Leading the Shift Inspiring Women

Leading the Shift Transcript:

• Welcome to “Leading the Shift,” a podcast from Microsoft Azure where we hear from the people at the forefront of data,
  AI and cloud technologies. I’m your host, Susan Etlinger. Today our guest is Shirli Zelcer,
  chief data and technology officer at Dentsu, an integrated growth and transformation partner
  to the world’s leading organizations.

• Dentsu was founded in Tokyo, Japan in 1901.
  Yes, you heard that correctly. And is present in over 110 countries and regions.
  And Shirli is a bit of a unicorn, or I don’t know, maybe more like a zebra, in that she oversees both data and technology strategy,
  which is a role that feels like it could become a real force multiplier in the coming years.
  We’re gonna talk about how Shirli is quite literally leading the shift to bring data and AI together
  to help Dentsu and its customers. And in the process we’re gonna touch on a whole bunch of topics from how our understanding
  of the customer journey is evolving to the untapped potential of synthetic data to help us better understand audiences
  in a privacy compliant way. So come for the strategy, stay for the anecdotes.
  Shirli welcome to, “Leading the Shift.” – [Shirli] Thank you so much for having me. – [Susan] No, it’s great to have you.
  The Evolution of Data. Shirli Zelcer discusses how data has transformed from a back-office practice to a C-suite priority.
  I’m so excited about this conversation. So, early on when we had our first little chat,
  I was really taken by the fact that we sort of had a similar, a kind of a parallel path
  except that you started life as a statistician and I kind of come from a liberal arts background
  and the technology landscape has evolved so much. And I just, I would love you to talk a little bit about
  how your background has influenced you and sort of what you’ve seen in the last several years
  and where you think things are going. – [Shirli] Sure. So, you know, as you just mentioned,
  I started out my career as a statistician and back then, 20 plus years ago it was a very back office type of practice
  and we were still getting all of the data and the insights and looking at things in a predictive way,
  but it was not really at the forefront of decision making. And I think over the years as things have evolved,
  we turned from statisticians to business analysts and then more recently into data scientists, right?
  And this whole concept of using it as a science and having it be at the forefront of strategy
  and decision making and marketing. And to me that’s been really exciting because I’ve always believed in the power of data.
  And so watching it evolve over time to where it’s really taking a front seat for most organizations is very exciting.
• [Susan] Yeah, absolutely. And more than that, it’s become kind of a topic of conversation. I mean, I remember early in my career, people would ask me,
  “What do you do for a living?” And I’d say, “Oh, data, I’m interested.” And they just sort of slowly back away from me
  and now people wanna have that conversation. – [Shirli] Absolutely. It’s definitely something that used to be back of mind,
  and now it’s really in the front of how do we get more data and what do we do with our data? And it’s definitely become a hot topic.
• [Susan] So how did you come to where you are? Like tell us a little bit about your journey.
• [Shirli] Well, I have been with Dentsu through the Merkle acquisition actually for,
  it will be 20 years this summer. – [Susan] Wow. – [Shirli] I’ve been data essentially my entire career.
  And so then it just became a natural progression of, you know, I’ve used the data my whole career.
  I’ve thought about how to apply it. I’ve worked within platforms and then how do we as Dentsu
  was thinking about this new capability of data and technology and bringing it together.
  It seemed like a natural fit and something I was really excited about to be able to join
  and marry together the data and the analytics and everything that I’d been doing along with the tech
  and the platforms and those capabilities. So, here we are today. – [Susan] Oh wow. So did you start as like a, you started in data
  and took on CTO or is that how it worked, or it all came together at the same time?
• [Shirli] It all sort of came together. Yeah, so my career was spent largely as,
  in my more junior career it was analytics and data and then it grew into data platforms.
  And then we actually just newly formed earlier this year, or I should say last year, the data
  and technology organization at Dentsu bringing everything together, and so this is where I ended up.
• [Susan] So, let’s talk for a minute about Dentsu. Dentsu is a company that has been first of all, global and has been around for a very long time.
  And I would love to also hear a little bit about your role, which is fairly unique in that you are both the chief technology officer and the chief data officer.
• [Shirli] Yeah, selfishly to me that makes a ton of sense. (chuckles)
  I think that at Dentsu we really wanted to create this organization of data and technology that takes advantage of the power of data and identity
  and AI and sort of brings that all together to create a true competitive edge for marketers.
