Evolving the Enterprise

Empowering Diversity in Data Science with Raoushan Ummulwara, Data Analytics Manager at Chevron Phillips Chemical Company

• SnapLogic • Season 3 • Episode 6

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In this episode of the Evolving the Enterprise Podcast, host Dayle Hall welcomes Raoushan Ummulwara, Data Analytics Manager at Chevron Phillips Chemical Company, to explore the multifaceted world of data science, business intelligence, and the importance of diversity in the tech industry. Raoushan shares her journey from a computer science graduate to a data analytics manager, highlighting her experiences and the challenges she faced as a woman of color in a male-dominated field. The conversation delves into the evolution of business intelligence and its crucial role in strategic decision-making, with a particular emphasis on the value of diverse perspectives in identifying biases and fostering innovation. Raoushan also discusses the significance of data governance, the balance between data access and security, and the impact of generative AI on data science roles. Throughout the episode, the importance of continuous learning, adaptability, and starting with a clear understanding of the business problem is underscored, offering valuable insights for both seasoned professionals and those aspiring to enter the field of data science.

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Empowering Diversity in Data Science

Dayle Hall:  
Hi, and welcome to the latest episode of Evolving the Enterprise Podcast brought to you by SnapLogic. I'm Dayle Hall, the CMO. Here we explore the latest trends and innovations that are shaping the world of business and technology. I'm thrilled to have a very special guest today. We're actually driving into the realms of business intelligence, data quality, and something that I'm sure is near and dear to our guest, but to other people out there, the realities of a data science career. We'll get into that in a second.

To help us navigate these topics, we have a special guest, Raoushan Ummulwara- hopefully I didn't butcher that, and she's going to tell me if I did in a second- who’s a data analytics manager at Chevron Phillips Chemical Company. She brings a wealth of experience and knowledge to the table, and we're excited to hear her perspective. Raoushan, welcome to our podcast.

Raoushan Ummulwara:
Thanks, Dayle. You did not butcher my name. Again, happy to be here on the podcast and looking forward to our chat.

Dayle Hall:  
That's great. Thank you for joining us. We're actually recording this in March, which, as most people know, is Women's History Month. On this Friday, I'm hoping we can actually get this podcast done and turned around, and we can release it actually on International Women's Day. But as we celebrate these achievements, it’s great to have you on here. So I thought wouldn't it be interesting to start with, as a female leader, this crazy tech world? Is there someone that has inspired you over the years? Is there another female leader who you think who you look up to and has had a big impact on your life that you could share with our listeners?

Raoushan Ummulwara:
Yes, Dayle. The name that comes to my mind is Susan Shields. She is amazing. She is a CEO and founder. She has been a VP. She has a wealth of knowledge. We both crossed paths in our Harvard Business Analytics program. And since then, she is my mentor, she's pretty much whom I look up to from any perspective as well. So a big shout out to her.

Dayle Hall:  
That's great. I'm going to definitely go and check out her profile afterwards. Well, look, before we get started, I think it will be interesting, Raoushan, give me a little bit about who you are, your background, how you ended up in this specific field and at an amazing company like Chevron Phillips.

Raoushan Ummulwara:
Sure. I come from a pretty technical background. My education has always been in the field of computer science. I have a bachelor's and master's in computer science. And this is, again, [take it back]. Since then, I landed in the field of business intelligence right out of college. I did not even know what is business intelligence to begin with. Landed in that field, and since then, I have played various roles in the field of data. I've always been in the field of data just playing different roles. Very recently, I joined Chevron Phillips in 2023 as the data analytics manager for finance, and it's been a journey since then. But yes, the early career has been in consulting versus I have been in Houston, Houston oil and gas industry. I have been in oil and gas industry for the past 10 years.

Dayle Hall:  
Was there something specifically about the company you just joined, Chevron, that really attracted you to the role? Was it that they're doing industry-leading things, could definitely benefit from your skills? What makes someone like you want to jump in that type of role?

