from the cube studios in Palo Alto in Boston connecting with thought leaders all around the world this is a cube conversation hi buddy welcome back to the cube this is Dave Volante and you're watching a special presentation data ops enacted made possible by IBM you know what's what's happening is the innovation engine in the IT economy is really shifted used to be Moore's Law today it's applying machine intelligence and AI to data really scaling that and operationalizing that new knowledge the challenges that is not so easy to operationalize AI and infuse it into the data pipeline but what we're doing in this program is bringing in practitioners who have actually had a great deal of success in doing just that and I'm really excited to have it Kumal a Himalayan Manali is here she's the executive head of data management or personal and business banking at Standard Bank of South Africa the tomb of length thanks so much for coming in the queue thank you for having me Dave you're very welcome and first of all how you holding up with this this bovid situation how are things in Johannesburg um things in Johannesburg are fine we've been on lockdown now I think it's day 33 if I'm not mistaken lost count and but we're really grateful for the swift action of government we we only I mean we have less than 4,000 places in the country and infection rate is is really slow so we've really I think been able to find the curve and we're grateful for being able to be protected in this way so all working from home or learning the new normal and we're all in this together that's great to hear why don't you tell us a little bit about your your role you're a data person we're really going to get into it but here with us you know how you spend your time okay well I head up a date operations function and a data management function which really is the foundational part of the data value chain that then allows other parts of the organization to monetize data and liberate it as as as the use cases apply we monetize it ourselves as well but really we're an enterprise wide organization that ensures that data quality is managed data is governed that we have the effective practices applied to the entire lineage of the data ownership and curation is in place and everything else from a regulatory as well as opportunity perspective then is able to be leveraged upon so historically you know data has been viewed as sort of this expense it's it's big it's growing it needs to be managed deleted after a certain amount of time and then you know ten years ago of the Big Data move data became an asset you had a lot of shadow I people going off and doing things that maybe didn't comply to the corporate ethics probably drove here here you're a part of the organization crazy but talk about that how what has changed but they in the last you know five years or so just in terms of how people approach data oh I mean you know the story I tell my colleague who are all bankers obviously is the fact that the banker in 1989 had to mainly just know debits credits and be able to look someone in the eye and know whether or not they'd be a credit risk or not you know if we lend you money and you pay it back the the banker of the late 90s had to then contend with the emergence of technologies that made their lives easier and allowed for automation and processes to run much more smoothly um in the early two-thousands I would say that digitization was a big focus and in fact my previous role was head of digital banking and at the time we thought digital was the panacea it is the be-all and end-all it's the thing that's gonna make organizations edit lo and behold we realized that once you've gotten all your digital platforms ready they are just the plate or the pipe and nothing is flowing through it and there's no food on the face if data is not the main photo really um it's always been an asset I think organizations just never consciously knew that data was that okay so so what sounds like once you've made that sort of initial digital transformation you really had to work it and what we're hearing from a lot of practitioners like self as challenges related to that involve different parts of the organization different skill sets of challenges and sort of getting everybody to work together on the same page it's better but maybe you could take us back to sort of when you started on this initiative around data Ops what was that like what were some of the challenges that you faced and how'd you get through them okay first and foremost Dave organizations used to believe that data was I t's problem and that's probably why you you then saw the emergence of things like chatter IP but when you really acknowledge that data is an essay just like money is an asset then you you have to then take accountability for it just the same way as you would any other asset in the organization and you will not abdicate its management to a separate function that's not cold to the business and oftentimes IT are seen as a support or an enabling but not quite the main show in most organizations right so what we we then did is first emphasize that data is a business capability the business function it presides in business makes to product management makes to marketing makes to everything else that the business needs for data management also has to be for to every role in every function to different degrees and varying bearing offense and when you take accountability as an owner of a business unit you also take accountability for the data in the systems that support the business unit for us that was the first picture um and convincing my colleagues that data was their problem and not something that we had to worry about they just kind of leave us to to it was was also a journey but that was kind of the first step into it in terms of getting the data operations journey going um you had to first acknowledge please carry on no you just had to first acknowledge that it's something you must take accountability of as a banker not just need to a different part of the organization that's a real cultural mindset you know in the game of rock-paper-scissors you know culture kinda beats everything doesn't it it's almost like a yep a trump card and so so the businesses embrace that but but what did you do to support that is there has to be trust in the data that it has to be a timeliness and so maybe you could take us through how you achieve those objectives and maybe some other objectives that business the man so the one thing I didn't mention Dave is that obviously they didn't embrace it in the beginning it wasn't a it wasn't there oh yeah that make sense they do that type of conversation um what what he had was a few very strategic people with the right mindset that I could partner with that understood the case for data management and while we had that as as an in we developed a framework for a fully matured data operations capability in the organization and what that would look like in a target date scenario and then what you do is you wait for a good crisis so we had a little bit of a challenge in that our local regulator found us a little bit wanting in terms of our date of college and from that perspective it then brought the case for data quality management so now there's a burning platform you have an appetite for people to partner with you and say okay we need this to comply to help us out and when they start seeing their opt-in action do they then buy into into the concept so sometimes you need to just wait for a good Christ and leverage it and only do that which the organization will appreciate at that time you don't have to go Big Bang data quality management was the use case at the time five years ago so we focused all our energy on that and after that it gave us leeway and license really bring to maturity all the other capabilities at the business might not well understand as well so when that crisis hit of thinking about people process in technology you probably had to turn some knobs in each of those areas can you talk about that so from a technology perspective that that's when we partnered with with IBM to implement information analyzer for us in terms of making sure that then we could profile the data effectively what was important for us is to to make strides in terms of showing the organization progress but also being able to give them access to self-service tools that will give them insight into their data from a technology perspective that was kind of I think the the genesis of of us implementing and the IBM suite in earnest from a data management perspective people wise we really then also began a data stewardship journey in which we implemented business unit stewards of data I don't like using the word steward because in my organization it's taken lightly almost like a part-time occupation so we converted them we call them data managers and and the analogy I would give is every department with a P&L any department worth its salt has a FDA or financial director and if money is important to you you have somebody helping you take accountability and execute on your responsibilities in managing that that money so if data is equally important as an asset you will have a leader a manager helping you execute on your data ownership accountability and that was the people journey so firstly I had kind of soldiers planted in each department which were data managers that would then continue building the culture maturing the data practices as as applicable to each business unit use cases so what was important is that every manager in every business unit to the Data Manager focus their energy on making that