19. Bridging The Gap Between Good Enough & Full Throttle - A Talk With Radhika Rangarajan (Women in Big Data)

Join us for an insightful talk about the powerful philosophy of 'Bridging The Gap Between Good Enough & Full Throttle' with Radhika Rangarajan, Co-founder and Executive Director of Women in Big Data.

In this episode, we will unpack Radhika's philosophy of 'Bridging The Gap Between Good Enough & Full Throttle', which is rooted in three fundamental principles: purpose-driven innovation, accessible excellence, and scalable impact. Tune in to discover how these ideas can guide your journey toward making a difference!

• 1:19 - The importance of the philosophy: 'Bridging The Gap Between Good Enough & Full Throttle'.
• 5:10 - Where does Radhika's philosophy come from?
• 6:44 - Artificial Intelligence and 'Bridging The Gap Between Good Enough & Full Throttle'.
• 9:33 - Who's a woman, or a group of women, in big data that Radhika deeply admires?
• 10:21 - How did Women in Big Data start?
• 14:46 - What is the best career advice that Radhika has ever received?
• 19:06 - What 3 ingredients go into Radhika's successful career recipe?
• 21:27 - The most meaningful innovation comes from pausing to ask 'why'?

Guest Info

• LinkedIn: Radhika Rangarajan

Chapters

• 0:00 Intro
• 1:19 The importance of the philosophy: 'Bridging The Gap Between Good Enough & Full Throttle'.
• 5:10 Where does Radhika's philosophy come from?
• 6:44 Artificial Intelligence and 'Bridging The Gap Between Good Enough & Full Throttle'.
• 9:33 Who's a woman, or a group of women, in big data that Radhika deeply admires?
• 10:21 How did Women in Big Data start?
• 14:46 What is the best career advice that Radhika has ever received?
• 19:06 What 3 ingredients go into Radhika's successful career recipe?
• 21:27 The most meaningful innovation comes from pausing to ask 'why'?
• 22:43 Outro

Transcript

[00:00:00] Intro:
Hey, Hello, Welcome to the Women in Big Data podcast, where we talk about Big Data, Analytics and Career Topics. We do this to connect, engage, grow and champion the success of Women in Big Data. 

"But there it is actually technical work or whether it is community work. What can we do better? Is this really resonating with who we are trying to serve? And that's some question that I've been asking and then going back to this good enough and full throttle: that sweet spot where purpose meets possibility. For me, finding that spot, the courage to slow down, to listen." - Radhika Rangarajan

[00:00:39] Desiree:
In this episode, we talk with Radhika Rangarajan about 'Bridging The Gap Between Good Enough & Full Throttle'. 

Radhika is Co-founder and Executive Director for Women in Big Data. 
With over two decades of experience working in tech, Radhika has a talent for turning complex challenges into scalable opportunities. However, her journey is not only about building solutions, it's also about building bridges. 

Let's start.

[00:01:05] Desiree: 
Radhika, welcome to the podcast. It's great to have you as a guest. And I'm excited to talk with you about 'Bridging Good Enough & Full Throttle'. 

Can you describe the aha moment when you realized the importance of 'Bridging The Gap Between Good Enough & Full Throttle'. 

[00:01:22] Radhika: 
Well, first of all, thank you for having me here Desiree.

Before I jump into the aha moment, I want to take a step back because I often get asked what I really mean by 'Bridging The Gap Between Good Enough & Full Throttle'. I know it sounds like an excellent tagline, but there's a little more to that. When I think about it, it actually comes down to three key principles: purpose driven innovation, accessible excellence, and scalable impact. 

You think of it like learning a new language in today's digital world. Good enough might mean just relying solely on basic translation apps for occasional use, but then full throttle could mean trying to get mastery of the language, the literary aspect, the colloquial fluency all overnight. Now the sweet spot, we all know this, lies in actually building that thoughtful journey between the balances, immediate needs versus long term capability. So, hopefully the language analogy helps people understand what I mean between good enough and full throttle. 