Raoushan Ummulwara:
It's a very interesting role. I have always been a part of the IT world and the technology world. This role is very different. It's part of standing up the center of excellence for data, but it's for finance, for all the finance. And finance comes like security, data security, data governance, data quality. These are such an essential standard. And also, I was looking from a very different perspective, from a business perspective. I always look things from an IT perspective, but just staying in business, staying so close to business. It's very new for me, and when this role came upon- and of course, Chevron Phillips has a great culture that attracted me right on when I talked to the folks, yes.

Dayle Hall:  
Our CEO has this phrase, and you probably heard it before, that culture eats strategy for breakfast.

Raoushan Ummulwara:
Yes, culture is so important, I feel, no matter what you do or any initiative. CP Chem has a very great culture that I can speak to.

Dayle Hall:  
That’s great to hear. Look, we have a few topics that we're going to go through today. Business intelligence and how it helps around decision-making for the enterprise is where we're going to start. We're going to delve into the women in tech in a second, but maybe just give me some background. What does business intelligence look like where you are now, or even over the past? And how do you think it has evolved over the last 5 or 10 years in really helping, making better decisions for the enterprise?

Raoushan Ummulwara:
Business intelligence, how it started off, again, throughout my career, I have witnessed the evolution of technology and its profound impact on business strategies. I'm an immigrant, first thing. I came to US-

Dayle Hall:  
You and I both, Raoushan.

Raoushan Ummulwara:
And being a woman of color, it brings you its unique challenges in the technology field. The technology field is a very male-dominated field.

Dayle Hall: 
I have noticed that, yes.

Raoushan Ummulwara:
Some of these challenges are, when it comes to business intelligence, what I feel, going back to the question, is as a woman in tech, I bring a unique perspective. And how that would be is diversity. It's very crucial in decision-making. Everyone comes with a unique perspective. Again, emotional intelligence is very big in business intelligence, thinking through different perspectives. And just having that holistic lens when you approach business intelligence, in my experience, has been crucial for decision-making.

Dayle Hall:  
Yeah, that's interesting. I like what you said, diversity is crucial in decision-making. Can you tell me in your perspective, in general, but also maybe some of your own experience, how do you believe those diverse perspectives, those of immigrants, person of color, male versus female, how has that enabled you to have success? What do you feel like you've benefited, or at least helped the company, because you have a diverse perspective?

Raoushan Ummulwara:
Sure. Business intelligence, it's like a backbone of enterprise data decision-making. I always say business intelligence goes beyond reporting, basic reports. It gives you a meaningful insight, I say. Business intelligence is all about giving meaningful insights that drive strategic initiatives. One example that I can think about is just take for one example of diverse stakeholder input. You have different stakeholders, and I work with multi-regions, all across the globe. The input that you get, and cooperating that feedback from individuals who come from different backgrounds, different genders, age, cultural perspectives, it gives you a very unique perspective of what is business intelligence, just giving a high level- for example, launching a product.

If you take in that different perspectives or that different feedback from different sets of people, it gives you data points of very unique perspectives. Something that works for someone does not work for something. Someone can think very differently because they come from very different cultural backgrounds. Just in that, and that shapes your strategy. That is what I have learned over the period of 17 years of this career.

Dayle Hall:  
Is that something that as you go forward, and I'm sure you have the opportunity to advise growing leaders, and again, culturally diverse background, is that something that you're a proponent of now, that you would advise people to have a very diverse background? Even on something as- because people would say like, the data is black and white, the diversity, less of an impact. But it sounds like you disagree with that.

Raoushan Ummulwara:
I disagree. I mean, diversity is so important. It helps you identify biases. In set of data and set of anything that your bias is such a big issue that we always have to keep, it's like a blind spot. You always have to identify your biases. And it's like identifying your blind spots and explore new possibilities. I would never say that it's 0 or 1. I know it's in a technical world, 0 or 1, but I wouldn't say that. Just to think about emotional intelligence, ust like humans have emotional intelligence, it plays a very huge role when it comes to diversity and having different perspectives, just not in the field of data. I feel like in any field, just having that all throughout, not only within a team but all across, just gives you that unique perspective and insights.