business unit happy by ensuring that they data was of the right compliance level and the right quality the right best practices from a process and management perspective and was governed and then in terms of process really it's about spreading through the entire ecosystem data management as a practice and can be quite lonely um in the sense that unless the whole business of an organization is managing data they worried about doing what they do to make money and most people in most business units will be the only unicorn relative to everybody else who does what they do and so for us it was important to have a community of practice a process where all the data managers across business as well as the technology parts and the specialists who were data management professionals coming together and making sure that we we work together on on specific you say so I wonder if I can ask you so the the industry sort of likes to market this notion of of DevOps applied to data and data op have you applied that type of mindset approach agile of continuous improvement is I'm trying to understand how much is marketing and how much actually applicable in the real world can you share well you know when I was reflecting on this before this interview I realized that our very first use case of data officers probably when we implemented information analyzer in our business unit simply because it was the first time that IT and business as well as data professionals came together to spec the use case and then we would literally in an agile fashion with a multidisciplinary team come together to make sure that we got the outcomes that we required I mean for you to to firstly get a data quality management paradigm where we moved from 6% quality at some point from our client data now we're sitting at 99 percent and that 1% literally is just the timing issue to get from from 6 to 99 you have to make sure that the entire value chain is engaged so our business partners will the fundamental determinant of the business rules apply in terms of what does quality mean what are the criteria of quality and then what we do is translate that into what we put in the catalog and ensure that the profiling rules that we run are against those business rules that were defined at first so you'd have upfront determination of the outcome with business and then the team would go into an agile cycle of maybe two-week sprints where we develop certain things have stand-ups come together and then the output would be - boarded in a prototype in a fashion where business then gets to go double check that out so that was the first iterate and I would say we've become much more mature at it and we've got many more use cases now and there's actually one that it's quite exciting that we we recently achieved over the end of of 2019 into the beginning of this year so what we did was they I'm worried about the sunlight I mean through the window you look creative to me like sunset in South Africa we've been on the we've been on CubeSat sometimes it's so bright we have to put on sunglasses but so the most recent one which was in in mates 2019 coming in too early this year we we had long kind of achieved the the compliance and regulatory burning platform issues and now we are in a place of I think opportunity and luxury where we can now find use cases that are pertinent to business execution and business productivity um the one that comes to mind is we're a hundred and fifty eight years old as an organization right so so this Bank was born before technology it was also born in the days of light no no no integration because every branch was a standalone entity you'd have these big ledges that transactions were documented in and I think once every six months or so these Ledger's would be taken by horse-drawn carriage to a central place to get go reconcile between branches and paper but the point is if that is your legacy the initial kind of ERP implementations would have been focused on process efficiency based on old ways of accounting for transactions and allocating information so it was not optimized for the 21st century our architecture had has had huge legacy burden on it and so going into a place where you can be agile with data is something that we constantly working toward so we get to a place where we have hundreds of branches across the country and all of them obviously telling to client servicing clients as usual and and not being able for any person needing sales teams or executional teams they were not able in a short space of time to see the impact of the tactic from a database fee from a reporting history and we were in a place where in some cases based on how our Ledger's roll up and the reconciliation between various systems and accounts work it would take you six weeks to verify whether your technique were effective or not because to actually see the revenue hitting our our general ledger and our balance sheet might take that long that is an ineffective way to operate in a such a competitive environment so what you had our frontline sales agents literally manually documenting the sales that they had made but not being able to verify whether that or not is bringing revenue until six weeks later so what we did then is we sat down and defined all the requirements were reporting perspective and the objective was moved from six weeks latency to 24 hours um and even 24 hours is not perfect our ideal would be that bite rows of day you're able to see what you've done for that day but that's the next the next epoch that will go through however um we literally had the frontline teams defining what they'd want to see in a dashboard the business teams defining what the business rules behind the quality and the definitions would be and then we had an entire I'm analytics team and the data management team working around sourcing the data optimising and curating it and making sure that the latency had done that's I think only our latest use case for data art um and now we're in a place where people can look at a dashboard it's a cubed self-service they can learn at any time I see the sales they've made which is very important right now at the time of covert nineteen from a form of productivity and executional competitiveness those are two great use cases of women lying so the first one you know going from data quality 6% the 99% I mean 6% is all you do is spend time arguing about the data bills profanity and then 99% you're there and you said it's just basically a timing issue use latency in the timing and then the second one is is instead of paving the cow path with an outdated you know ledger Barret data process week you've now compressed that down to 24 hours you want to get the end of day so you've built in the agility into your data pipeline I'm going to ask you then so when gdpr hit were you able to very quickly leverage this capability and and apply and then maybe other of compliance edik as well well actually you know what we just now was post TDP our us um and and we got GDP all right about three years ago but literally all we got right was reporting for risk and compliance purposes they use cases that we have now are really around business opportunity lists so the risk so we prioritize compliance report a long time it but we're able to do real-time reporting from a single transaction perspective I'm suspicious transactions etc I'm two hours in Bank and our governor so from that perspective that was what was prioritize in the beginning which was the initial crisis so what you found is an entire engine geared towards making sure that data quality was correct for reporting and regulatory purposes but really that is not the be-all and end-all of it and if that's all we did I believe we really would not have succeeded or could have stayed dead we succeeded because Dana monetization is actually the penis' t the leveraging of data for business opportunity is is actually then what tells you whether you've got the right culture or not you're just doing it to comply then it means the hearts and minds of the rest of the business still aren't in the data game I love this story because it's me it's nirvana for so many years we've been pouring money to mitigate risk and you have no choice do it you know the general council signs off on it the the CFO but grudgingly signs off on it but it's got to be done but for years decades we've been waiting to use these these risk initiatives to actually drive business value you know it kind of happened with enterprise data warehouse but it was too slow it was complicated and it certainly didn't happen with with email archiving that was just sort of a tech balk it sounds like you know we're at that point today and I want to ask you I mean like you know you we talking earlier about you know the crisis gonna perpetuated this this cultural shift and you took advantage of that so we're out who we the the mother nature dealt up a crisis like we've never seen before how do you see your data infrastructure your data pipeline your data ops what kind of opportunities do you see in front of you today as a result of ovid 19 well I mean because of of the quality of kind data that we had now we were able to very quickly respond to to pivot nineteen in in our context where the government put us on lockdown relatively early in in the curve or in the cycle of infection and what it meant is it brought a little bit of a shock to the economy because small businesses all of a sudden didn't have a source of revenue or potentially three to six weeks and based on the data quality work that we did before it was actually relatively easy to be agile enough to do the things that