Emerging to Converging Technologies Conference with GIBS and Georgia Tech – Manoj Chiba



[Applause] you know ten years ago I was the unsexiest person by saying I'm a statistician I had no future right anyone in the Kwan's field what are you gonna study math and stats and all of a sudden what happened who you know what do you do all of a sudden even when this morning's talking about data Wow artificial intelligence hmm image recognition all the nice stuff where does it start ladies and gentlemen data your data is interesting Angela you mentioned the behavioral economist behavioral economists believe that it's not about the size of the data thankfully it's rather the quality of the data and that's still the same thing that works with big data and the Internet of Things ladies and gentleman I'll speed up my talk a little bit here to get through this year but I'll quickly take you through what I was hoping to get through is separating these concepts of Big Data and the thing called Internet of Things I now commonly term it the Internet of everything and I think there's key problems that come with this and that's what I'm going to talk about right right at the end around the key concerns and the opportunities that really sit I'm gonna take a different lens the ease some healthcare stuff in here some of the Google Flu trained stuff that went horribly wrong but also starting to look at it from an organizational perspective corporate companies how do we start really using this stuff to leverage value for both our consumers and users of our products and/or services because I think there's been a lot of talk about Big Data there's been a lot of talk about artificial intelligence let's talk about recommendation engines I came across ml Amazon's recommendation engine probably about 15 years ago and if you bought a tray let's eat today guess what they recommend it to you tomorrow another one how many toilet seats do you really need so we've been falling a little bit on the the recommendation engines but this year was the first year that we've actually seen recommendation engines hit the bottom line of a company and I'll show you some of those stats as well because all of this is based on the accurate or good use of data I'll take you through the strategic planning as we've missed the trick ladies and gentlemen I often get asked can we come up with a data strategy and I'm like a organization only has one strategy and that's the organizational strategy your data strategy feeds into it or assists it in its decision which markets and we to play simple as that and so let's not elevate data further than it really can assist it's always going to be a cost center in my per in my perspective unless your recommendation engine can hit the bottom line unfortunately and that's what we've got to look at it as I talk about data as being a bridge that's all it is it enables something similar to technology and I think the question came up earlier you know it's great that we're seeing these technologies specifically on the emerging market context the African continent how are we using it t-tom Bellinis favorite country Rwanda is using technology drone technology in places where the infrastructure is bad to deliver medicine blood so how do we start using the technologies that we often see coming out of the West or the Western world how do we use it and please listen carefully to this to solve needs in emerging markets because the West is often concerned with rather than solving needs creating new markets we need to be concerned more about servicing basic needs when we conserve basic needs leveraging technology we see efficiency we see impact that's what we see when we look at how they doing it in Rwanda as an example I'm sorry I follow Tito Marini on Twitter and his Cooking Channel that's to be coming soon but if we look at how they leverage technology that's important it's not about what new markets can be created it's about how do we serve our customers consumers including us as citizens Peter it's about bringing back respect we talk about trust and I'm always interested that we talk about trust because we can see what happened to Facebook and the US data but we also talk about the fancy devices your iPhone your Samsung phones all of these things the fancy watches Internet of Things how many doors do we have these are all doors into your house windows into your house I speak about this in this context of cybersecurity we quick to adopt but we quick to say oh that's my data you know when puppy came in to act how many people were phoned randomly and also what's your ID number my login for many things was my ID number now you want to tell me it's mine I think it's a bit late Who am I married to I hope my wife still ok yeah I hope so if she listens but otherwise we use my ID number going to so I think we must be just be pragmatic about some of the stuff that we see in coming out and I'll talk about dynamic operating decisions in somewhat examples I think that's what many people are looking for is we those concrete examples I was speaking to a behavioral economist again and saying we are the concrete examples of behavioral science coming to fruition to assist organizations let's rather engage with those because I think it's a fascinating area but how and that's what we need to really get into so I'll quickly get into it we've all seen this the first Industrial Revolution my academic colleagues here we can have a long line longer lunch 20 minutes more if you don't mind but is this another revolution why are we numbering it and all of that we've got our opinions but what I want to really point out is specifically