And when I put that in actual context of a technology world: good enough would actually mean implementing quick solutions. And we see a lot of that today, where you're trying to solve immediate problems, but then you're limiting the future growth. But then full throttle, on the other hand, could be pushing cutting edge technologies just for the sake of it, sometimes at the expense of practicality, at the expense of adoption.

Now, sweet spot here is lying bridging between these approaches, right? What is the problem we really solving while ensuring the solution is relevant and accessible. I've had many aha moments throughout my career, but one of the most recent ones from my time at VMware, where I was leading cloud foundation adoption: we were seeing two extremes organizations that are just trying to do basic lift and shift to move their workloads to the cloud. That's good enough. But then we were also seeing organizations where they were trying to rebuild everything from scratch. That's like the full throttle approach. Both were failing, epic failures on both sides and for different reasons. Because enterprises are trying to settle for simple cloud migration or they were trying to rebuild everything from scratch.

Now, what my team, we ended up doing was the bridge was what we called us vMware validated solutions. That wasn't just about technology. It was truly about actually pausing and asking: why? Why does this customer want to migrate their workloads? Why does this particular organization need this? Are they even ready for this new technology? And why are they failing in what they are trying to implement? And why is it that some customers were being successful, but others were struggling? 

So, when we paused and we actually asked these questions, we were actually developing answers for these use cases. So, we were able to develop prescriptive solutions for customers who are trying to do identity and access management, who are trying to build their own private cloud automation, who are trying to do monitoring and logging - specific answers to specific questions.

And when we did that, we saw customers were actually able to accelerate their adoption to the cloud. And I'm talking orders of magnitude improvement, two to five times improvement while they were able to maintain the enterprise quality. What I'm trying to say is: success is not just about choosing extremes, but it's about building these bridges between them.

[00:04:50] Desiree:
Okay. And then understanding what kind of value you are bringing. 

[00:04:54] Radhika: 
Exactly. 

[00:04:55] Desiree: 
What does the customer really want? 

[00:04:56] Radhika: 
Yes. 

[00:04:57] Radhika: 
Know what your customer wants and meet them where they are, not where you are. 

[00:05:03] Desiree: 
Yes, that's very clear. 

And we have now talked about bridging the gap, but how did you come up with this philosophy? Where does it come from? 

[00:05:12] Radhika:
Well, it comes from a lot of learning experiments, both personally as well as professionally. When you're trying to learn something new: do you just stop short at just learning the basics? But if you want to truly experience something, whether it is a personal hobby, or whether it is actually a technology, you need to go beyond the starting point, right?

[00:05:35] Desiree: 
So, out of your comfort zone. 

[00:05:37] Radhika: 
Exactly. 

The real learning comes when you truly get out of your comfort zone. Now, obviously you can get extremely over ambitious and try to learn everything overnight, which we know doesn't happen that way. So it's all about taking those small steps, taking one step at a time, but actually being consistent throughout the journey and finding what the middle ground is. Why am I doing what am I doing? And am I heading in the right trajectory? 

[00:06:06] Desiree:
So, actually what you are saying is it's a journey. And first of all you need to understand where do you want to go. And then, think back and make a plan step- by-step what you need to do to get there. 

[00:06:19] Radhika:
Yes. 

And sometimes you may not have all the steps figured out, but if you're able to answer your why, and you have the conviction and the passion to actually follow through with it, you may not have clarity. It can be an extremely ambiguous journey, but those are the most exciting ones too, right? 

[00:06:36] Desiree:
Definitely. 

So, if we now look at AI, what does it mean 'Bridging Good Enough & Full Throttle' in the age of AI?

[00:06:44] Radhika: 
Now you're getting me very excited about this because, honestly, I see this philosophy becoming more crucial than ever because organizations are treating AI adoption like as if it's a race. There are AI wars being fought, rushing to implement the latest tools out of fear of being left behind. But just we wouldn't expect someone to become fluent overnight in a language: AI adoption, it's not a race to be won, it's a journey to be navigated. And it's a journey to be navigated thoughtfully, even more because of the implications of biases. 

