It is said that data science is more art than science, and that data scientists are artists who make sense of complex chaotic information and present that in exciting ways. They’re changing the world, building new capabilities, one data set at a time. Even named the ‘sexiest job’ of the time, this field has become a staple across companies around the world.
Having spent more than a decade and a half growing and leading Data Science and Analytics Teams, Ranjani Mani joins us for a conversation to talk about the future of Data Science jobs, leadership etiquette, and more.
Praveen Bhadada: Data science is more art than science and data scientists are artists who make sense of complex chaotic information and present that in exciting ways. Even named the ‘sexiest job’ of the time, this field has become a staple across companies around the world.
Hi, I'm Praveen Bhadada, Managing Partner at Zinnov and today I have with me Ranjani Mani, Director, Analytics and Data Products at VMware. Ranjani leads a global team of 35 plus Data Scientists, Analytics Managers, and Product Managers across four geographies, and engages with leadership teams across PM, Engineering, IT, Technical and C-suite to solve business problems for Customer Experience and Success Organization at VMware.
She has more than 15 years of experience in setting up and growing Data Science teams, Product Management, Consulting, and Analytics with stints across high-tech companies, such as Dell, Oracle, and most recently at VMware.
Welcome to the Zinnov podcast Business Resilience series, Ranjani. It's a pleasure to have you with us.
Ranjani Mani: Hello Praveen and hi to everyone who's listening. It's a great honor and pleasure to be here. I did go through your podcast, Praveen, and you have had eminent speakers. Absolutely an honor to be here.
Praveen Bhadada: Thanks so much for the kind words. Let's just dive right in and I want get started with my first question. You've been consummate technologist. You've worked with some of the biggest technology brands out there. You've done Product Management, Analytics, most recently, Data Science. You've worked and led global teams in different roles over the last 15 years. I may curious to find out how did you eventually land up at Data Science? What drew your attention, energy, and passion towards the field of Data Science?
Ranjani Mani: Yeah. So, if you kind of take a step back and think about where our country is today, even two decades ago, right? The massive transformation we’ve had which we take for granted is thanks to technology. And for me, Praveen, my parents had degrees in English Literature, so very different backgrounds. And they worked in government organizations. So for someone like me, I think Tech has been one of the greatest levelers. And personally, I'm passionate about a lot of things, but over a period of time, I realized that it falls under three categories - AI and Technology – absolutely love the space in itself, books, and being better.
Growing up, thanks to my mom who is also working, this is like three decades ago. I've been a strong believer in breaking these stereotypes and hence my strong interest in Tech. And honestly there's no better time to be in technology than right now. And you spoke about Analytics. So Analytics, I think marries my interest of solving for business problems and making sense of patterns and data. And in this 15 plus years of experience I've worked across Product Pricing and Customer Experience, Analytics and what I realized was that what I love the most is solving problems. And, if I were to conclude that, I very strongly believe that analytics and the most accurate models are not useful unless the humans who are involved in it can use it. And therefor I think that AI strategy should be very aligned to business strategy and we should start with the needs of humans in the loop. And managing a team of Data Sciencients is managing a team of high performers. It requires you to build your leadership skills and requires exceptional leadership. It means that you need to constantly learn and enable people and all of that is something which appeals to me very strongly.
I do love going into work each day. I love doing what I'm doing and I think that's a good place to be.
Praveen Bhadada: Love it. Love the response. Technology is a great leveler. We hear that very often. It couldn't have been truer in today's context as we have just come out of the pandemic period. I really love your focus on AI Technology, books and better.
I can start to relate to some of those. But I was particularly curious about the last point you made, which is the whole blend between Data Science and Business Strategy. Then obviously, to have successful outcomes, these two have to work in tandem. There has to be a lot of coherence in how these two teams operate or how these two concepts align at an organization level. You know, little bit of a deeper dive in terms of how do leaders like you try and strike that balance? What are the challenges and what are the best practices that you've learned in bringing Data Science and Business Strategy together?
Ranjani Mani: Yeah, that is such a great question. And I don't think it gets asked enough. I was reading this research, I think by Gartner, and they had predicted that 80% of analytics will not deliver business outcomes, I think till 2022 at that point in time. Why do you have so many AI projects which fail? And I think one of the key reasons is that AI strategy doesn't align with Business Strategy and these are usually driven in parallel. And there was this case study which they had highlighted about this organization which illustrates this point about what happens when Data Science and Business don't play as a team sport. So apparently there's this organization and they wanted to build out a Recommender System. So this is for use by their Customer Service representatives. And the idea was to suggest solutions to common problems which customers faced.