  And so bringing together the data competency and really understanding the depth of data
  and the different usages of data and then combining that with our tech and what we’re creating
  and how it’s both connected into our client environments and to partner environments.
  To us, that was the right way to think about it. – [Susan] Yeah, yeah, it’s interesting ’cause I’ve met
  many chief technology officers, many chief data officers, and they almost feel like really different breeds.
  You know, like you’ve got, a lot of chief data officers come from the data governance space
  and really intimately know data and of course chief technology officers are looking at technology strategy all up.
  And marrying those two capabilities I think is so important, especially now when everything is sort of converging.
  And one of the things that you had said earlier in our conversation really stuck with me
  because of course in the last two years and now it’s like just a bit over two years, you said,
  you know, my biggest excitement about generative AI is actually in the data space.
  And I think that’s a really, it’s just, it’s a really intriguing thing to say and I’m curious to hear you expand on that.
• [Shirli] Yeah, so I think we’ve seen a lot of tech come across in using generative AI
  Generative AI in Data. Potential of generative AI to transform the data space, including examples of how generative AI can combine structured and unstructured data to gain deeper insights and predictive capabilities
  around images and around copy and around all of these really amazing things that I don’t wanna take away from.
  And generative AI has done a ton for us there. But where I’m really also interested is how it can help us
  not only bring together new sources of data that weren’t able to be brought together before,
  whether it’s structured data, unstructured data coming together, whether it’s combining data without the risk of privacy
  and security because we’re using generative AI, whether it’s the rapid speed to insights
  that you can get with generative AI. I think all of the use cases around data
  to me are incredibly intriguing. And then when you combine that with all of the other things that you see out there for generative AI,
  I think it’s incredibly powerful. – [Susan] Yeah, so maybe give us a little bit of a deeper dive on that.
  So like, what are some examples? – [Shirli] So think about, for example, data that you have
  that we call unstructured data. So ratings and reviews or call center data
  that typically is not structured in a way where you would be able to combine it with other data. Now we actually have the power to combine that
  with first-party data, with third-party data and really get so much more insight and so much more predictive capability
  around how that is influencing behaviors, how that is basically taking advantage of all
  of the signals that consumers are giving us in order to really make better decisions.
• [Susan] Yeah, for sure. And first-party data being, of course your own data and third-party data being data from other places, right?
• [Shirli] That’s right. – [Susan] I mean, it reminds me of several years ago I was doing a project with a big hotel chain
  and they had this property in Hawaii and the property always got three star reviews
  and they were like, “Why are we getting three star reviews?” And they were looking at all these different sources. They were trying to look at market research,
  they were looking at NPS, they were looking at their social signals. And what they discovered was that it wasn’t really
  a three star review, it was a bunch of one star reviews and a bunch of five star reviews. And the five star reviews were like,
  “Oh, this is the most beautiful place ever. The gardens are so lovely, their restaurant’s so quiet.
  It’s just so elegant. It’s so great for our anniversary.” And the one star reviews were like, “There’s no nowhere to go after nine o’clock.
  It’s really boring. There are all these old people there.” (Shirli and Susan laughing) – [Shirli] So, that’s actually an amazing example
  because imagine if we could take all of those ratings and reviews and now we combine them with who the consumer is
  and what their demographics are and what their purchase behavior is and everything else.
  And then all of a sudden you can create audiences and tailor experiences that are specific and you know, okay, this specific property in Hawaii
  is meant for this type of demographic or consumers who are looking for this specific experience
  and that type of, you know, in a different property would be the better fit for people who are looking for the big party life.
  And it allows us to connect data in a way that we really haven’t before. Because even in the example that you gave,
  we could do that anecdotally, right? We can go and we could sift through the ratings and reviews
  and there’s lots of of great tools to be able to do that and sort of connect the sentiment, right?
  And we’ve been doing sentiment analysis for a long time, but now we can actually take that raw data and combine it
  with the actual behavioral data and get a lot more powerful
  in how we’re giving customers essentially what they want. – [Susan] Exactly, exactly. And then you can tell, who thinks the towels
  are scratchy and who’s still mad they have to pay for Wi-Fi. – [Shirli] Everybody, The answer is everybody is still mad
  if they have to pay for Wi-Fi. – [Susan] I know. It’s funny. And it’s been that way for like 10 years.
  I think one of the things that really has been, I don’t know, kind of just inspiring for me is seeing
  how all of this comes to life through generative AI and through the ability to query
  just all of this incredible data. So can you talk a little bit about your predictive analytics copilot,
  Predictive Analytics Copilot. Predictive analytics copilot at dentsu
  sort of how you’re using that, you know, specifically actually for this kind of use case? – [Shirli] Yeah.