So, when I think about how companies are trying to actually adopt AI, just because it's the latest shiny tool and not truly understanding: is this actually serving the purpose for my customer base? Then we are seeing fancy, fun use cases getting implemented, but are we seeing purposeful use cases out there? There's plenty of wonderful use cases out there that are actually evolving, but are they being crafted in a thoughtful manner? Because we all know it's data that actually powers AI. What kind of biases are in the data?

So, it's even more critical that organizations pause and actually understand where they want to be in the spectrum between good enough and full throttle when they are trying to do this. So, always starting with a clear purpose, understand what is the challenge we are solving, focus on augmenting that. Then replacing it with something fancy. Then build on the proper foundation. And then you have to think about sustainability and scalability. Enable sustainable scaling. You start with a focused pilot. Start with something small. And then continue to create feedback loop. Learn. Learn from that experience for continuous learning. And then you can actually continue to scale.

In the world of AI, what works for one sector isn't necessarily going to work for another. I think each vertical needs to develop its own AI fluency based on their specific needs, right? What is required for healthcare as critical aspects may be different for FinTech.

So, just like a learner language, start with basic phrases. Gradually build the capability. The key is creating the thoughtful journey, that mindful journey that brings everyone along. If enterprises are not bringing their customers along, then there isn't a sustainable business model here. 

[00:09:23] Desiree:
I absolutely agree.

Let's go to the next question. Who's a woman or a group of women in big data that you deeply admire? 

[00:09:30] Radhika:
I'm actually happy that we are at a place where I can cite a lot of women that I admire, especially if I want to think about specific industry pioneers, names that come to mind are Fei-Fei Li. She has revolutionized computer vision and championing AI education. Daphne Koller, the work she's done in terms of co founding Coursera, democratizing tech education. 

There's a lot more women who I absolutely admire. The women who inspire me, it's actually a remarkable collective. The volunteer leaders at Women in Big Data. Leaders who choose to create impact beyond their day jobs. And that's what makes it very special. 

[00:10:10] Desiree:
Let's take a step back. 

[00:10:11] Radhika:
Yes. 

[00:10:12] Desiree:
How did you start with Women in Big Data? Because I think a lot of the listeners still don't know this. How did it start? 

[00:10:19] Radhika:
It started with actually a simple question at Intel. 10 years ago, I was in the open source big data team and Intel was investing a lot of money in accelerating, improving diversity and inclusion within the groups. But somehow in our group, besides me and obviously one of our co founders, Shala Arshi and my boss, we were looking around saying, well, where are all the women?

And we went to our VP, Michael Green. And he said: you know what, I hear what you're saying, but is this a problem that's just specific to us, to our group or to Intel? Or do you see this everywhere? Why don't you actually reach out to your network and let's do something about it. And then we reached out to a bunch of our network within the Bay Area and we called for a meeting about 15 women.

This is that epic 15 women team meeting at Intel conference. We went from SAP, IBM, Cloudera, Oracle. That first meeting had a pretty good cross section of companies. And we were like: okay, do you see a lot of representation in your teams? Like, well, you're looking at us. So, then how do we actually bring diversity in this rapidly evolving tech space?

And one of the things from our personal experience and from my personal experience was that even though I had worked for about a decade prior to joining this team, it was extremely difficult for me to make that transition. Even though I had a master's in computer science, I was doing hands on development, databases was not a new concept for me, but why is it that there was actually not much awareness that even I had about what was happening in this evolving space?

So, we said: okay, maybe we need to talk a little more about skills, transferable skills. Opportunities in this space and maybe even actually: how do you uplevel yourself? More training in the space, more networking. So, we looked at it and we said: okay, then this is what we need to do from our personal experience. And essentially that was the beginning of that first chapter of Women in Big Data. 

And we've done workshops. We've done mentoring. We've done a lot of things over the last 10 years, and we have democratized what it means to be community change makers. So, we are a global community, but extremely localized, and that's all possible because of these volunteer leaders, 120 volunteers leaders. They are choosing purpose over time. And they don't just share their expertise, they are actually igniting a movement. 