this one here is that we've now entered into this impact on societies bigger than impact on industry that's the shift that's the massive shift is the technology now enables and gives the power to the consumer and the citizen rather than to the corporation for the first time all of a sudden business has this thing called customer experience user experience now they want our input just hold on now I'm a bit young so in the 90s you didn't want my input but now so manoj how did you experience that oh and then you're going to design your product or service around my experience who's got the power now ladies and gentlemen right and I think this is a fundamental shift because we're also interconnected the internet we can start complaining and we can start doing all of these things very very quickly so I think this is what I'm entire talk on here specifically big data we've heard this term a lot I think there's quite a bit of confusion around it so I'll quickly define it traditionally it's data that is far too complex or large complex and dynamic for conventional data tools to capture store manage and analyze I think it's poor because big data is not about size it's really about the ability to take it away from size and talk about its ability to search aggregate and cross-reference datasets and when we talk about datasets many people automatically think numbers we've got to start abstracting away from that because big data is really about taking words which is text numbers images and voice has data sources overlaying them on top of each other to be able to come up with a true understanding of what that data subject is about because I'm not a number I've got a personality I hope okay and I've got how I look how I speak those are all inputs into the data for artificial intelligence and all of these nice things to work properly you can't take a single data source a sample of one has always been a bad idea ladies and gentlemen you've got to be able to understand that people are made up differently we are multi-dimensional and so we must understand the big data is really about different types of data as well and then the analytical the identification of patterns and every time I think of machine learning algorithms they come up with these things don't expect your machine learning algorithm to come up with anything different than what you've programmed it to come up with didn't instance a that's the definition of insanity or stupidity whichever one you like right and I think it's important for us to remember that because as our understanding what are the implications is that we don't end up with variants we end up with convergence which means that we treat everyone in every segment exactly the same that's the real reason and a real fear that I have from a data analytics perspective but this really starts highlighting something big for me is how do we handle big data in a rapidly changing environment you know normally probably about January or last year sometime you could say the South African country our context was a highly complex but let me show you what we believe is complex in the world if the current rate of change and complexity were to remain constant and this I take from an astrophysicist so I'm going to take it as the truth which remain constant we were we have experienced all the major milestones of the 20th century in a single week in 2025 ladies and gents the week is still going to be seven days the day is still going to be 24 hours let's see what are we looking to her to undergo here the creation of the car first and second world war in the Vietnam War decoding of the DNA structure nuclear energy space travel the internet human genome sequencing all happening in a single week the Internet has fundamentally changed the way we do business and how we communicate with each other you want to talk about complexity we ain't seen nothing yet that's the difference each and every one of these events here has fundamentally changed how we view the world it's changed how we behave as well so when we start thinking about data and big data its ability to be able to get through this complexity what are the fundamental trends that we may see into the future and analytics is postulated to be that quicker than answer analytics I think has been elevated to a stage to be the gospel truth now and people are keep saying we have the answer in the data the data is not going to just give you an answer it's asking the right question which is a human thing that's the difference so that's the big thing here and we've all know about this about big data we heard about the four three V's first and then there was a four three and the three académica headed another V value kept through every V let us understand what is that value because otherwise business and practitioners don't care about it it becomes an abstract concept because what is value if we talk about value in this case yeah it's generally we're talking about return on investment efficiency gains because it affects the bottom line we've seen some of the banks sorry sagen how you from standard bank closing down branches to go digital and what efficiency does that have it's got a cost on the other side but it's a sunk cost but you've got to be able to understand what data allows us to do is moving so I'm gonna give us the answer but it allows us to move from uncertainty into something called a usable probability that's what it allows us there's a 60% chance of this happening or there's an 80% chance of that happening instead of I don't know that's what data gives us some sort of certainty but it's a user probability I'm gonna flip through the