we did so within the first weekend of of lockdown in South Africa we were the first bank to proactively and automatically offer small businesses and student and students with loans on our books a instant three month payment holiday assuming they were in good standing and we did that upfront though it was actually an opt-out process rather than you had to fall in and arrange for that to happen and I don't believe we would have been able to do that if our data quality was not with um we have since made many more initiatives to try and keep the economy going to try and keep our clients in in a state of of liquidity and so you know data quality at that point and that Dharma is critical to knowing who you're talking to who needs what and in which solutions would best be fitted towards various segments I think the second component is um you know working from home now brings an entirely different normal right so so if we had not been able to provide productivity dashboard and and and sales and dashboards to to management and all all the users that require it we would not be able to then validate or say what our productivity levels are now that people are working from home I mean we still have essential services workers that physically go into work but a lot of our relationship bankers are operating from home and that face the baseline and the foundation that we said productivity packing for various methods being able to be reported on in a short space of time has been really beneficial the next opportunity for us is we've been really good at doing this for the normal operational and front line and type of workers but knowledge workers have also know not necessarily been big productivity reporters historically they kind of get an output then the output might be six weeks down the line um but in a place where teams now are not locate co-located and work needs to flow in an edge of passion we need to start using the same foundation and and and data pipeline that we've laid down as a foundation for the reporting of knowledge work and agile team type of metric so in terms of developing new functionality and solutions there's a flow in a multidisciplinary team and how do those solutions get architected in a way where data assists in the flow of information so solutions can be optimally developed well it sounds like you're able to map a metric but business lines care about you know into these dashboards you usually the sort of data mapping approach if you will which makes it much more relevant for the business as you said before they own the data that's got to be a huge business benefit just in terms of again we talked about cultural we talked about speed but but the business impact of being able to do that it has to be pretty substantial it really really is um and and the use cases really are endless because every department finds their own opportunity to utilize in terms of their also I think the accountability factor has has significantly increased because as the owner of a specific domain of data you know that you're not only accountable to yourself and your own operation but people downstream to you as a product and in an outcome depend on you to ensure that the quality of the data you produces is of a high nature so so curation of data is a very important thing and business is really starting to understand that so you know the cards Department knows that they are the owners of card data right and you know the vehicle asset Department knows that they are the owners of vehicle they are linked to a client profile and all of that creates an ecosystem around the plan I mean when you come to a bank you you don't want to be known as a number and you don't want to be known just for one product you want to be known across everything that you do with that with that organization but most banks are not structured that way they still are product houses and product systems on which your data reside and if those don't act in concert then we come across extremely schizophrenic as if we don't know our clients and so that's very very important stupid like I can go on for an hour talking about this topic but unfortunately we're we're out of time thank you so much for sharing your deep knowledge and your story it's really an inspiring one and congratulations on all your success and I guess I'll leave it with you know what's next you gave us you know a glimpse of some of the things you wanted to do pressing some of the the elapsed times and the time cycle but but where do you see this going in the next you know kind of mid term and longer term currently I mean obviously AI is is a big is a big opportunity for all organizations and and you don't get automation of anything right if the foundations are not in place so you believe that this is a great foundation for anything AI to be applied in terms of the use cases that we can find the second one is really providing an API economy where certain data product can be shared with third parties I think that probably where we want to take things as well we are really utilizing external third-party data sources I'm in our data quality management suite to ensure validity of client identity and and and residents and things of that nature but going forward because been picked and banks and other organizations are probably going to partner to to be more competitive going forward we need to be able to provide data product that can then be leveraged by external parties and vice-versa to be like thanks again great having you thank you very much Dave appreciate the opportunity thank you for watching everybody that we go we are digging in the data ops we've got practitioners we've got influencers we've got experts we're going in the crowd chat it's the crowd chat net flash data ops but keep it right there way back but more coverage this is Dave Volante for the cube [Music] you

from the cube studios in Palo Alto in Boston connecting with thought leaders all around the world this is a cute conversation everybody welcome back to the cube this is Dave Volante and you're watching a special presentation data ops enacted made possible by IBM you know what's what's happening is the innovation engine in the IT economy is really shifted used to be Moore's Law today it's applying machine intelligence and AI to data really scaling that and operationalizing that new knowledge the challenges that is it's not so easy to operationalize AI and infuse it into the data pipeline but what we're doing in this program is bringing in practitioners who have actually had a great deal of success in doing just that and I'm really excited to have it Kumal a the tumor lang Manali is here she's the executive head of data management or personal and business banking at Standard Bank of South Africa the tumor length thanks so much for coming in the cube thank you for having me Dave you're very welcome and first of all how you holding up with this this bovid situation how are things in Johannesburg um things in Johannesburg are fine and we've been on lockdown now I think it's day 33 if I'm not mistaken lost count and but we're really grateful for the swift action of government we only I mean we have less than 4,000 places in the country and infection rate is is really slow so we've really I think been able to flatten the curve and we're grateful for being able to be protected in this way so we're all working from home or learning the new normal and we're all in this together that's great to hear why don't you tell us a little bit about your your role you're a data person we're really going to get in with here with us you know how you spend your time okay well I hit up a date operations function in a data management function which really is the foundational part of the data value chain that then allows other parts of the organization to monetize data and leverage it as as the use cases apply we monetize it ourselves as well but really we're an enterprise wide organization that ensures that data quality is managed data is governed that we have the effective practices applied to the entire lineage of the data ownership and curation is in place and everything else from a regulatory as well as opportunity perspective then is able to be leveraged upon so historically you know data has been viewed as sort of this expense it's it's big it's growing it needs to be managed deleted after a certain amount of time and then you know ten years ago the Big Data move data became an asset you had a lot of shadow ID people going off and doing things that maybe didn't comply to the corporate ethics probably drove here here you're a part of the organization crazy but talk about that how what has changed but they in the last you know five years or so just in terms of how people approach data oh I mean you know the story I tell my colleague who are all bankers obviously is the fact that um the banker in 1989 had to mainly just know debits credit and be able to look someone in the eye and know whether or not they'd be a credit risk or not you know if we lend you money and you pay it back um the the banker of the late 90s had to then contend with the emergence of technologies that made their lives easier and allowed for automation and processes to run much more smoothly um in the early two-thousands I would say that digitization was a big focus and in fact my previous role was head of digital banking and at the time we thought digital was the panacea it is the be-all and end-all is the thing that's gonna make organizations edit lo and behold we realized that once you've gotten all your digital platforms ready they are just the plate or the pipe and nothing is flowing