[00:12:41] Desiree:
That's very clear. 

Well, I had another question. What is the vision of 2025? What can we expect from Women in Big Data? 

[00:12:52] Radhika: 
Oh, 2025, guess what? It's our 10 year celebration. It's our 10th year of coming together as a community, June of 2025.

I anticipate us having a global celebration of not just our achievements, but also inspiring the young leaders who are not at part of communities like ours to be part of this. 

[00:13:15] Desiree:
So, strengthen the connections. 

[00:13:17] Radhika:
Absolutely. Yes. 

But beyond that, in terms of programs, mindful programs that we have actually introduced the last few years, right?

We have done a phenomenal job at introducing our flagship mentorship program, which has had one of the most profound impact on all our community members. Every year we are bringing in several hundred mentees through this program. I anticipate us scaling that this year. 

Hackathon, which we just started with last year. I would love to see us participate and connect and network with more companies and solve for more use cases this year. 

We started our focused technical training with DataCamp donates last year. And we are expecting that to grow significantly again this year. 

I think what I would love for us to see in 2025 is: in a world where AI is accelerating at a breakneck pace, this is a community that is poised to actually talk about responsible AI development. We are going to be doing some very creative work in getting the message out on what it means to be a responsible AI developer this year. 

[00:14:29] Desiree:
It sounds wonderful. I'm really looking forward to 2025. 

So, I also would like to ask you: what is the best career advice you ever got? 

[00:14:37] Radhika:
That actually I've received a lot of good advice over the years, but I would say the most crucial advice comes from the most uncomfortable feedback.

I think it was 2012 or 2013. Several unsuccessful attempts on my part to get that next promotion. And I did something that was quite uncharacteristic for me at that time. I wrote an email to Intel's CIO because I was listening to her speak in one of the town hall meetings where they're talking about how much investment they were making in people growth.

And I actually said: you know what, I'm going to write an email saying, I'm not feeling it. And I expressed my frustration. I was thinking, what's the worst thing that could happen? I thought the best and the worst thing that could happen is my email would get ignored, but at least I tried. But I didn't expect her response, but she responded.

She responded within a day's time. And her response was both unexpected for me, but it was also very transformative. She sent me a note wanting to know a bit more about me, because I was one among 8000 people in her organization. And we went back and forth, and with a little more background on my side, and after having explained how long I had been in that particular role, which was, I think it was seven years in that business unit. She came back and she said something. And she said: you know what, I think Radhika, I think you overstayed your tenure in this business unit. And she questioned if I had considered moving to another business unit or even leaving the company. 

Now, just pause and think about this. Normally you would expect corporate leaders to actually tell you: oh, we are investing this. We will connect you with mentors. We will do that. What she said, I thought: well, wait, this wasn't the sympathetic response I was expecting. But was exactly what I needed to hear. Because if after repeatedly trying, I was not actually get the promotion or get the growth that I was seeking, perhaps it was time for me to accept that I had probably reached my glass ceiling within that business unit for that role, and I should be looking elsewhere. It was time to go. Rather than someone placating you and telling you: no, stay here, we will find a way for you. What? To get the next small incremental growth, and then once again have the same frustration. Here was a leader was telling me: maybe it's time to pack up and look elsewhere.

I negotiated a transfer out of that business unit three months from that email. And actually I was surprised. I was thinking to myself, why hadn't I asked about this earlier? I was able to get a rotation out of the business unit because our leaders felt that there was no way for me to grow there, mainly because of the limitation of the strategy for the program that we were involved with.

And that led to the most rewarding experience for me in my career, because that was my foray into big data. If I had not done that, I wouldn't have had a chance to actually enter into the open source big data space, even within Intel. I spent another wonderful seven years after that at Intel before I left, but that experience taught me several things. I want to share that. Always be curious. What happened to me? Why had I stopped thinking about new opportunities? And why was I wearing my blinders about what was within that space? So, I was actually not exploring that. So, always be curious. Always be relevant, right? The best growth opportunities come from perspective and complete change sometimes.