next three slides very quickly so please just keep your eyes open I know you generally get hungry around this time I am right now this was the big data landscape in 2012 fast forward to 2016 fast forward to 2017 and it's not because I don't know how to do slides ladies and gentlemen is that what happened here is that we saw many compared of many companies being spawned to be able to leverage what's happening in this environment I often get asked what is the ideal tick stack and my answer is two bottles of wine blindfold and a bunch of darts choose because they will all integrate very easily that's what will happen right and this is the reality this landscape is fundamentally changed I've been looking for the 2018 one's not out yet but I also want to just bring about in 2012 we didn't even add on just go back here you Internet of Things as a data source at the bottom here it starts making an appearance just have a look at that and then fast forward to 2015 and look at those data sources down there now and this ladies and gentlemen is where we always talk about data and I just want to pause here because I see as many big companies in the room here and this is always a bag bear for me specifically in massive organizations how do we structure our analytics departments whether we call them business intelligence services advanced analytics whatever fancy name you wanna give it we make it a standalone business unit like this I don't know what profit they generating but okay ideally if you see successful companies to not structure analytics in a department this way they overlaid and impede it across the organization that's when you see the value not when it's a standalone or you have a center of excellence and you've got a wheel that spire have been spoke it out nothing else so I just wanted to bring this up because I've got to talk about some high or T as well and if we look at this here Google sometimes lies to us but not all the time but let's have a look at this search interesting data terms over time by Google Trends I think we've seen a massive shift here specifically we saw a big data ramping up about 2015 we started seeing it come down and then we started seeing this data science go up and I'll talk about that beast or the unicorn called the data scientist briefly and then we've seen the other one artificial intelligence plunking up why do I use Google Trends here and I always say it's perception people's perception actually drives some adoption if you think about this year and I use my wife as a good example yesterday she sent me some what's her pictures of techies or shoes that she wants I know this weekend she's going to bite its intention to intent right so by by saying I just want to find out it's because you're really interested in this thing and I know she's going to buy it and I know my credit card is gonna cry but in any case but what we've seen and why have we started seeing moving from big data and we're seeing an uptick in this this unicorn core be a data science of this field called data science is because it was always there to give us some big hope this is what it's really about ladies and gentlemen without big data analytics companies are blind in depth wandering out onto the web like a deer on freeway that's what it's all about here information is the new oil of the 21st century analytics is the combustion engine and then we saw the Economist come up with a nice cover page saying that data is the new oil of the 21st century I don't buy it because oil has a one-to-one relationship and my right Roisin from shell you use it once it's done data has a one-to-many relationship that's the difference massive difference they as though there's some big hope but the big creation stall stood Weiner rapidly we are rapidly entering a world where everything can be monitored and measured the doctors even told us that now right but the big problem is going to be the ability of humans to use and analyze and make sense of the data let that sink in the technology is cool to collect it how do we use it because if you're not going to use it why did we collect it in the first place our machine's gonna make those decisions for us we can help them if you want to program them to do that but understand by programming your machine to analyze all this data you're gonna come to the same point again over and over again and that's the big issue that we have and so what we saw is the big emergence of the data science field coming up and then this little beast ladies and gentlemen the data paint is the sexiest job of the 21st century as our colleagues at Harvard told us someone gave me the label so just to let you know this is what the new sexy looks like brother yes okay so we trying to shift people's perception of sexy there's been a massive uptick in this job here but it is the sexiest job of the 21st century everyone's looking for a data scientist to do what make sense of the data how many of us don't believe them though you don't understand business but can you see how important these things become now so I think that just from a big data perspective and let's just briefly talk about this Internet of Things by definition is just connected physical optional objects and it's a thing ladies and gentlemen that collects and transfers data over the internet without sort of any manual intervention so we know that this thing is syncing to my phone syncing to somewhere else and I don't know who else knows how many steps I've taken in a day that's way it basically sets and that's where we seeing much of this go ahead and then we've seen the rise of the Internet of Things I think we've seen the