through it and there's no food on the plate if data is not the main so really um it's always been an acid I think organizations just never consciously knew that data was there okay so so it sounds like once you've made that sort of initial digital transformation you really had to work it and what we're hearing from a lot of practitioners like toughest challenges related to that involve different parts of the organization different skill sets of challenges and sort of getting everybody to work together on the same page it's better but maybe you could take us back to sort of when you started on this initiative around data ops what was that like what were some of the challenges that you faced and how'd you get through them first and foremost Dave organizations used to believe that data was I t's problem and that's probably why you you then saw the emergence of things like shadow IP but when you really acknowledge that data is and si just like money is an asset then you you have to then take accountability for it just the same way as you would any other asset in the organization and you will not add the a its management to a separate function that's not code to the business and oftentimes IT are seen as a support for an enabling but not quite the main show in most organizations right so what we we then did is first emphasize that data is a business capability a business function it presides in business next to product management next to marketing makes to everything else that the business needs for data management also has to be for to every role in every function to different degrees and varying bearing events and when you take accountability as an owner of a business unit you also take accountability for the data in the systems that support the business unit for us that was the first picture um and convincing my colleagues that data was their problem and not something that we had to worry about and they just kind of leave us to - it was was also a journey but that was kind of the first step in - in terms of getting the data operations journey going um you had to first acknowledge please carry on no you just had to first acknowledge that it's something you must take accountability of as a banker not just need to a different part of the organization that's a real cultural mindset you know in the game of rock-paper-scissors you know culture kinda beats everything doesn't it it's almost like a yep a trump card and so so the businesses embrace that but but what did you do to support that is there has to be trust in the data that it has to be a timeliness and so maybe you could pick us through how you achieve those objectives and maybe some other objectives that business the man so the one thing I didn't mention Davis that obviously they didn't embrace it in the beginning it wasn't a it wasn't there oh yeah that make sense they do that type of conversation um what what he had was a few very strategic people with the right mindset that I could partner with that understood the case for data management and while we had that as as an in we developed a framework for a fully matured data operations capability in the organization and what that would look like in a target date scenario and then what you do is you wait for a good crisis so we had a little bit of a challenge in that our local regulator found us a little bit wanting in terms of our data quality and from that perspective it then brought the case for data quality management to the whole so now there's a burning platform you have an appetite for people to partner with you and say okay we need this to comply to help us out and when they start seeing their opt-in action do they stick then buy into into the concepts so sometimes you need to just wait for a good price and leverage it and only do that which the organization will appreciate at that time you don't have to go Big Bang data quality management was the use case at the time five years ago so we focused all our energy on that and after that it gave us leeway and license really bring to maturity or the other capabilities of the business might not well understand as well so when that crisis hit of thinking about people process in technology you probably had to turn some knobs in each of those areas can you talk about that so from a technology perspective that that when we partnered with with IBM to implement information analyzer for us in terms of making sure that then we could profile the data effectively what was important for us is to to make strides in terms of showing the organization progress but also being able to give them access to self-service tools that will give them insight into their data from a technology perspective that was kind of I think that the genesis of of us implementing and the IBM suite in earnest from a data management perspective people wise we really then um also began a data stewardship journey in which we implemented business unit stewards of data I don't like using the word steward because in my organization it's taken lightly it's almost like a part-time occupation so we converted them we call them data managers and and the analogy I would give is every department with a pl any department worth its salt has a FD or financial director and if money is important to you you have somebody helping you take accountability and execute on your responsibilities and managing that that money so if data is equally important as an asset you will have a leader a manager helping you execute on your data ownership accountability and that was the people journey so firstly I had kind of soldiers planted in each department which were data managers that would then continue building the culture maturing the data practices as as applicable to each business unit use cases so what was important is that every manager in every business unit to the Data Manager focus their energy on making that business unit happy by ensuring that their data was of the right compliance level and the right quality the right best practices from a process and management perspective and was governed through and then in terms of process really it's about spreading through the entire ecosystem data management as a practice and can be quite lonely in the sense that unless the core business of an organization is managing data they worried about doing what they do to make money and most people in most business units will be the only unicorn relative to everybody else who does what they do and so for us it was important to have a community of practice a process where all the data managers across business as well as the technology parts and the specialists who were data management professionals coming together and making sure that we we work together on on specific use so I wonder if I can ask you so the the industry sort of likes to market this notion of of DevOps applied to data and data op have you applied that type of mindset approach agile of continuous improvement is I'm trying to understand how much is marketing and how much actually applicable in the real world can you share well you know when I was reflecting on this before this interview I realized that our very first use case of data officers probably when we implemented information analyzer in our business unit simply because it was the first time that IT and business as well as data professionals came together to spec the use case and then we would literally in an agile fashion with a multidisciplinary team come together to make sure that we got the outcomes that we required I mean for you to to firstly get a data quality management paradigm where we moved from 6% quality at some point from our client data now we're sitting at 99 percent and that 1% literally is just the timing issue to get from from 6 to 99 you have to make sure that the entire value chain is engaged so our business partners were the fundamental determinant of the business rules apply in terms of what does quality mean what are the criteria of quality and then what we do is translate that into what we put in the catalog and ensure that the profiling rules that we run are against those business rules that were defined at first so you'd have upfront determination of the outcome with business and then the team would go into an agile cycle of maybe two-week sprints where we develop certain things have stand-ups come together and then the output would be - boarded in a prototype in a fashion where business then gets to go double check that out so that was the first iterate and I would say we've become much more mature at it and we've got many more use cases now and there's actually one that it's quite exciting that we we recently achieved over the end of 2019 into the beginning of this year so what we did was they've am worried about the sunlight coming through the window you look crazy to me like the sunset in South Africa we've been on the we've been on CubeSat sometimes it's so bright we have to put on sunglasses but so the most recent one which was in in late 2019 coming in too early this year we we had long kind of achieved the the compliance and the regulatory burning platform issues and now we are in a place of I think opportunity and luxury where we can now find use cases that are pertinent to business execution and business productivity the one that comes to mind is where a hundred and fifty eight years old as an organization right so so this Bank was born before technology it was also born in the days of light no no no integration because every branch was a standalone entity you'd have these big ledges that transactions were were documented in and I think once every six months or so these Ledger's would be