Now the project was touted as this big cost saver for the company itself. After they rolled this out, it took them a few months to realize that the agents, the Customer Service agents were not even using it. And then it took them some time to figure out why are they not using it. So apparently they realized that the problem was not necessarily with the algorithm in itself, but the data which the model was trained on.
Apparently it was trained on this ideal set of technical descriptions versus what the customer actually enters, which is very different. And apart from that, they also realized that there are overlapping categories in the data which were not taken into consideration when they were training the model.
So, this is something which I have noticed over a period of time in the work which we do as well, that most data scientists tend to miss it. They don't think about it from a Design Thinking approach, starting with the user and say, what is the problem? What are we trying to solve? And then, how can AI solve it? Are we augmenting it or automating? It usually starts with the solution and then saying, okay, how can we use the output of this model? So I think that's a gap which exists. And I think HBR, you spoke about the sexiest job when we started and then the same publication, HBR, also calls out the need for a role akin to a translator of the business into a technical problem.
Being the storyteller and conveying the value to the business and therefore what I’ve tried to do over a period of time is to hire for complementary skill sets. So not just ML engineers, I hire for Business Analysts. I hire for people from Consulting backgrounds. Again, your hiring strategy depends on your maturity of an organization, but as I kind of see my team, that's something I've consciously looked at.
And therefore I probably would conclude with this. So BCG had this report on winning with AI and the need of integrating AI Strategy with Business Strategy if you need to win. So short answer, yes, how I tried to drive that balance is to start with the customer and the business objective, and then say, okay, what are the problems we are trying to solve, and then think about how can AI solve it in a unique way. So that's something which I've tried to start the over the period.
Praveen Bhadada: So let's talk a little bit about winning with AI. You brought that up as part of the conclusion of the previous question. I think, you know, as you are aware, I get to talk to a lot of CXOs in the global ecosystem and in private conversation while most of them agree that AI hasn't really delivered on its promise, I think there are two reasons why broadly people talk about that. One is exactly what you said, which is, how do you keep customer problems at the center of it and train your model around that scenario of the customers rather than through a technological guideline which definitely is a big problem.
But the hope is that as we get more data, as the data gets cleaner, some of that modeling issues will get solved and as Business Strategy and Data Science teams come together, they will find a way to solve for the biggest, most complex problems that are there in the enterprise. But then there is this second problem that they don't talk about a lot in public domain, but in private conversations they also believe that a lot of people do not want to use AI because there's this inherent sense of insecurity that these AI technologies will someday take over their own jobs quite often faced in Analytics and Data Science space.
You know, when you think of Data Processing and Visualization, the amount of automation on the back of AI that's happening today is mind-blowing in terms of how AI can really perform some of those tasks that in the historic times we would have depended on an Analyst to do that. So, my question to you as someone who's at the center of all of this do you believe that AI can truly take over Analytics and Data Science jobs and how do Data Scientists be really thinking about the next few years as this technology evolves. What are you reading? What are you planning for as a leader in this space?
Ranjani Mani: Before getting into the Data Science space, I’ll probably take a step back. What this reminded me is this quote from Seth Godin’s book… I read a lot by the way, so this is one of the books I really loved, it's called Linchpin, it's by Seth Godin and he talks about the difference between a job and a career.
So he says, a job is what you do when you're told what to do. The job is like showing up at the factory, following instructions, meeting specifications, and being managed. And he says, someone can always do your job better or faster or cheaper than you can, but your art or career is what you do and no one can exactly tell you how to do it. Like connecting the dots, taking personal responsibility, challenging the status quo, critical thinking. And he says that if you focus on doing work and driving your career and focusing on the art, that's how you become a linchpin at your careers.
I thought that was very relevant when I read this, because if you think about it from the Data Science world, if all you can do is code in Python as well as someone else can, then yes, absolutely, you're going to be replaceable because there will always be someone or for that matter some tech which will come up which would do it faster and cheaper and make you replacable. So that applies across skill sets.
So getting back to your question. Yes, AI is disrupting sectors like never before and it is going to happen whether we like it or not. So I would probably rephrase or look at the question not as Data Science jobs getting automated, rather Data Science tasks that could be automated. So let's talk about what AI is automating. And you did touch up on it. Potential areas which can be done without human intervention. Like say, data processing or low level tasks like automated testing, iteration, or monitoring data quality for that matter.