hockey fear hockey stick effect going up this way here all the connected devices you know there's the whole thing about the lakes in your home in everything else and all the knives devices that's good for all of us but there's a massive population out there that has no access to any of these internet of things how do we use the patterns if we can to understand how we behave and how do we make sure that other people can also behave in the same ways by transferring and making inferences based on the data that we have we keep talking and we keep forgetting about a large portion of the African continent people that are living below the poverty line Angelo showed Gapminder what Hans Rosling the late Langer Hans Rosling used to say be hope below the washing line because it's the $2 per day who said it's $2 per day because on the African continent we say you live below the poverty line when you hand a $1 per day so which data do you look at how big is our poverty problem and then how do we give used technology to get access to services and that for me becomes the big big bug big question and so the IOT devices that we all use and we all see is simply an added data source ladies and gentlemen I'm being really pragmatic about it it gives us some intelligence will CERN I'll show you the example of our fancy watches but it's a debt that's added datasource and the question for me is what are we doing with that data at any level and so more importantly what we're seeing is some convergence in manufacturing we sing dynamic pricing models come up number two for me is very interesting we can remotely operate our customers equipment more efficiently and I've got the words Giza IOT they're so involved in this project by putting IOT devices on people's geezers to be able to predict when a Giza is about to burst because that's a big problem for the insurers specifically not the cost of the Giza about the collateral damage essentially and that becomes a big issue we were getting quite accurate at it and then what was happening the insurer could remotely switch off the Giza so now I'm saying okay if the Giza person you didn't switch it off who's liable do you see how that not shifts the question so how much control do we really want over these things a predictive maintenance that we've seen what if France is then for government I think smart cities has got to be the way forward I think in Johannesburg right now you know that we under some 54 hour think that you know changing a valve but our preventative maintenance is massive ladies and gentlemen it's been quantified I think in Johannesburg alone by sitting in traffic you lose about eleven days of productive work eleven days that's why I love Investec was not come up with a new HR policy did you hear about that I think it's it's quite cool my boss isn't GRC she's live in tiant okay the other one here but what is less clear is how organizations in the car is steeped in the current business models are gonna change I always ask this question are you ready to have your Kodak moment companies because what we are seeing fundamentally is the rise of what we call global monopolies we've seen this apples gonna become a bank hold on they provide you with a phone not even connect just the phone now they want to get into banking who the hell are they okay discovery let's just pick one discovery for a little bit because I paid my premium yesterday you guys were supposed to give me health insurance cool because I could afford it now you've even got a banking product and I was laughing at writing notes at the gym yes I do go to the gym and said how many kilometers my said on for my statement and right I mean that's the new because you guys have the integrated business model right the more products I have with you the cheap rate becomes in Medinah so much even just winking at me okay but those are new business models and when we see these new business models arise guess what ladies and gentlemen is we've all taken aback and saying wow that was innovative no all they've done is flipped them the thinking behind the organization nothing else many of our organizations are steeped in current business models which unfortunately does not make sense and does not make us future fit and so we need to understand how IOT is an input into Big Data fundamentally effects business models and steeped in what we call the resource based view of the firm ladies and gentlemen it's no big wonder globally and not only in emerging markets that there's a dearth of skills in critical areas it's not a South Africa problem it's not a Africa problem it's a global problem we know that we're sitting in a resource-constrained world we can look at that from a climate change perspective we can look at that from any perspective so how do we operate our businesses will be a better using a different business model when we have constrained resources how do we reorganize our resources to be able to leverage what may be in the future and that for me is going to be critical and I'll show you some of the thinking and some of the research that we've brought on this we're seeing three emergent properties specifically around cost value experienced vanity and platform value I'm not talking to traditionally just about platform businesses here and this here for me are always worried when I put up the slide specifically visually because many companies think they know information technology companies IT companies tech companies not every one of your companies is a tech company they just keep strict to our core nothing else that's one of the things that we start seeing and then we're starting to see the emergence