• [Susan] Maybe not the towel use case, but you know what I mean. – [Shirli] Not the towel use case, but so yeah, so we essentially, for us it was important to try
  this use case of how is generative AI working for data, and so what we did is we really took an example
  of taking organization’s full suite of data. So like you mentioned first-party data,
  which is their own data, whether it’s transactional data, whether it’s data that they’ve acquired through their sites, et cetera.
  And then third-party data or demographic data, any data that they’ve been able to collect from partners.
  And then all of this unstructured data, whether it was ratings and reviews or call centers
  or anything that they’re able to get. And then bringing it all together and really transforming
  the way that they’re accessing that data and accelerating their speed to insights. So being able to use just regular
  easy conversational language to ask questions that typically would live in dashboards
  or typically you’d have to write code to be able to access and getting answers to questions
  and then being able to say, Okay, well, let’s forecast for the future certain scenarios
  and how would they look based on all of this data? And really giving our clients the ability
  to use these tools in a very powerful way. – [Susan] So can you give an example of like
  what kinds of questions can they ask? – [Shirli] It’s really anything that we have data for.
  So it’s everything from are there meaningful client segments that are responding to this certain message
  in a specific way? Or how is my creative performing? Or if I have an extra budget of $1 million
  at the end of the quarter, where would be the best way to do it or on which channel, et cetera. So it really gives you the power to not only gain insights,
  but also really think forward looking into what you wanna do. – [Susan] Yeah, it’s, you know, I was, I had the opportunity
  to interview a gentleman who is actually the chief digital officer at ABB,
  which is one of Microsoft’s, one of our customers. And we were talking about exactly the same thing, right?
  Except that that was from the industrial manufacturing perspective. So the people on the shop floor can ask,
  can sort of query using a chatbot and find out how the machinery is working
  and whether anything is about to fail. And that gives them just this incredible kind of,
  it empowers them actually to be able to take these actions that otherwise they would’ve had to wait
  until the thing failed and maybe their emissions were now out of control or there was something else happening,
  and so it’s given them the opportunity not just to anticipate things at the shop floor, but then also once you have that capability,
  it opens up this portal of sort of, now what can we do? And it feels like this is a really consistent theme
  with Dentsu, you know, which is of course a global agency to industrial manufacturing to lots
  of other different types of industries. So it makes me wonder kind of how that is,
  how that’s coming to life culturally, because you must have people at multiple levels
  of the organization who are now using data in a really different way.
• [Shirli] Yes, to me it’s super exciting because it gives the ability for every different level
  to get the insights and the answers to questions that they have that are relevant to making their day go better and making the decisions
  that they need to make better. So whether it’s an executive who wants to see performance overall, but there’s some kind of anomaly
  and they’re trying to dig in and they get those questions answered quickly or whether it’s a campaign manager
  or whether it’s a product owner trying to understand how their product performance is going. So it’s really nice that now these insights
  are at people’s fingertips very quickly and it takes all of the back and forth
  that we used to have where, I mean, a lot of people have very rich dashboards and capabilities,
  but if there is something different or if there’s an anomaly, the question would usually have to go to somebody who would write some code
  who would then pull the data and get that information and then send it back. And that cycle is sort of becoming a lot smoother
  and a lot faster so that you can get insights much more rapidly.
• [Susan] Yeah, for sure. And so you can get those insights more quickly, you can generate audiences more quickly,
  you can do all sorts of different things, which I think probably opens up that creativity as well.
  I’ve been really intrigued and coming to sort of data as somebody who grew up in the liberal arts kind of world
  as opposed to the world of mathematics and statistics, I always found unstructured data so interesting
  because it’s just, it’s human data, right? It’s all the things that people express
  in the different ways that they operate throughout their day. So maybe they’re making a comment on a social channel
  or maybe they’re responding to a survey or maybe they’re having a conversation. I can always hear my spouse in the other room
  having conversations with folks in call centers and feeling very, very bad for them. But you can now connect, interpret those signals in a way
  that is so much more precise than what was possible in the past and connect them.
  I wonder also like what you think about in terms of the opportunities of unstructured data going forward,
  ’cause it feels like there’s more opportunity to enable that connected experience.