And if you think about it, there was this report, probably by McKinsey, which said that around 69-70% of the Data Scientists’ time is spent on just data collection and processing. That could be automated. So I would say that's probably a good thing, right? Because automation of tasks which are repetitive, manually intensive and which do not require deep science expertise will free up bandwidth for Data Scientists to focus on more value added activities. So that's what I would think of how AI and humans could work in a complementary manner to augment the efforts and use human discretion of things which they need to, like understanding the context of the problem, asking the right questions, or converting an output to an outcome. So in all honesty, I would focus less on worrying about jobs being replaced unless you are in such a role yourself, and more on skills one could add and stack up that could play to your favor. And the sheer number of rules which are there on ML Engineers and Data Scientists is adding credibility to that.
Praveen Bhadada: I love that. I think it's quite thought provoking the whole career versus job narrative of things, something that is very thought provoking. I'm going to definitely pick up that book and go deeper into that is an interesting and unique lens to look at what should be automated and where should humans really focus on what once they have the free bandwidth because of AI. It's a great perspective.
Let's shift some gears here and talk a little bit on the personal side. I follow you on LinkedIn and I recommend everyone on the podcast listening today to follow Ranjani for some real thought provoking, fun content on LinkedIn. There's so much value and education in the content that Ranjani posts on LinkedIn. I've often observed Ranjani that you place a lot of value on empathy and compassion in whatever you think, whatever you write, whatever you do.
So as a leader, I just wanted to pick your brains in terms of how do you think of non-negotiables for leaders? How deeply rooted today is empathy, compassion, things like those? What are some of the other values that you live by as a leader in the tech space and specifically a women leader in the tech space?
Ranjani Mani: Yeah. This is a topic I've thought about offline as I've tried to consciously improve myself also as a leader. So here's the thing, Praveen. So very early in my career when I just started off I had the opportunity to work with two extremely talented leaders. One I absolutely loved and wanted to emulate. And one, I did not want to be when I became a leader and it's good to have those learning because it helps you define what you want to be.
When I first became a manager one of my mentors told me this apart from your spouse your manager is probably the one who has the most influence on how your day turns out.
And that's a lot of responsibility. So if you think about it that's something thing I consciously choose to work towards improving over time. So, if you think about great bosses, what matters is how one is supported, not just at their best, but also at their worst. So coming back to the question, in terms of values, what does that translate to? There are multiple things. But at its core for me, it translates to three key different things.
The first one is to treat people fairly by being nice and being respectful, irrespective of where they come from, what level they are at, and what they do. And that means that for me, ensuring that the teams operate at an equal playing field that everyone has voices and they get heard to unlock people to do their best work and help them live with their potential. If people are happy they tend to do their best work. It sounds like a cliché, but I've seen that over, and over, and over again. So I think that is something which is absolutely a non-negotiable for me.
The second thing is to assume goodwill. And many a projects, most of the time we would think about why they fail. It’s less to do with technical issues. It's more to do with lack of communication. So most folks don't intentionally want to mess up. So it usually tends to be a lack of a common understanding. So one thing I've learned to do over time and I stress my teams to do is to assume goodwill on the other person's part. Because that means that they're coming from the right place and being open to communication and that helps build those bridges over a period of time.
And the third is probably the most important - integrity. And as a hiring manager, I've seen one solid trait across all the great hires I've had. It's definitely not technical skills or business domain or even communication. It's this unwavering sense of accountability and ownership. And you spoke about the different era. And in this knowledge economy, I cannot tell them do A-B-C. It's very complex problem solving. It requires multiple teams to come together, collaborate, and work in ambiguity.
And if you're working in such a scenario, it requires integrity. It requires you to do the right thing even when no one is watching, so integrity in yourself and the teams who work on, I think inculcating that as culture or something is a non-negotiable for me as well. So probably those are things which I would put my money on.
Praveen Bhadada: Love it. So just to round up the serious part of the podcast and my final question, and then we'll get into a bit of a fun section, what are the top three tech trends that you would put your personal money on, if you think of the next three to five year timeframe? What are you most excited about?
Ranjani Mani: I love understanding what's going to happen in the future. One thing about tech is that we think linear but Tech moves exponentially, which is very important to understand. There are a lot of areas which are going to change, but the ones which I'm very curious about and probably going to put in my money on is (a) on Web 3.0. The entire crypto, decentralization, Blockchain, and the technologies covering that 3.0.