of this here which is the emergent business property that I've got been running it gives for about four or five years now research around the critical components of this and what we're really starting to see is value delivery value capture value architecture resource competency data analytics and value network a value network is important because it's about creating ecosystems it's about being able to let your entire supply chain speak to itself by itself but hold on Walmart did that a long time ago it's not new Walmart has done it so how do we make sure that we can deliver something to someone at the time in in it in the time of need that's what's important yet and so the big imperative still remains what are we doing with our data here data that is that sir that's unused is no different from data that was never connected and then I love this little one here let's solve this problem by using big data none of us have the slightest idea to what to do with how many organizations and it's about feature engineering I think Andy has raised the see it's about bringing bringing up those critical features that really make an impact on what we're trying to solve hold on what are we trying to solve that's a human thing that we need to define and if we don't define that data is not gonna give us any answer we can end up with what we call spurious correlations and so that brings us into these emerging technologies that I just took a screenshot of this these are the top 30 emerging technologies or top five required data each and every one of them and what's really happening ladies and gentlemen were at an individual level is this thing called the Internet of it's number two which is sensor than the wearables that you and I we and how do we start embedding it our doctors had shown that but this is really driving a change in human condition it really is all of us here in this room are guilty let's look it's up the work that's been done around this does it mean because I knew Angela is going to talk about Android I said I'm gonna go iOS just because I like that no I'm joking okay but have a look at anyone anyone with an iOS phone will be given this screen time why do they give us a screen time just to show unproductive viewer what absolutely I don't know but the reality is here is looking at a growing trace growing concerns around increasing device usage there's a thing called smartphone addiction gone about gone Alcoholics Anonymous gone it's smartphone addiction I'll never forget a few years ago I was with my wife at Rose Bank and there were teenagers that were walking in a line this way and each and every one of them was like this and I'll ask my wife so why the hell are they even here together how many of us catch ourselves doing that there's this thing and it's a big problem and this whole thing about digital health so as much as digital is good we've got to be careful about the impact that it has on individuals health and the reality that exists we love talking about technology ladies and gentlemen and I don't think anyone in the world can put up the hand and says I've got a proper handle on what the technological landscape looks like it's evolving far too quickly that's the problem and the problem that we have with technology is that that's how it's growing and our ability to adapt is at that space there which leaves us unfortunately with a gap and then we talk about the regulator the regulator on average from a technological space is about three to five years behind so if we're three to five years to really make a lot of money and then shut down that's my philosophy right but let's have a look at that okay and that's what I see is the biggest the big problem and the reality that they satiric planning and I'll go through this quite quickly here that I think it's we seeing of space of rapid innovation innovations to gain market G and faster scaling than those that are only clinging to sort of physical business models I came across a term called fidget oh my god digital physical plus digital oh thank you I'm so happy I heard it for the last time last I read it for the first time last week my word I was thrown aback I think I lost the rest of the 60 minutes of the meeting I was like digital vision I even googled it to see what it was and I think it's it's a fascinating concept because it really says because at some stage do you remember if you once said that either brick and motar was going huh South Africans do you remember yeah go on and then what happened Amazon decided to open up some physical stores ie hold on they are single-handedly playing with our minds when told us that ecommerce wins they went and bought whole foods and then they did Amazon go hold on I thought you were playing in that space now you want to play in the brick and mortar space you guys are just playing with us now ok yes these distribution channels and all of that going up as well but I think there's some big things is that we need to rethink the way the concept of an organization ladies we've got to understand that it's just simply about us interacting with a whole bunch of actors and players in the space here so it's really about coordination across all activities there's three inferences that we've picked up specifically they're coming to play now from a strategy and strategic planning perspective number one is that we know that there's an information explosion number two is the distribution cost it's tending towards zero that's just the reality being the emergence of new ecosystem in the world is becoming digital at a faster pace that's the first inference the second most companies have to globalize in