Dayle Hall:  
In your experience over the last 17, 18 years as you've been working, even before your current company, do you think that's universally accepted, universally understood? Or do people still need a little bit of a push or prompt to understand what diversity could bring to, you call it the backbone? There's no [inaudible], but you’re also saying the person's role is also just as important. Do other people see that, or is it unique?

Raoushan Ummulwara:  
I have seen companies recognize it, but I feel we need to do a lot more in that area. It's good we identify- the half problem is solved when you identify the issue, but you also need to put some processes and controls in place to make sure that we act on top of it. Either in terms of analyzing data to look for your biases and rectify those biases is important, or the other thing is look for those DE&I metrics, how those impact across. I know Chevron Phillips is huge on DE&I, and that’s something that has attracted me in the company, someone who's really focused on it. But I also have worked in other companies where we talk about it but not act on it. I do see that as a gap in that.

Dayle Hall:  
Yeah. Anyone that's listening to the podcast, I try and think about what would they take from the discussions that we have. I think sometimes, you kind of hit the nail on the head, I think people do a lot of talking about it. I think people want to improve situations, but I also think they need people like yourself, people that have experience. And like anything, they need- so people just need the proof. They need to see how it impacts, the positive impact. I think that would move them faster. How was your experience, your perspective really helped to change people's minds over the years? Have you been able to show them the proof? Whether it's through a person of color, whether it's through male or female, how have you been able to show them that they can definitely still get advantages and a little bit broader?

Raoushan Ummulwara:
That's a very tricky question on how to show the proof. Something that I always try to incorporate is adaptability in techni industry. Adaptability is key and continuous learning, especially in ever evolving landscape. We used to call it BI. Now we are calling it analytics. We are going to call it something else. There's so many branches now in terms of automation, in terms of visualization, in terms of data quality, again, making DE&I as one of the important metric that is always discussed. And showing data to back up how much we do on the DE&I metric from a company level always gives everyone that perspective on what the company is doing towards it, and making it an important metric and capturing that metric from a data perspective, either it's you’re hiring data or it's how you're trying to include it in your workflows.

But showing that to people on what a company is doing is important, making them aware what is DE&I. Not many people would even know what is DE&I metric that companies try and measure, but making them aware and talking about it, I think, from a company perspective. When I talk to my team, building a diverse team is important. When I'm always thinking about my team, creating the culture of respect, having feedback, providing that, looking from different lens, and being valued and heard is also very important from a team's perspective for me. So I make sure that I always incorporate those things when it comes to my team, but also from a company perspective, what I'm looking for.

Dayle Hall:  
Yeah. Look, I think that perspective on the team, having diversity, and anyone dealing with any kind of data is just as important as the data itself. You specifically mentioned bias in the data. That kind of moves us on to let's talk about data. When you say bias in the data, for someone in your role at a company, what do you mean by bias in the data?

Raoushan Ummulwara:
For example, we take data and we derive insights. Let me give you a small example. For example, it's a very silly example probably, and I'm pretty sure I can do much better in the example. For example, a particular role was always filled in by a male, and we are trying to predict who is the best fit. We have a group of interviewees, which are male and female. Your outcome will always be a male because the data that you're deriving insights is always a male candidate. So you have to know what your biases are in the data. You have to make sure that based on what data you're deriving insights should not be biased. In this case, it is a very biased data set that you are looking into. It's a very small example, but it's a relatable example.

Dayle Hall:  
In your example, it's not just the bias, isn't it? You mentioned this before, having different people in the team brings diversity, but also having that clear, understood attitude that some of the data is going to be biased because of the historical aspect.

Raoushan Ummulwara:
Yes, because of the historical aspect. And understanding that is a key. If you understand that, how your past population of data has been, based on what you're trying to predict, is also very much important in the field of data and in the field of insights is very important. Those are some of the biases, and those can be found in pretty much all kinds of data that I can think of.

Dayle Hall:  
So then the next question is, if we recognize that you need diversity, you referenced there's some bias in the data, how do you ensure that these perspectives are considered when you're assessing or looking to improve the quality of turnover, ultimately improve business outcomes by recognizing this? How do you address that?