taken by horse-drawn carriage to a central place to give go reconcile between branches and paper but the point is if that is your legacy the initial kind of ERP implementations would have been focused on process efficiency based on old ways of accounting for transactions and allocating information so it was not optimized for the 21st century our architecture had has had huge legacy burden on it and so going into a place where you can be agile with data is something that we're constantly working toward so we get to a place where we have hundreds of branches across the country and all of them obviously telling to client servicing clients as usual and and not being able for any person needing sales teams or executional teams they were not able in a short space of time to see the impact of the tactic from a data perspective um we were in a place where in some cases based on how our Ledger's roll up in the reconciliation between various systems and accounts work it would take you six weeks to verify whether your technique were effective or not because to actually see the revenue hitting our our general ledger and our balance sheet might take that long that is an ineffective way to operate in a such a competitive environment so what you had our frontline sales agents literally manually documenting the sales that they had made but not being able to verify whether that or not is bringing revenue until six weeks later so what we did then is we sat down and defined all the requirements from a reporting perspective and the objective was moved from six weeks latency to 24 hours um and even 24 hours is not perfect our ideal would be that bite rows of day you're able to see what you've done for that day but that's the next the next epoch that will go through however um we literally had the frontline teams defining what they'd want to see in a dashboard the business teams defining what the business rules behind the quality and the definitions would be and then we had an entire I'm analytics team and the data management team working around sourcing the data optimising and curating it and making sure that the latency had done that's I think only our latest use case for data art um and now we're in a place where people can look at a dashboard it's a cubed self-service they can Logan at any time I see the sales they've made which is very important right now and the time of overt nineteen from a from a productivity and executional competitiveness listing those are two great use cases of cooling so the first one you know going from data quality 6% the 99% I mean 6% is all you do is spend time arguing about the data stills probity and then 99% you're there and you said it's just basically a timing issue use latency in the timing and then the second one is is instead of paving the cow path with an outdated you know ledger Barratt data process week you've now compressed that down to 24 hours you want to get the end of day so you've built in the agility into your data pipeline I'm gonna ask you then so when GDP are hit were you able to very quickly leverage this capability and and imply and then maybe other of compliance edik as well Oh actually you know what we just now was post gdpr us um and and we got GDP all right about three years ago but literally all we got right was reporting for risk and compliance purposes the use cases that we have now are really around business opportunity lists so the risk so we prioritize compliance report a long time ago were able to do real-time reporting of a single transaction perspective I'm suspicious transactions etc I'm two hours in Bank and our governor so from that perspective that was what was prioritize in the beginning which was the initial crisis so what you found is an entire engine geared towards making sure that data quality was correct for reporting and regulatory purposes but really that is not the be-all and end-all of it and if that's all we did I believe we really would not have succeeded or could have stayed dead we succeeded because data monetization is actually the penisy the leveraging of data for business opportunity is is actually then what tells you whether you've got the right culture or not you're just doing it to comply then it means the hearts and minds of the rest of the business still aren't in the data game I love this story because it's me it's nirvana for so many years we've been pouring money to mitigate risk and you have no choice do it you know the general council signs off on it the the CFO but grudgingly signs off on it but it's got to be done but for years decades we've been waiting to use these these risk initiatives to actually drive business value you know kind of happened with enterprise data warehouse but it was too slow it was complicated it certainly didn't happen with with email archiving that was just sort of a tech balk it sounds like you know we're at that point today and I want to ask you to me like you know you we talking earlier about you know the crisis gonna perpetuated this this cultural shift and you took advantage of that so we're on the mother nature dealt up a crisis like we've never seen before how do you see your data infrastructure your data pipeline your data ops what kind of opportunities do you see in front of you today as a result of mobit nineteen well I mean because of of the quality of mind data that we had now we were able to very quickly respond to to pivot nineteen in in our context where the government and put us on lockdown relatively early in in the curve in disciple of infection and what it meant is it brought a little bit of a shock to the economy because small businesses all of a sudden didn't have a source of revenue for potentially three to six weeks and based on the data quality work that we did before it was actually relatively easy to be agile enough to do the things that we did so within the first weekend of of lockdown in South Africa we were the first bank to proactively and automatically offer small businesses and student um students with loans on our books a instant preman payment holiday assuming they were in good standing and we did that upfront though it was actually an up out process rather than you had to fall in and arrange for that to happen and I don't believe we would have been able to do that if our data quality was not with um we have since made many more initiatives to try and keep the economy going to try and keep our clients in in a state of of liquidity and so you know data quality at that point and that Dharma is critical to knowing who you're talking to who needs what and in which solutions would best be fitted towards various segments I think the second component is um you know working from home now brings an entirely different normal right so so if we have not been able to provide productivity dashboard and and sales and dashboards to to management and all all the users that require it we would not be able to then validate or say what our productivity levels are and other people are working from home I mean we still have essential services workers that physically go into work but a lot of our relationship bankers are operating from home and that face the baseline and the foundation that we said productivity packing for various metric being able to be reported on in a short space of time has been really beneficial the next opportunity for us is we've been really good at doing this for the normal operational and front line and type of workers but knowledge workers have also know not necessarily been big productivity reporters historically they kind of get an output then the output might be six weeks down the line um but in a place where teams now are not locate co-located and work needs to flow in an edge of passion we need to start using the same foundation and and and data pipeline that we've laid down as a foundation for the reporting of knowledge work and agile team type of metric so in terms of developing new functionality and solutions there's a flow in a multidisciplinary team and how do those solutions get architected in a way where data assists in the flow of information so solutions can be optimally developed well it sounds like you're able to map a metric the business lines care about you know into these dashboards you using the sort of data mapping approach if you will which makes it much more relevant for the business as you said before they own the data that's got to be a huge business benefit just in terms of again we talked about cultural we talked about speed but but the business impact of being able to do that it has to be pretty substantial it really really is um and and the use cases really are endless because every department finds their own opportunity to utilize in terms of their also I think the accountability factor has has significantly increased because as the owner of a specific domain of data you know that you're not only accountable to yourself and your own operation but people downstream to you as a product and and an outcome depend on you to ensure that the quality of the data you produces is of a high nature so so curation of data is a very important thing and business is really starting to understand that so you know the cards Department knows that they are the owners of card data right and you know the vehicle asset Department knows that they are the owners of vehicle they are linked to a client profile and all of that creates an ecosystem around the plan I mean when you come to a bank you you don't want to be known as a number and you don't want to be known just for one product you want to be known across everything that you do with that with that organization but