Always understand that if you're staying too long in one place, then you're probably limiting your growth. Always be learning, and especially in this world where technology is evolving at such a fast pace. If you don't learn, if you don't adapt, then you can be irrelevant. 

But the most important of all of this was seeking that honest feedback, which is very uncomfortable. And sometimes feedback and the best career advice doesn't actually validate your frustration. It actually challenges your assumption. 

[00:18:23] Desiree:
It gives you something to think about yourself, and to figure out what am I doing? 

[00:18:27] Radhika: 
What am I doing? And it's going to actually push you into some uneasy spaces. The approach to seeking growth through change, right? It's going to be an uncomfortable journey, but I think it's a journey worth taking. So, I would say that was one of the best career advice: get feedback to seek growth, not to seek appreciation. 

[00:18:45] Desiree:
Wonderful. 

Another question I have is also about career advice. What three ingredients go into your successful career recipe?

[00:18:56] Radhika:
I actually associate success with a lot of my happiness quotient. I always relate that to do I get the satisfaction? Do I get the joy out of what I'm doing? That's always been important for me. I'm an extremely passionate person. I wear my passion on my sleeve and I make no excuses for that. So for me, the number one thing is lead with authentic passion.

I actually recall one of my Berkeley professors. He pulled me aside and he said: you know what, Radhika, your passion is your strength; at any point in time, somebody asks you to dial down your passion, you should just quit that immediately because that's your strength. 

A second thing I would say is embrace all parts of you. And I wasn't this way when I started off. When I started off as a young software engineer, I thought I had to fit into the definition of mold of what an engineer needed to look like. Well, I know better now. And I think I'm actually better for having learned that. I would say that: it's extremely exhausting when you're trying to be limiting. So embrace every aspect of you because all of those are the different secret ingredients of who you are as a full person. And that helps you to bring your best to anything. 

A third, through your meaningful connections, you will learn your strengths. My 10 years with Women in Big Data has been the most rewarding experience of my professional life. The connections, the friendships that I learned. through this last 10 years. They've also helped me understand who I am. They've also allowed me to lean into my strengths as opposed to actually trying to compensate for my weaknesses. 

My strength was I could bridge different worlds, technology and people. That's my sweet spot. Solutions and needs, expertise and accessibility. My community has helped me discover that. 

[00:20:49] Desiree: 
Okay, I understand. 

Radhika, is there anything else that you would like to share with the listeners that I didn't ask you yet? 

[00:20:57] Radhika:
In technology, we often talk about moving at lightning speed. It's all fast, right? Especially with AI. And that is constant pressure to keep up, to stay relevant, to be ahead of the curve, ahead of the game. But the thing that I've learned by hitting the pause button is the most meaningful innovation always doesn't come from racing forward. It actually comes from pausing to ask 'why'? Why are we building this? How will this make lives better? 

Whether it is actually technical work or whether it is community work, what can we do better? Is this really resonating with who we are trying to serve? And that's some question that I've been asking and then going back to this good enough and full throttle: that sweet spot where purpose meets possibility. For me, finding that spot, the courage to slow down, to listen. You have to build bridges rather than just features. I see this clearly now, obviously, after stepping back, having the privilege to take a short break 25 years after corporate tech. And I actually fully intend to get back because I love technology and building things.

Look, technology keeps evolving, that's a given. But our ability to make it truly meaningful, to make it accessible and inclusive, to ensure it serves everybody - now that's the real challenge ahead of us. 

[00:22:13] Desiree:
Thank you very much for that Radhika to share your journey and how you are 'Bridging Good Enough & Full Throttle. Thank you very much for that. 

[00:22:21] Radhika:
Thank you for this opportunity.

[00:22:22] Outro:
Thanks for listening to the Women in Big Data podcast. For more information and episodes, subscribe to the show or contact us via datawomen@protonmail.com. 

Tune in next time!