  Maybe it’s connecting the person, but also just connecting an idea. – [Shirli] I think it’s connecting the entire
  customer journey, right? Because to me if a consumer is taking the time
  to write a review or call in, those are very meaningful signals.
  It’s actually one of the most powerful things that you have. And so if you can take that and not only understand
  what they’re saying in the sentiment, but rather where they were in their journey, where they left that, were they in the decision making
  part of their journey, were they post purchase, were they ready to make a secondary purchase?
  Where did they give you those signals and how did that influence the next step that they took? And using all of that information
  and really curating experiences for consumers that are much more meaningful, that really take into account their,
  what they tell the brand. I think that’s really powerful. – [Susan] Yeah, yeah. I’m super intrigued by the context
  around those signals because I remember in the early days I thought, “Well, social is so different from everything that we have
  and in that people are just expressing themselves in the moment.” And also it reaches people who would not necessarily
  be reached by a survey because in order to survey someone you kind of have to know who and where they are and how to reach them and have at least,
  you have to have the permission to be able to reach them. And then by the time somebody is at an emotional state
  where they pick up the phone or they open up a chat box
  to speak with a call center, they’ve clearly had something happen where they have a need and maybe they have a strong feeling.
  It feels like that just gives us so much more insight. But also we think about sort of a purchase funnel,
  which is really, feels like an antiquated term in the sense that everybody’s always in all of these states,
  right? – [Shirli] That’s right. – [Susan] With all the different brands. So I just feel like that this could be something
  that really helps us understand fundamentally what people want and helps brands
  actually do much more precise work to be able to meet people where they are.
• [Shirli] I completely agree. I think, like I said before, these types of signals are to me a lot more powerful than what segment you’re in
  or where you live or the information that we would’ve typically used historically, right?
  Not taking away from that. I think that’s also very powerful data, but I think the data that speaks to what the consumer
  is actually telling the brand is so powerful and we’ve really only been able to use that
  as sentiment or directional. And now with generative AI, we’re really able to bring it much closer to the behavioral aspects
  and really change the way that we’re curating experiences. – [Susan] Yep, for sure.
  I mean, as an example, many years ago when my son was born, a certain social platform, which I will not name,
  started sending me targeted advertisements for losing stubborn belly fat.
  And needless to say, that was not really the message that I wanted to hear at that particular point in time,
  but there was a certain feeling that I had, right, when I got that message.
  And I do think that we have this potential now to be,
  you know, to actually open up more empathy in our relationships with customers and consumers
  simply because we have a little bit more insight into what they really want.
  And I think that ability to create that personalized interaction at scale is what I think
  is really important and interesting. – [Shirli] It’s gonna also be really interesting to see
  how customers actually then react to brands.
  Because if consumers are actually starting to see this type of data is giving them a better experience,
  are they going to be more vocal and interact more, right?
  In that example that you gave, did you actually go in and say, “Hey, this is not relevant to me
  and why are you showing this to me?” But now that we actually have the ability to use that data
  and to curate better experiences, it’s interesting to see how consumers are gonna react to that and if they’re going
  to take the time to actually give that type of feedback or connect more with the brands
  to get that better experience. – [Susan] Yeah, well, and it really gets into the question
  of responsible data use and responsible AI and sort of the culture and what individual’s specific
  Data Governance and Responsible AI. Importance of data governance, responsible AI use, and data privacy and compliance
  desires are at a point in time. I mean, I remember having a conversation with an identity management company a long time ago
  and I said, “Well, if you want us, if you want the ability to have your privacy controlled,
  we have to know who you are.” And I thought like, “Oh gosh, that hadn’t occurred to you even in the past.”
  But of course you do because you have to know what I want versus what Shirli wants versus what somebody else wants. And we may be in different places in our lives, right?
  Or we may be in different places with regard to what we want technology to do for us. And I think, that is a really important piece
  is not so much the, I mean, I’m not under estimating the importance of privacy.
  It’s critical, but the control over the privacy, I think, right? ‘Cause we may have different needs and different desires
  in terms of what we believe privacy really means. So, let’s talk about that though. Let’s talk a little bit about data governance,
  about responsible use of data. What are some of the issues that you’re seeing, that you’re thinking through and certainly
  that you’re focused on in your role. – [Shirli] There’s a wide variety of those things.
• Susan I bet. – [Shirli] So, there’s obviously the data compliance that we think about and the data privacy
  and both keeping data safe and making sure that we treat the data with the right level of security
  and try as much as possible to not move data around and keep it in secure environments.
  So, that’s number one. Number two, there’s also the compliance from legal standpoints, right?