So I think, Chris Nixon has this brilliant article, which I would recommend everyone read, about why Web 3.0 matters. And he talks about how right now we are in the beginning of this web 3.0 era and how it combines this decentralized ethos of web 1 with the functionality of web 2. And so I think, personally web 3 is bringing in the best of both the eras.
Why is it important? Just because it's going to put the power in the hands of the creators and the end users. Now that means that it’ll cut down on the middleman and potentially rewire the way the business models exists today. And that's going to bring in a completely new work stream of technology and companies working towards it. So again, it's an area I'm not an expert on, but something which I'm definitely invested in learning more on because that's the power of Tech which I believe in.
The second is probably Clean Tech. I was reading the report which called out that by 2025, probably the World Economic Forum, that the carbon footprint will be viewed as socially unacceptable. Much like drunk driving today is unacceptable. So I think not just individuals, but companies and countries will look at ways in which, or affordable ways in which they can achieve their next goal and how they can eliminate their carbon footprint. Then you see that around. You see the focus on Green Tech around. So what this is going to probably bring in is this diversity of new technologies which are aimed at both reducing and removing emissions across the world. So when you focus on things like this, it will unleash a wave of innovation, similar to what you called as industrial revolutions in the past.
And this is something you would be proud of innovating as well. That's something I would love for it to grow. Finally, maybe, the convergence of all the technologies. We tend to think of them in silos, not just linearly, but also in silos. So Peter Diamandis has this book around how the Tech is going to move in the future. And he talks about exponentially converging technologies across AI, and Quantum Computing, and Cloud and 5G, and IOT, and Ar-VR. I think all of them coming together and he talks about how they transform every industry.
So that's the other thing. When we think about tech, how can we think about them coming together. That's something I've consciously tried to learn more about. So I do that. Those are the top three, but maybe one thing I do want to touch upon is Metaverse. This is something I acknowledge I know this happening, this blurring of physical and virtual spaces. But I don't know where I stand on it today. So I’m reading this book called Snow Crash by Neal Stephenson and the book is the first time the term Metaverse phrase was coined. It borrows it from that book. And honestly, first of all, it was written in 1995, so it’s phenomenal that he envisioned something like that.
But then it's very intriguing and worrying at the same time. And I know that that is something which is happening and something which we should be aware of and learn more about if you are in this space. So to end, I think in Tech in itself is intrinsically not good or bad. It depends on the intentions of the user. So I hope we work on building a world which we would love our kids to live.
Praveen Bhadada: I think we've come to the fag end of the podcast.
I just have a quick rapid fire round to do with you, just simple questions in terms of your current preferences, et cetera. So I know this might sound like a repetitive one, but I'm very curious to know what are you currently reading? What is the most favorite book that you have in hand that you are not able to leave?
Ranjani Mani: Reading is always a fun thing to talk. I tend to read in parallel, I tend to read two three books in parallel. Otherwise I get bored. So one, like I said, I'm reading Snow Crash. It’s sci-fi, but highly recommend it if you are interested in the genre.
The second I'm reading is Behave, it's by Robert Sapolsky. So he talks from the neuroscience perspective in terms of why we behave the way we think, what drives our actions. And it's kind of dense in terms of science, but very interesting in terms of unpacking our behavior as human beings.
Praveen Bhadada: Very nice. Very nice. What's the one favorite restaurant in Bangalore that you'd recommend to everyone?
Ranjani Mani: Honestly, I am not a foodie. So, I usually go to places which are open. I think the recent one I’ve been to Pump House and I quite like it.
Praveen Bhadada: The words that you live by every day?
Ranjani Mani: Yeah. Quite a lot, but okay. Maybe if I have to pick one, ‘Do what does right. Not what is easy.’ Maybe that's what I would think.
Praveen Bhadada: Wonderful. Lovely. Thank you so much, Ranjani, for this fun session and really appreciate you taking out time to talk to us today and share very candidly your thoughts and opinions across a range of questions, some serious ones and some not so serious ones. I really enjoyed the conversation, the best part for me was how you link everything to what you’ve read and such lovely recommendations of books that I'm itching to just go to Amazon and order all those books for myself. So really, really appreciate you sharing all the insights and best practices. And I loved everything that you talked about on the podcast.
Ranjani Mani: Brilliant. Yeah, I absolutely enjoyed this conversation with Praveen and all of your back and forth on this and your thoughts on this as well. Brilliant conversation. And as always, I highly recommend the books that I hope you folks pick up, please feel free to drop a note in case you found any of them interesting. Thanks for having me here, Praveen. I quite enjoyed this.
Praveen Bhadada: Lovely. Thank you so much.
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