most banks are not structured that way they still are product houses and product systems on which your data reside and if those don't act in concert then we come across extremely schizophrenic as if we don't know our clients and so that's very very important to me like I could go on for an hour talking about this topic but unfortunately we're out of time thank you so much for sharing your deep knowledge and your story it's really an inspiring one and congratulations on all your success and I guess I'll leave it with you know what's next you gave us you know a glimpse of some of the things you wanted to do pressing some of the the elapsed times and the time cycle but but where do you see this going in the next you know kind of mid term and longer term currently I mean obviously AI is is a big is a big opportunity for all organizations and and you don't get automation of anything right if the foundations are not in place so you believe that this is a great foundation for anything AI to to be applied in terms of the use cases that we can find the second one is really um providing an API economy where certain data product can be shared with third parties I think that probably where we want to take things as well we are ready utilizing external third-party data sources I'm in our data quality management suite to ensure validity of client identity and and and residents and things of that nature but going forward because been picked and banks and other organizations are probably going to partner to to be more competitive going forward we need to be able to provide data product that can then be leveraged by external parties and vice-versa the trooper like thanks again great having you thank you very much Dave appreciate the opportunity and thank you for watching everybody that we go we are digging in the data offs we've got practitioners we've got influencers we've got experts we're going in the crowd chat it's the crowd chat dot net flash data ops but keep it right there way back but more coverage this is Dave Volante for the cube [Music]

>> narrator: From Las Vegas it's theCUBE covering IBM Think 2018. Brought to you by IBM. >> We're back at IBM Think 2018. My name is Dave Vellante. I'm here with Peter Burris, my co-host, and you're watching theCUBE, the leader in live tech coverage. Our day two of our wall to wall coverage of IBM Think. Madhu Kochar is here. She's the Vice President of Analytics, Product Development at IBM and she's joined by Pat Maqetuka >> Close enough. She's a data officer at Nedbank. Ladies, welcome to theCUBE. You have to say your last name for me. >> Patricia Maqetuka. >> Oh, you didn't click >> I did! >> Do it again. >> Maqetuka. >> Amazing. I wish I could speak that language. Well, welcome. >> Madhu & Pat: Thank you. >> Good to see you again. >> Madhu: Thank you. >> Let's start with IBM Think. New show for you guys you consolidated, you know, six big tent events into one. There's a lot of people, there's too many people, to count I've been joking. 30, 40 thousand people, we're not quite sure, but how's the event going for you? What are clients telling you? >> Yeah, no, I mean, to your point, yes, we brought in all three big pillars together; a lot of folks here. From data and analytics perspective, an amazing, amazing event for us. Highlights from yesterday with Arvind Krishna on our research. What's happening. You know, five for five, that was really inspiring for all of us. You know, looking into the future, and it's not all about technology, was all about how we are here to help protect the world and change the world. So that as a, as a gige, as an engineer, that was just so inspiring. And as I was talking to our clients, they walk away with IBM as really a solution provider and helping, so that was really good. I think today's, Ginni's, keynote was very inspiring, as well. From our clients, we got some of our key clients, you know, Nedbank is here with us, and we've been talking a lot about our future, our strategy. We just announced, Ginni actually announced, our new product, IBM Cloud Private for Data. Everything around data, you know, where we are really bringing the power of data and analytics all together on a private cloud. So that's a huge announcement for us, and we've been talking a lot with our clients and the strategies resonating and particularly, where I come from in terms of the co-ordinance and integration space, this is definitely becoming now the "wow" factor because it helps stitch the entire solutions together and provide, you know, better insights to the data. >> Pat from your perspective, you're coming from Johannesburg so you probably like the fact that there's all IBM in one, so you don't have to come back to three or four conferences every year, but love your perspectives on that, and can you please tell us about Nedbank and your role as Chief Data Officer. >> Nedbank is one of the big five financial banks in South Africa. I've been appointed as the CDO about 18 months ago, so it's a new role in the bank per say. However, we're going through tremendous transformation in the bank and especially our IT eco-system has been transformed because we need to keep up with what is happening in the IT world. >> Are you the banks first Chief Data Officer? >> Definitely yes. I'm the first. >> Okay. So, you're a pioneer. I have to ask you then, so where did you start when you took over as the Chief Data Officer? I mean banking is one of those industries that tends to be more Chief Data Officer oriented, but it's a new role, so where did you start? >> Well we are not necessary new in the data, per say. We have had traditional data warehousing functions in the organization with traditional warehousing or data roles in the organization. However, the Chief Data role was never existent in the bank and actual fact, the bank appointed two new roles 18 months ago. One of them was the Chief Digital Officer, who is my colleague, and myself being appointed as the Chief Data Officer. >> Interesting, okay. I talked to somebody from Northern Trust yesterday and she was the lead data person and she said, "I had to start with a mission. We had to find the mission first and then we looked at the team, and then we evolved into, how we contribute to the business, how we improve data quality, who has access, what skills we needed." Does that seem like a logical progression and did you take a similar path? >> I think every bank will look at it differently or every institution. However, from a Nedbank perspective, we were given the gift by the regulator in bringing the BCBS 239 compliancy into play, so what the bank then did, how do we leverage, not just being compliant, but leveraging the data to create competitive advantage, and to create new sources of revenue. >> Okay. Let's talk about, Madhu we talked about this in New York City, you know, governance, compliance, kind of an evil word to a lot of business people. Although, your contention was "Look, it's reality. You can actually turn it into a positive." So talk about that a little bit and then we can tie it into Nedbank's experiences. >> Yeah, so I firmly believe in, you know, in the past governance 1.0 was all about compliance and regulations, very critical, but that's all we drew. I believe now, it's all about governance 2.0, where it's not just the compliance, but how do I drive insights, you know, so data is so, so critical from that perspective, and driving insights quicker to your businesses, is going to be very important, so as we engage with Nedbank and other clients as well, they are turning that because they are incumbents. They know their data, they've got a lot of data, you know, some of, they know sitting in structure, law structure land, and it's really, really important that they quickly able to assess what's in it, classify it, right, and then quickly deliver the results to the businesses, which they're looking for, so we're, I believe in lieu of governance 2.0, and compliance and regulations are always going to be with us, and we're making, actually, a lot of improvements in our technology, introducing machine learning, how we can do these things faster and quicker. >> So one of the first modern pieces of work that Peter and I did was around data classification and that seems to be, I heard this theme before, it seems to be a component or a benefit of putting governance in place. That you can automate data classification and use it to affect policy, but Pat, from your standpoint, how do you approach governance, what are the business benefits beyond "we have to do this"? >> Like I earlier on alluded to, we took the regulation as a gift and said, "How do we turn this regulation into benefits for the organization?" So in looking at the regulation we then said, "How do we then structure the approach?" So we looked at the two prompts. The first was, the right to win. The right to win meaning that we are able to utilize the right to compete approach from a regulation perspective, to create a platform and a foundation for analytics for our organization. We also created the blueprint for our enterprise data program and in the blueprint, we also came up with key nine principles of what it means to stay true to our data. I.e. you mentioned classification, you mentioned data politic, you mentioned lineage. Those are the key aspects within our principles. The other key principle we also indicated was the issue around duplication. How do we ensure that we describe data once, we ingest it once, but we use it multiple times to answer different questions, and as you are aware, in analytics, the more you mine the data, the more inquisitive you