Raoushan Ummulwara:
Data quality, again, it plays a crucial role. It's about trustworthiness of the data and reliability of the data for decision-making. Also coupled with completeness of the data. It's a very hard question to answer. First is recognizing that you have a problem in the data set, that is hard. It starts always from what are you trying to answer? What is the insight that you're trying to answer? What is the question that you're trying to answer? And making your way back, it's very much like reverse engineering, working your way back to your data and understanding your data set. Most of the tools nowadays in the market gives you very good analysis of if you have outliers in your data, if you do not have outliers in your data, how does your data population look like? What are your unique factors? Making that data analysis is very much important to help organizations make those informed decisions.

And some of the mistakes can be costly mistakes by organizations. If you want to avoid those costly mistakes and improve overall your data quality, understanding what you're trying to answer, having the question clearly defined your business problem and making your reverse engineering your way back to the data population, and what are you looking at is something very crucial in this process. Again, some of those industry-level terms, I would say, you want to make sure your data is unique. You want to make sure you have integrity in your data. This is like authorized data, data security. Data plays a crucial role in accurate reporting. But also understanding what is your data is very important.

Dayle Hall:  
Yeah. It's interesting because I do hear that a lot on these podcasts that the smart people like yourself and people who are trying to solve problems, whether it's with data analytics, with what we're seeing now with artificial intelligence, everyone comes back to always start with the business problem, start with what you're trying to solve. And then you use the term reverse engineer, going back to look at the data, which I think is really important that everyone understands that, because I think there are still people probably out there, there are projects where they set off on a path and lose the the focus of what they're trying to solve for. And I think you have to keep coming back to that.

Raoushan Ummulwara:
Yeah. You always have, like you mentioned, the goal in mind or the business problem in mind, what you're trying to answer, and then you can shape your way through the data. Of course, when you have data, there will always be question about accuracy. There will always be question about completeness. Doing your due diligence, I feel, helps you avoid those biases.

Dayle Hall:
Yeah. I think one of the things that I think about with these types of processes, so even if you start with a business process, a business you're trying to solve, and then you're trying to identify certain bias or really look at the data, is there sometimes a challenge to say, we've got to move fast to get the data going? Is there sometimes a challenge with making sure the data is unbiased, is high quality, and actually being fast for the business perspective? Have you ever been in a scenario where someone has said, well, it's kind of good enough, let's just go with it? Or is it important that people go all the way back and just make sure that the baseline of the data is clean, has no bias, has been validated, before you actually make the decision? How do you balance all the right data versus the speed of getting to that?

Raoushan Ummulwara:
It’s always a balance. It's always a balance. It really depends on what strategy you're trying to use, whether you're trying to staff people of information, so they have no insights to derive, versus are you trying to give them information where they come back and ask you questions about what is the data accuracy versus the data standards implying. In these kinds of situations, what I would say is democratize the access to the data with proper security standards. Tools like Power BI or the reporting tools nowadays have become to a point where everyone can get their own analytics, but you have a huge problem to solve even that what is your standard analytics, if you have everyone building one kind of KPI.

In those scenarios, what I usually suggest is whatever metrics are on the C-suite agenda, the data for that should have to be standardized. It has to be certified so we know those metrics are not altered. There’s always going to be ad hoc analysis. That's fine to have your data more accessible, but some of those C-suite metrics should really be honed down on the data.

Dayle Hall:  
Yeah. I like what you said around, and obviously, this is a big term these days, which is democratizing the data, allowing multiple people to be able to input, to use the data and so on. How does that play into something like data governance? So we talked a little bit about quality of data. Now, data governance, how do you maintain that level of control? Does diversity and inclusion play any role in data governance at all?

Raoushan Ummulwara:  
Hard to say on the diversity and inclusion piece, but the data governance itself is such a crucial aspect that many organizations overlook, what are your security and what are your controls. You do not want people to start building something that you do not have control about. But also on the other side, you don't want to control too much on what people are doing. What is a healthy balance on data governance and quality standards?