order just to be sustainable I'm not saying beating the competition just to be sustainable that's the big thing that comes out now and here we see the ability to collect collate process data and draw inferences from global trends and markets would just be the basic ticket to play in the market in other words you've just got your hand on the door handle nothing else and the last and we've seen I think between the first emergence of digital currencies and I'm gonna keep banging this on for many many years I think that's gonna become a real thing and we've seen what Facebook is done with Libra and the last one here these businesses have to cope with an increasing number of interdependent variables in other words what I'm not going to call what I'm going to call the butterfly effect a small change somewhere else has a ripple effect on the line nothing is only industry dependent now the fundamental changes that happen in the macroeconomic environment that happens in the isolated place in Russia as an example or Alaska can affect your business and we need to be cognizant of that dynamic operating Desir dynamic operating decision-making I think this is where machine learning for me really comes into the play we've all heard of scene these two big companies Alibaba and Amazon we've heard about them roughly I just want the one stat because I'm a statistician just let's sink in singles day 2017 Alibaba alone was more than double of what all US retailers made on Black Friday and Cyber Monday in 2017 combined so everyone familiar with single train in China it's on the 11th of November one one one one one right there's only two things that come to matter one thing that comes to mind is a lot of seeing people buying themselves presents in China on alibaba's channels right that's the only way just let that sink in ladies and gentlemen its massive Alibaba is massive but then let's just contextualize that a bit existing population China is but bigger hey Internet users many more it's only the size of the economy that's slower smaller and but we also understand that China has got a closed economy it's largely a closed economy so we must be careful when we start looking at these here what we are seeing is how doesn't what can we learn from Alibaba from an organizational perspective because to address poverty to address better health outcomes to address all the problems that we face in emerging markets if our companies can do good we can charge them a lot more text right let's just be open and honest with each other more money into the economy more money that the size of the Revenue Services can appropriate to services that we need that's just the basic equation and so how do they do this yen and what we learned that you've got a date if I every customer exchange smart business capture all the information generated during exchanges in communications and let the algorithms figure it out because unfortunately ladies and gentlemen none of us here were both to deal with complexity we see data we get confused I've stalled trying to look in an Excel spreadsheet with numbers on there and you know in the matrix that movie where stuff just appears doesn't happen so we've got to get some processing then that's number one number two you've got a software every activity think the activity in real time what does that mean Paul the model of how humans currently make decisions because I'm sorry to say there's all senior managers in the room and executives in the room just go make the decisions based on your same historical way of making decisions it's difficult to change human behavior that way so why don't you just let a machine do it for you come to the same conclusion relatively ok then the third and the fourth one is get data flowing I spoke out this one here in terms of structuring analytics departments and the last one years apply algorithms in other words automate so question is if these four ways allow you to be able to leverage good impact for your bottom line in combination with your three inferences that I spoke about for strategic planning the question is how and quite simply for me to how is machine learning that's what it's about and what is machine learning we talk about it often but it's really about by feeding data and information in the form of observations in real world interactions lady just come out let there be some stuff because if we use machine learning to help us make decisions I'm choosing my words very carefully help us make decisions then it becomes a powerful tool expecting a machine to make the decision for you why are we employing you again that becomes the question because it's not a human that needs to make a rational or honest irrational but make a decision based on the context within which the operating and so some examples briefly here about what we've seen out the resource constrained current source I think we talked about resource constraint only emerging market context but developed markets have resource constraints let's look at Yelp and the city of Boston they use machine learning using Yelp reviews people give up the information quite freely I mean if you comment in post stuff social media yeah I didn't like that thing we mined that data and what they did is a 25 percent rise in the number of spot fictions that uncover violations you don't need to chuck more resources at things you leverage the existing read the next one they popped up here is connecting for customer value Under Armor the Census and track all the activity more personalized experiences that's what they doing using your data and feeding it you back in a manner that you can use it at a personal level