  So in different markets you have different levels of fidelity of data that you’re allowed to use. Obviously in EMEA there’s GDPR,
  and there’s a lot of regulation around what you’re able to do with data and with AI specifically in data.
  And there’s a lot of new laws and regulations, especially in the EU around AI.
  So, there’s that component. And then there’s also the component that to me is a little bit softer,
  but I think equally as important, which is making sure that you’re using AI
  in a responsible and ethical and not for, you know, not creating bias that is unintended.
  And so I think all of those things are top of mind day in and day out as we’re using so much rich data
  and making sure that we’re not abusing the data and that we’re both keeping it safe
  and compliant as well as using it in a way that does not make unintended decisions
  or unintended bias in our day-to-day. – [Susan] Yeah, absolutely. And I think one of the things that we’ve talked about
  is how important it is to have certainly the governance piece from a compliance
  and legal perspective, but also to have the processes supporting that also to have the education and skilling,
  so that people have that awareness. And then of course, to have the tooling that enables you to detect bias in an algorithm
  or understand how a decision was made. And I think that’s something that’s really important.
  On the other, on the flip side though, one of the things that’s been super intriguing to me lately
  is some ways that we’re using technology to do things in a more privacy compliant or responsible way.
  So for example, synthetic data, you know, one of the ways that we can protect privacy is by using data
  Synthetic Data and Zero Party Data. Concepts of synthetic data and zero party data, and how they can support privacy compliance
  that we actually create for particular purpose. So, can you talk a little bit about that? – [Shirli] Yeah, this is an area to me
  that I think is very untapped and I think over the next year or two, we’re gonna see a ton more of this,
  both from a persona standpoint and creating synthetic personas and seeing how they react
  and seeing how they behave and using that. But then I think also more broadly for creating audiences
  that we can map to or we can activate against, because especially in privacy compliant regions
  like the EU, we’re not really able to do addressability at scale
  because of the challenges around PII. But using synthetic data and getting at audiences
  and behaviors and then being able to activate through certain signals, to me gives us a lot of opportunity
  to get a much more personalized experience while maintaining that compliance.
• [Susan] So yeah, so let’s break it down. So PII is personally identifiable information.
  And so synthetic data, if I understand properly, gives you the ability to create a data set
  that mirrors an actual situation in the world, but without actually using their data.
  So you could say, okay, so I wanna make a data set that is women in France from ages 23 to 40
  or something like that who have done X, Y, and Z. You can actually create a data set to do that
  without using their specific data. – [Shirli] Well, you would wanna have a seed of data. So you still need to have a seed that is compliant
  that is allowed for usage and approval. So you would use that seed data and then you would
  kind of essentially blow it up into a full data set that has a lot of characteristics to it
  that you can then apply models on or activate against or do really cool things with that you wouldn’t be able
  to do otherwise. – [Susan] Yeah, and I’ve heard examples of this,
  for example, in the medical field where you can’t necessarily do modeling on a population,
  let’s say for brain MRIs or something like that, but you could take a subset of brain MRIs and actually create a population and actually model
  that for different kinds of outcomes, and so- – [Shirli] No, because the nice thing is in that scenario, you could kind of scrub
  any of the personal information to create that synthetic data, so you’re not actually using
  anybody’s personal information, but then you’re creating a rich data set that will allow you to get a ton of learning out of.
• [Susan] And you could do forecasting against that and different things like that. So, I love that idea.
  Can you actually talk a little bit about zero-party data? What is zero-party data?
• [Shirli] Zero-party data is when a consumer actually gives their data consentedly.
  Is that even a word, consentedly? – Susan It is now. – [Shirli] Let’s go with it.
  To a brand. So, and a lot of times it’s an exchange of value of some sort.
  So if there’s a loyalty program and I willingly give my information because I’m joining a loyalty program
  or because a brand asked me to do something. So when a brand gets all of that information
  and stores it and it’s consented and privacy compliant,
  that is zero-party data. So different from transactional data, which we call first-party data and behavioral data,
  and then different from the third-party data, which is data that you get that is from different companies that you purchase.
• [Susan] So it’s really a mosaic, right, that we’re thinking about from all the different ways that we can either encourage people to share data,
  Future of AI and Data. A look ahead to the future of AI and data
  we can create set, we can use the data that they, you know, in our transactions with them,
  we can use data from the outside. I mean, ultimately this feels like a mosaic and it feels like a way of using this technology
  to bring together as much information, as much knowledge and insight as we can
  in order to be able to achieve business outcomes. And in many cases, I think we haven’t, you know,
  we’re still so early on in this conversation, in this whole platform shift that we’re in
  that we haven’t really even seen I think a lot of the impact that we’re gonna see in the future.