Here is my thinking. We have to set up the controls and let people play in those controls. Just like bowling, you have your controls. But within those controls, you can have your own playground. So setting up those proper controls in terms of data governance is the key, but also having the forethought that you need to give people the independence of doing things but setting up the controls as well.

In my experience, having those peer review committees or whatever you're trying to do from a strategy perspective is very essential, not with an intention to keep an eye on what people are doing, but to keep an intention that whatever you want to do, you want to have the best quality. You want to have the best standards. You want to have the best quality checks in place. You want to make sure whatever that people are doing from a data perspective is going through some form of quality control check, from a business perspective. That's where the good governance plays such a pivotal role.

Again, it goes back to having that data-driven culture and advocating to people that when it comes to data, your biases can lead to wrong insights. You're wrong, not having proper controls can lead to XYZ. Having that, educating people also is pretty important on governance. Why they are doing governance is also important, I feel. Whenever I am interacting with any number of users, I explain them why governance is important. It can be painful sometimes, controls, but just having that forethought in mind on the governance and quality assurance gives them that perspective.

Dayle Hall: 
Do you feel that because, again, technology is moving at the speed of light, you look at what's going on with generative AI and so on, do you find over the years, with access to different tools, with more places to collect data, everyone's building a data lake, there's so many terms, the lake house, everything around data, do you think leadership in organizations, they accept more that they have to share the data? Or is there still a little bit of reticence about who gets access to the data? I know it comes to the governance a bit, but I'm just thinking, not just the controls of the data, actually sharing that, we all have more access, but sometimes do you think leadership is like, well, I don't know if everyone should have access to this data? How is that balance in large enterprises?

Raoushan Ummulwara: 
Right. It is, again, setting up the controls and also coming back and reviewing your controls at a quarterly basis or yearly basis. Of course, you do not want someone to have access to data that is sensitive. In companies, we define what is data, what is sensitive, not sensitive data, who can access to what kind of data. I have worked in companies who are very stringent on data access versus I have worked in companies who are a little bit lenient on data.

Dayle Hall:
One better than the other or…

Raoushan Ummulwara:
It’s not about better. It really depends on, from a strategy perspective, what is important and how much is important. How your company works defines a lot. That's why I say the culture on a company matters a lot when it comes to these things. But again, coming back, if you want a meaningful change, if you're doing something like a center of excellence, if you want to see a meaningful change, just talking about diversity is not important, and cooperating those DE&I metrics is important. And I say again, going back to your diversity topic, if you want to see a meaningful change versus if you are thinking just about data governance and all of this, this is like this traditional data governance, like rules that we follow in terms of what we want to do.

But how the industry is going, earlier, we used to be a bunch of IT experts doing things. Now how I see, it's the business who want to take up and want to learn the technology and do things. Controls are important but not too much control that they are not aren't able to do anything. And then educating them also is very key, I feel. Having education programs is important. Educating them on the technology, educating them on the governance processes is the key.

Dayle Hall: 
Yeah. In the last section, let's talk a little bit about the role that you're in. So we've talked about intelligence. We've talked about data quality in and around the enterprise. Let's talk about the role, the data science role, and the things on a daily basis. I'm going to ask this, right, the perfect question for this month for International Women's Day on Friday, and we'll delve in a little bit deeper after that. But as a woman, as a female leader in  this field, what have been some of your experiences regarding expectations versus reality of working in this field? I know it's very male dominated, but give us some of your experiences. They could be good, bad, or interesting.

Raoushan Ummulwara:  
In terms of women in data science, again, data science is such a new world. It has always been there, but it is catching up right now. It's a very technical world when it comes to the data science world. Going back to your question, experiences versus reality, I feel as women, we need to come together and do a little bit better when it comes to the data science world as a community in itself. Because there are so few of us, first of all, in this field. And when there are so few of us in this field, I feel, as a woman, we need to work extra hard to make our voices heard, just my experience. So I feel we need to come together, but as a community to do more in the field of data science and being also- I have worked with wonderful data scientists. I have worked with wonderful leaders. But there are just so few of us in this field who actually understand this field, who actually have worked in this field.

