the value of the data in the context of your business right so and understand deep understanding of the technology of collecting organizing and analyzing that data is needed for you to be a success for CBL so in terms of in terms of those skill sets that you're looking for and one of the things that Interpol said earlier in his keynote is that they're just it's a rare individual who truly understands the idea of how to collect store analyze curate eyes monetize the data and then also has the the soft skills of being able to navigate the organization being able to be a change agent is inspiring yeah inspiring the rank-and-file yeah how do you recruit and retain talent it seems to be a major tech expertise is not getting the right expertise in place and Interpol talked about it in his keynote which was the very first thing he did was bring in Terrence sometimes it is from outside of your company maybe you have a kind of talent that has grown up in your company maybe you have to go outside buddy God bring in the right skills together form the team that understands the technology and the business side of things and build esteem and that is essential for you to be a successful CTO and to some extent that's what Interpol has done that's what the analytic CEOs office has done a set up in my boss is the analytics EDF and he and the analytic CDO team actually engineering skills data science skills visualization skills and then put this team together which understands the how to collect govern curate and analyze the data and then apply them in specific situations a lot of talk about AI at this conference what seems to be finally happening what do you see in the field or perhaps projects that you've worked on examples of AI that are really having a meaningful business impact yeah Paul it's a very good question because you know the term AI is overused a lot as you can imagine a lot of hype around it but I think we are past that hype cycle and people are looking at how do i implement successful use cases and I stressed the word use case right in my experience these how I'm going to transform my business in one big boil the ocean exercise does not work but if you have a very specific bounded use case that you can identify the business tells you this is relevant the business tells you what the metrics for success are and then you focus your your attention your your efforts on that specific use case with the skills need for that use case then it's successful so you know examples of use cases from across the industries right I mean everything that you can think of customer-facing examples like how do I read the customers mind so when when if I'm a business and I interact with my customers can I anticipate what the customer is looking for maybe for a cross-sell opportunity or maybe to reduce the call handling time and a customer calls in to my call center or trying to segment my customer so I can do a proper promotion or a campaign for that customer all of these are specific customer facing examples there are also examples of applying this internally to improve processes capacity planning for your infrastructure can I predict when a system is likely to have an outage and or can I predict the traffic coming into my systems into my infrastructure and provision capacity that on-demand so all these are interesting applications of AI in the enterprise so when you're trying I mean one of the things we keep hearing is that we need data to tell a story the data needs to the data needs to be compelling enough so that the people the data scientists get it but then also that the other kinds of business decision makers get it - so what are sort of the best practices that have emerged from your experience in terms of being able to for your data to tell the story that you wanted to tell yeah well I mean if the pattern doesn't exist in the data then no amount of fancy algorithms can help you know so and sometimes it's like searching for a needle in a haystack but assuming I guess the first step is like I said what is the a use case once you have a clear understanding of your use case and success metrics for the use case do you have the data to support that use case so for example if it's fraud detection do you actually have the historical data to support the fraud use case sometimes you may have transactional data from your your transaction data from your current or PI systems but that may not be enough you may need to augment it with external data third party data may be unstructured data that goes along with the transaction data so question is can you identify the data that is needed to support the use case and if so can I do is that data clean is that is that data do you understand the lineage of the data who has touched and modified the data who owns the data so that I can then start building predictive models and machine learning be planning models with that data so use case do you have the data to support the use case do you understand how the data reached you then comes the process of applying machine learning algorithms and deep learning algorithms against that data one of the risks of machine learning and particularly deep learning I think is it becomes kind of a black box and people can fall into the trap of just believing what comes back regardless of whether the algorithms are really sound or the data is somewhat what is the responsibility of data scientists to sort of show their work yeah Paul this is a fascinating and not completely solved area right so bias detection can I explain how my model behaved can I ensure that the models are fair in their predictions so there's a lot of research lot of innovation happening in the space iBM is investing a lot in the space we call trust and transparency being able to explain a model it's got multiple levels to it you need some level of AI governments itself so just like we talked about data governance there is the notion of AI governance which is what version of the model was used to make a prediction what were the inputs that went into that model what were the decisions that are that what were the features that were used to make a certain prediction what was the prediction and how did that match up with ground truth you need to be able to capture all that information but beyond that we have got actual mechanisms in place that IBM Research is developing to look at bias detection so pre-processing during execution post-processing can I look for bias in how my models behave and do I have mechanisms to mitigate that so one example is the open source Python library called AI F 360 that comes from IBM's research on its contributor to the open source community you can look at there are mechanisms to look at bias and and and provide some level of bias mitigation as part of your model building exercises and is the bias mitigation does it have to do with and I'm gonna use an IBM term of art here at the human in the loop I mean is how much are you actually looking at the humans that are part of this process humans are at least at this point in time humans are very much in the loop this this notion of P or AI where humans are completely outside the loop is we're not there yet so very much something that the system can it provide a set of recommendations can it provide a set of explanations in can someone who understands the business look at it and make corrective take corrective action as needed there has been however to Rebecca's point some prominent people including Bill Gates who have have speculated that AI could ultimately be a negative for humans are what is the responsibility of companies like IBM to ensure that humans are kept in the loop I think at least at this point IBM's V was humans are an essential part of AI in fact we don't even use the term artificial intelligence that much we call it augmented intelligence where the system is presenting a set of recommendations expert advice to the human who can then make a decision so for example you know my team worked with a prominent healthcare provider on you know models for predicting patient death death in in the case of sepsis sepsis onset this is we're talking literally life and death decisions being made and this is not something that you can just automate and throw it into a magic black box and have a decision be made right so this is absolutely a place where people with deep domain knowledge are supported are augmented with with AI to make better decisions that's where that's where I think we are today as to what will happen five years from now I can't predict that yet the role so you are helping doctors make these decisions not just this is what the computer program says about this patients symptoms here but this is really you're helping the doctor make better decisions what about the doctors gut and the ease into his or her intuition too I mean what is what is the role of that in the future I think it goes away I mean I think the intuition really will be trumped by data in the long term because you can't argue with the facts much as some some people do these days the perspective on that is there will there all should there always be a human on the front lines who is being supported by the backend or would would you see a scenario where an AI is making decisions customer-facing decisions that are really are life and death so I think in the consumer industry I can definitely see AI making decisions on its own right so you know if let's say a recommender system which says you know I think you know John Thomas bought these last five things online he's likely to buy this other thing let's make an offer team you know I don't even in the loop for no harm it's it's it's it's pretty straightforward it's already happening in a big way but when it comes to some of these mortgage yeah about that one even that I think can be can be automated can be automated if the thresholds are said to be what the business is comfortable with where it says okay about this probability level I don't really need a human to look at this but and if it is below this level I do want someone to look at this that's you know that is relatively straightforward right but if it is a decision about you know life-or-death situations or something that affects the the very fabric of the business that you are in then you probably want to domain expert to look at it and most enterprises enterprise use cases will for lean towards that category these are big questions they're hard questions are questions yes well John thank you so much oh absolutely thank you we've really had a great time with you yeah thank you for having me I'm Rebecca night for Paul Gillen we will have more from the cubes live coverage of IBM CDO here in Boston just after this