become, so it is, (clears throat) Sorry. It's not been from data to information, information to insight, and eventually insights to foresight, so looking into the future, and now you bring it back into data. >> And also some points that you've made Pat, so the concept is, one of the challenges of using the fuel example, is that governance of fuel, is still governance of a thing. You can apply it here, you can apply it there, you can't apply it to both places. Data's different and you were very, very accurate when you said "We wanted to find it once, we want to ingest it once, we want to use it multiple times". That places a very different set of conditions on the types of governance and in many respects, in the past, other types of assets where there is this sense of scarcity, it is a problem, but one of the things that I'm, and this is a question, is the opportunity, you said the regulatory opportunity, is the opportunity, because data can be shared, should we start treating governance really as a way of thinking about how to generate value out of data, and not a way of writing down the constraints of how we use it. What do you think about that? >> I think you are quite right with that because the more you give the people the opportunity to go and explore, so you unleash empowerment, you unleash freedom for them to go and explore. They will not see governance as a stick like I initially indicated, but they see it as business as usual, so it will come natural. However, it doesn't happen overnight. People need to be matured, organization is to be matured. Now, the first step you have to do is to create those policies, create awareness around the policies, and make sure that the people who are utilizing the data are trained in to what are the do's and the don'ts. We are fully aware that cyber security's one of our biggest threats, so you can also not look at how you create security around your data. People knowing that how I use my data it is an asset of the bank and not an asset of an individual. >> I know you guys have to go across the street, but I wanted to get this in. You're a global analytics global elite client; I want to understand what the relationship is. I mean, IBM, why IBM, maybe make a few comments about your relationship with the company. >> I think we as Nedbank, we are privileged, actually, to be inculcated into this global elite program of IBM. That has helped me in actual, in advancing what we need to do from a data perspective because anytime I can pick up a phone to collaborate with the IBM MaaS, I can pick up the phone whenever I need support, I need guidance. I don't have to struggle alone because they've done it with all the other clients before, so why should I reinvent the wheel, whereas someone else has done it, so let me tap into that, so that that can progress quicker than try it first. >> Alright. Madhu, we'll give you the final word. On Think and your business and your priorities. >> So, Think is amazing, you know, the opportunity to meet with all our clients and coming from product development, talking about our strategy and getting that validation is just good, you know, sharing open road maps with clients like Nedbank and our other global elites, you know. It gives us an opportunity, not just sharing of the road maps, but actually a lot of co-creation, right, to take us into the future, so I'm having a blast. I got to go run over and meet a few other clients, but thank you for having us over here. It's a pleasure. >> You're very welcome and thank you so much for coming on and telling your story, Pat, and Madhu, always a pleasure to see you. >> Thank you. >> Alright, got to get in your high horse and go. Thanks for watching everybody, we'll be right back after this short break. You're watching theCUBE live from IBM Think 2018. We'll be right back. (electronic music)

>> Hey, welcome back, everybody. Jeff Frick here with theCUBE. It's International Women's Day 2018, there's stuff going on all around the world. We're up at the Accenture event at downtown San Fancisco. 400 people at the Hotel Nikko, lot of great panels, a lot of interesting conversations, a lot of good energy. Really about diversity and inclusion and not just cause it's the right thing to do, but it actually drives better business outcomes. Hm, how about that? So we're really excited to have our next guest, it's Vivienne Ming. She's a founder and chair of Socos Labs, Vivienne, welcome. >> It's a pleasure to be here. >> Yeah, so what is Socos Labs? >> So, Socos Labs is a think tank, it's my fifth company, because apparently, I can't seem to take a hint. And we are using artificial intelligence and neuroscience and economic theory to explore the future of what it means to be human. >> So who do you work with? Who are some of your clients? >> So we partner with enormous and wonderful groups around the world, for example, we're helping the Make A Wish Foundation help kids make better wishes, so we preserve what's meaningful to the child, but try and make it even more resonant with the community and the family that's around them. We've done wonderful work here with Accenture to look at what actually predicts the best career and life outcomes, and use that to actually help their employees. Not for Accenture's sake, but for the 425,000 people get to live better, richer lives. >> Right, right. That's interesting, cause that's really in line with that research that they released today, you know, what are these factors, I think they identified 40 that have a significant impact, and then a sub set of 14 within three buckets, it's very analytical, it's very center, it's great. >> I love numbers. I'm you know, by training, I'm a theoretical neuroscientist, which is a field where we study machine learning to better understand the brain, and we study the brain to come up with better machine learning. And then I started my first company in education, and to me, it's always about, not even just generating a bunch of numbers, but figuring out what actually makes a difference. What can you do? In education, in mental health, in inclusion, or just on the job, that will actually drive someone to a better life outcome. And one of those outcomes is they're more productive. >> Right, right. >> And they're more engaged on the job, more creative. You know, a big driver behind what I do is the incredible research on how many, it's called the Lost Einsteins Research. >> The Lost Einsteins. >> Lost Einsteins. >> So a famous economist, Raj Chetty at Stanford just released a new paper on this, showing that kids from high wealth backgrounds, are 10 times as likely as middle class peers to, for example, have patents or to have that big impact in people's lives. In our research, we find the same thing, but on the scales of orders of magnitude difference. What if every little kid in Oakland, or in Johannesburg, or in a rural village in India, had the same chances I had to invent and contribute. That's the world I want to live in. It's wonderful working with a group like Accenture, the Lego Foundation, the World Bank, that agree that that really matters. >> Right, it's just interesting, the democratization theme comes up over and over and over, and it's really not that complicated of a thing, right? If you give more people access to the data, more people access to the tools, it'd make it easier for them to manipulate the data, you're just going to get more innovation, right? It's not brain surgery. >> You get more people contributing to what we sometimes call the creative class, which you know, right now, probably is about 1.5% of the world population. Maybe 150, 200 million people, it sounds like a big number, but we're pushing eight billion. What would the world be like not if all of them, just imagine instead of 200 million people, it was 400. Or it was a billion people, what would the world be like if a billion people had the chance to really drive the good in our lives. So on my panel, I had the chance to throw out this line that I was quoted as saying once. "Ambitious men have been promising us rocket ships and AI, "and self-driving cars, "but if every little girl had been given the reins "to her own potential, we'd already have them". And we don't talk not just about every little girl, but every little kid. >> Right, right. >> That doesn't have the chance. You know, if even one percent of them had that chance, it would change the world. >> So you must be a happy camper in the world though, rendering today with all the massive compute, cloud delivery and compute and store it to anyone, I mean, all those resources asymptotically approaching zero cost and availability via cloud anywhere in this whole big data revolution, AI and machine learning. >> I love it. I mean, I wouldn't build AI, which that's, I'm a one trick pony in some sense. I do a lot of different work, but there's always machine learning under the hood for my companies. And my philanthropic work. But I think there is something as important as amazing a tool as it is, the connectivity, the automation, the artificial intelligence as a perhaps dominant tool of the future, is still just a tool. >> Jeff: Right. >> These are messy human problems, they will only ever have messy human solutions. But now, me as a scientist can say, "Here's a possible solution". And then me as an entrepreneur, or a philanthropist, can say, "Great. "Now with something like AI, we can actually share that "solution with everybody". >> Right. So give us a little bit of some surprise insights that came out of your panel, for which I was not able to attend, I was out here doing interviews. >> So you know, I would say the