  But I wanted to kind of just bring it home a little bit with what you’ve learned.
  Like in the last couple of years you’ve been through a lot, we’ve all been through a lot
  and we’ve all learned a tremendous amount. Like what pops out at you?
• [Shirli] It’s interesting, I think that at the beginning of this boom of generative AI,
  everybody was super excited about it and there was so much hype.
  And I think where companies have been successful is when they’ve said, “Hey, we still have the same goals
  as a company and we still have the same KPIs we wanna achieve.
  Let’s just see if AI can help us get there faster or more efficiently,” as opposed to companies
  that have said, “Hey, we’re gonna bring in all of this cool AI and figure out what to do with it.” And so I think that the biggest area of learning from your,
  I guess the advice that I would give is to think about the use cases that you have as a business
  or as an organization, and what are the specific use cases that you’re trying to achieve,
  and can AI help you with those specific use cases as opposed to trying to kind of fit AI
  as a tool for everything. So, that’s sort of the big thing for me.
• [Susan] Yeah, start with the business problem. That’s right. I mean, I think it’s interesting though. Like you start with the business problem.
  You start with a real problem, but at the same time, then there are things that you just discover kind of,
  you know, along the way. – [Shirli] Discover. – [Susan] Exactly. Spontaneously. And I think that combination of like business rigor
  and sort of spontaneous ideation is also really powerful.
  And it’s something that I hope we see more of. When you think out. So we were talking a little bit,
  I know we’re gonna geek out in the final moments. So there’s a saying that I know you know,
  by the very famous statistician, George E. P. Box,
  and he said potentially, he said, although we’re not entirely sure if he said it, “That all models are wrong, but some models are useful.”
  And I thought that was super interesting when you apply it to unstructured data because humans are so complex
  and the way that we express ourselves and our language is changing all the time. In fact, you just coined a word
  in the middle of this podcast, right? Language changes all the time. And so do you think all models are wrong?
  Do you think some models are useful? Like how do you think about some of these kind of first principles?
  It’s funny as it relates to where we are. – [Shirli] No model is a hundred percent accurate. Nothing, no tool, no model.
  So in a sense that it’s actually right, all models are wrong, but I firmly believe
  that they’re also incredibly useful and they’re better than no models.
  So I think I would actually change the saying a little bit to say all models are wrong,
  but properly built models are better than no models.
• [Susan] I think that sings and I think that we should just propagate that as our motto for the future.
  (Shirli chuckles) Amazing. Well, thank you so so much, Shirli. It’s been such a pleasure chatting with you.
• [Shirli] It’s been great. So, thank you for having me. – [Susan] Of course, thank you.
  Conclusion. Susan Etlinger shares takeaways from the conversation
  (upbeat music) Wow, that was such a fantastic conversation
  and there’s a lot going through in my head right now, but there are a few things that really stuck with me. The first thing is everybody talks about
  how data is the fuel for AI, but Shirli raises a really, really intriguing point,
  which is that it’s actually kind of a two-way street. Data fuels AI. But generative AI is also transforming the data space
  by enabling organizations to bring all these new sources of data together to do all sorts of things
  that couldn’t be done before. And then right alongside her appreciation of the opportunities of rich data and advanced models
  is Shirli’s respect for using data and AI responsibly. She has this incredible background in statistics,
  which gives her tremendous insight into how important data privacy and security
  and responsible AI really are. And finally, Shirli’s journey is kind of a microcosm
  of the platform shift we’re all living through. She started her career as a statistician, which was frankly seen as a back office practice back then.
  But given all the technology innovations of the past two decades, and particularly in the age of AI,
  strategic use of data is so critical to an organization’s competitive advantage that technology
  and data strategy are now C-Suite priorities. And Shirli’s career is kind of a perfect illustration of that.
  Thanks for listening. I learned so much from this conversation, and I hope you did too.
  (upbeat music) We hope you’ve enjoyed this episode. And as I’m sure you know, new podcasts
  live and die on engagement. So please like, comment, share, tell your friends
  and let us know what you’d like to hear about. We’re listening. If you’d like to learn more about how people
  and organizations are innovating with Microsoft Azure, visit azure.microsoft.com.

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