we've heard from the doctors around Google Flu Trends we were trying to predict the outbreak of a flu yes it was a bit of a failure but what can we learn from that failure and how do we now move that forward the last one here this one here we've all known the fancy devices the watch and the iPhone here anecdotally I've heard one example with someone's watch it actually ping them in a meeting to say that their heart rate had gone above an acceptable level one okay but the reality is is that that saved one person's life that's what he did because this was a heated meeting and they realized the heart rate going up and that thing had stopped him it stopped him it really you know he looked at this thing and he realized hold on this is what's happening so I think we've got to start looking at this here so if these are let's become regular occurance and what's since is an operating properly you wholeheartedly recommend it to make an appointment with the doctor can you see we're not replacing them we simply telling you before you realize it's too late and you fall and down go and see your doctor that's what we're doing and what as much as we do it for aeroplanes and in the manufacturing industry what we're doing is fast the question for me is how do we get it low-cost enough to make sure it's accessible for everyone that's the trick here and then this one here is there an impact on parenting I had to put this one up after I heard about the crying from Alton I think Helton spoke about it just look at what these individuals did here it's it's called an infant cry baby analyzer I do not I've got a story behind it but I have no shares in the company but they collected the crying songs of two hundred thousand babies from newborn babies and he can tell if he's hungry tied in pain and has a wet diaper takes 15 seconds of the app listening to crying to make the analysis 92% accurate in infants under two two weeks and eighty-five under two months how many of you have children you had three o'clock in the morning do not tell me about parenting in you should know the babies cry 15 seconds change the nappy good to go I bought this for my brother-in-law and his wife gave breast and one of my friends that I was a cheapskate I was like it saved him sleep time guys okay this one here that's familiar with these guys yeah this time they didn't even get that far as the semi-finals but we can also analyze on the reason I show this is because we from an image perspective which we can understand our peoples faces and what images and what emotions that will sit behind that for data analysis what can we do is make sure that we have targeted advertising that goes out at the right time through the right channel at to the right people and that's the right offering at the right time through the right channel nothing more than that but it's all data driven we watch people their facial reactions and that's important because if there's something that I've realized is that your face never lies given the poker players that's where the weight losses okay and then this one here about I'm almost Daniel ooh I'm eating the impact of personalized recommendations as you can see ladies and gentlemen Alibaba eventually on the 15th of May wind public with it they beat the estimates as personalized recommendations boost sales and it's not because if you bought a toilet seat yesterday the recommending your another toilet seat support offering in the interest to a toilet seat the staff that you would nearly wouldn't have thought about buying they end up buying that we have these smart mechanisms around this year but for the first time ladies and gentlemen data can now be a profit or loss in say this is how much we've contributed to the bottom line it's the first time they said it increased itself by thing 56.3% give or take me two percent if I miss the number but over fifty percent of that sales so I think it's a massive way to start looking at it the ability to provide products and services just in time based on evidence data okay from multiple sources remains the aim that pick up three fundamental capabilities that we do require here hyper-awareness inform decision making and fast execution are gone in organizational terms a long time to make the C if we don't change our capability and how we do these things should leverage that the data that we have we're going to lose it and so some of the key concerns quickly IOT in different standards I use a very crude example for the South Africans the more connected devices simply means motors in Windows for Abhi's let's understand that the more connected devices you have the more options hacker has to injury I hope that metaphor resonates on that image that's what it is look at all the hits that we've seen 46% of cyber breaches are employees HR HR what we can do about this ok average number of days did you take to a breach by industry I've got them here health care takes 255 days yet it's the probably the most sensitive data you can have on a person outside of your bank statements – that's a whole yeah okay other problems that we have is leadership and talent management motor does not me lead to success and I think we must be clear about that a company culture I don't think this is going to change very quickly it's difficult to change something that's intangible and the most dangerous phrase in the language is we've always done it this way we can talk about AI we can talk about machine learning ladies and gentlemen but unless you don't have a grasp on what your data is weight comes from how you plan on using it stop collecting it

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