theme of our panel was about role modeling. >> So I was the weirdo outlier on the panel, so we had Oakland mayor Libby Schaaf, we had the CFO of the Warriors, Jennifer was great, and we talked about simply being visible, and doing the work that we do in AI, in sports, in politics. That alone changes people's lives, which is a well studied phenomenon. The number one predictor of a kid from an underrepresented population, taking a scholarship, you know, believing they can be successful in politics is someone from their neighborhood went before them and showed them that it was possible. >> And seeing somebody that looks like them in that role. >> And so seeing a CFO of the Warriors, one of the great sports teams in the world today... >> Right. >> Is you know, this little Filipino woman, to put it in the way I think other people would perceive her and realize no, she does the numbers, she drives the company, and it's not despite who she is, it's because she brought something unique to the table that no one else had, plus the smarts. >> Jeff: Right. >> And made a difference to see Libby Schaaf get up there, with a lot of controversy right now, in the bigger political context. >> Jeff: Yes, yes. >> And show that you can make a difference. When people marginalize you, when I went out and raised money for my first company, I had venture capitalists literally pat me on the head and treat me like a little girl, and what I learned very quickly is there are always going to be some one that's going to see the truth in what I can bring. Go find those people, work with them, and then show the rest of the world what's possible. >> Right. It's pretty interesting, Robin Matlock is a CMO at VMware, we do a lot of stuff with VMware, and they put in a women in tech lunch thing a couple years ago, and we were talking, and I was interviewing her, she said, you know, I'd never really took the time to think about it. I was just working my tail off, and doing my thing, and you know, suddenly here I am, I'm CMO of this great company, and then it kind of took her a minute, and somebody kind of said, wait, you need to either take advantage of that opportunity in that platform to help others that maybe weren't quite so driven or are looking for those role models to say, "She looks kind of like me, "maybe I want to be the CMO of a big tech company". >> Well part of what's amazing you know, I get to work in education and work force, and part of what's amazing, whether you're talking about parents or the C Suite, or politicians is... A lot of that role modeling comes just from you being you. Go out, do good work in the world. But for some people, you know, there's an opportunity that doesn't exist for a lot of others. I'm a real outlier. I was not born a woman. I went through gender transition, it was a long time ago, and so for most people like me, being open about who you are means losing your job, it means not being taken seriously in any way, I mean, the change over the last couple of years has been astonishing. >> Jeff: It's been crazy, right? >> But part of my life is being able to be that person. I can take it. You know, my companies have made money, my inventions I've come up with have literally saved lives. >> Right. >> No one cares, in a sense, who I am anymore. That allows me to be visible. It allows me to just be very open about who I am and what I've experienced and been through, and then say to other people, it's not about me, it's not about whether I'm happy. It's about whether I'm serving my purpose. And I believe that I am, and does anything else about me really matter in this world? >> Right. It really seems, it's interesting, kind of sub text of diversity inclusion, not so much about your skin color or things that are easy to classify on your tax form, but it's really more just being your whole you. And no longer being suppressed to fit in a mold, not necessarily that's good or bad, but this is the way we did it, and thank you, we like you, we hired you, here you go, you know? Here's your big stack of rags, here's your desk, and we expect you to wear this to work. But that to me seems like the bigger story here that it's the whole person because there's so much value in the whole versus just concentrating on a slice. >> You know, it's really interesting, again, this is another area where I get to do hard numbers research, and when I do research, I'm talking looking at 122 million people. And building models to explain their career outcomes, and their life outcomes. And what we find here is one, everybody's biased. Everybody. I can't make an unbiased AI. There are no unbiased rats. The problem is when you refuse to acknowledge it. And you refuse to do something about it. And on the other side, to quote a friend of mine, "Everybody is covering for something. "Everybody has something in their life that they feel like "compromises them a little bit". So you know, even if we're talking about you know, the rich white straight guy, everyone's favorite punching bag. And I used to be one of them, so I try and take it easy. It is, the truth is, every one of them is covering for something, also. And if we can say again, it's not about me, which amazingly, actually allows you to be you. It's not about what other people think of me, it's not about whether they always agree with everything I say, or that I agree with what my boss says. It is about whether I'm making a difference in the world. And I've used that as my business strategy for the last 10 years of my life, and even when it seems like the worst strategy ever, you know, saying no to being chief scientist after you know, Fortune 50 company, one after another. Every time, my life got better. And my success grew. And it's not just an anecdote. Again, we see it in the data. So you build companies around principles like that. Who are you? Bring that person to work, and then you own the leadership challenge up, and I'm going to let that person flourish. And I'm going to let them tell me that I'm wrong. They got to prove it to me. But I'm going to let 'em tell it me, and give them the chance. You build a company like that, you know, what's clear to me is over the next 10 years, the defining market for global competition will be talent. Creative talent. And if you can't figure out how to tap the entire global work force, you cannot compete in that space. >> Right. The whole work force, and the whole person within that work force. It's really interesting, Jackie from Intel was on the panel that I got to talk, to see if she talked about you know, four really simple things, you know? Have impact. Undeniable, measurable impact, be visible, have data to back it up, and just of course, be tenacious, which is good career advice all the time, but you know. >> It's always good. >> Now when you know, cause before, a lot of people didn't have that option. Or they didn't feel they had the option to necessarily be purpose driven or be their old self, because then they get thrown out on the street and companies weren't as... Still, not that inclusive, right? >> Vivienne: I get it, believe me. >> You get it. So it is this new opportunity, but they have to because they can't get enough people. They can't get enough talent. It's really about ROI, this is not just to do the right thing. >> If even if you look at it from a selfish standpoint, there is the entire rest of the professional world competing for that traditional pipeline to get into the company. So being different, being you, it's a-- I mean, forgive me for putting it this way, but it's a marketing strategy, right? This is how you stand out from everyone else. One of my companies, we built this giant database of people all over the world, to predict how good people were at their job. And our goal was to take bias out of the hiring process. And when I was a chief scientist of that company, every time I gave a talk in public, 50 people would come up afterwards and say, "What should I do to get a better job?" And what they really meant was, what should I write on my resume, you know, how should I position myself, what's the next hot skill? >> Right. >> And my advice, which I meant genuinely, even though I don't think they always took it as such, was do good work and share it with the world. Not just my personal experience. We see it again and again in these massive data sets. The people that have the exceptional careers are the ones that just went out there and did something because it needed to get done. Maybe they did it inside their last job, maybe they did it personally as a side project, or they did a start up, or philanthropy. Whatever it was they did it, and they did it with passion. And that got noticed. So you know, again, just sort of selfishly, why compete with the other 150 million people looking for that same desirable job when the person that you are, I know it's terrifying, it is terrifying to put yourself out there. But the person you are is what you are better at than everyone else in the world. Be that person. That is your route to the best job you can possibly get. >> By rule, right? You're the best you you can be, but by rule, you're not as good at being somebody else. >> It sounds like a corny line, but the science backs it up. >> That's great. All right Vivienne, I could go on for a very long time, but unfortunately, we're going to have to leave it there. I really enjoyed the conversation. >> It was a lot of fun. >> And thanks for spending a few minutes with us. All right, she's Vivienne, I'm Jeff, you're watching theCUBE from the Accenture Women in Tech event in downtown San Francisco. Thanks for watching. (upbeat electronic music)