Dayle Hall: 
Yeah. I think that's always interesting. And you use the best word for it, which is community. I feel like there's always the need for community, whether it's people in certain tech industry, whether it’s people of different backgrounds. I’m not born American. I came from the UK, from England. And I remember, even now sometimes, trying to make sure that my own team understands that you have to support everyone globally. It's not just about what we can do because we sat here in America, it's like being that expectation that all people you need to include. But I think community is the key area.

Is there a data science community that you're part of or you think that would need to be developed a bit more? Because I wonder if there are groups out there that are trying to inspire inclusion and support diversity, female leaders, across those type of field.

Raoushan Ummulwara:
There are women in data science fields, but I feel like we need to build that influence, being a leader in this data science. We have to go from the phase of being in the adoption phase to influencing phase as women in the data science field. I'll stop there.

Dayle Hall:  
I know it's not an easy topic. And I think you mentioned it a couple of times, which is I think a lot of this depends on the culture of the organization that you're in, the support that you get, and it’s not the same everywhere. But I think that like anything, if you can- this podcast may have a small influence on someone else out there that may hear it and be inspired. That's the kind of thing. You can start with something small, but the community has to grow, and raising the visibility of someone like yourself and the work that you've done, and how you pay it forward. I love the fact you recognize Susan up front because that's exactly what this is about. It's about paying it forward. We should all think about that, particularly in this area.

Raoushan Ummulwara: 
Susan is an amazing leader, I would say. I can’t just stop talking good about her, but she's an amazing leader and someone who always I look up to.

Dayle Hall:  
It's good. In terms of data science as a career, as the role, are there some misconceptions about what you do on a daily basis? What does that role entail? If you're in data science, are you literally looking at, maybe not the ones and zeros, but you're looking at spreadsheets, you’re looking at the data all day? What's a misconception about this type of role?

Raoushan Ummulwara: 
The misconception from a data scientist perspective, what have many times people get confused is data analyst is data engineering, and then there is data science. These are three different worlds. Many people get confused on what data scientists do versus what data engineers do, what data analysts do on a regular basis. And that's the whole confusion in this industry. And there's someone who has been in this industry for 17 years, who has done all of this. These rules are so different. But most of the times, industry try to club everything and try to call it data science, where the data science industry is such a different industry all throughout.

Dayle Hall:
I can totally see that happening. I know how that works. So give me your description. What is a data analyst versus data engineering versus a data scientist?

Raoushan Ummulwara:  
The data analyst, how I define them, they are the people who are analyzing the data, which is so much more different. They know the data. They know the process. They know the business processes. Data engineers are the ones who are building the pipelines. They are going and getting the data. They're making sure they're getting the right data. And they're trying to build something meaningful.

Versus data science would be more about the feature engineering, or we're trying to build models, machine learning models, so the AI model. That would be the whole data science world. They understand the data, but they also understand that there are limitations in the data, so they try to come up with new features in the data. There's so much more we know about our data now versus what we used to know. There's so many ways of getting the data nowadays. So that is the data science world. It's more about building the models world and making sure that the models are accurate and your predictions are right and all of that.

Dayle Hall:
And you came from an engineering-type background, right?

Raoushan Ummulwara:
Yes. I'm a computer science engineer by education, yes.

Dayle Hall:  
You're a computer science engineer. Does that lead you down to more of the data science side? And do you recommend, should everyone that’s interested in this type of career, should they spend some time in engineering, being an analyst?

Raoushan Ummulwara: 
Yes. I would highly recommend. I have played every role. I have been a data engineer. I've been an analyst. I have been an architect. I have done security, pretty much all of that. When it comes to data science, having the understanding of what data engineer’s doing is good. Having also the understanding of what data analyst is good. Because once you have that understanding, then you build accurate models on what you're trying to do. You understand the business problem correctly, to a certain extent, I feel. So data science, sometimes you also have to do data engineering. They might not know that they're doing engineering, but they might be doing data engineering as well. So knowing those terms, knowing who those people are, what they do is very crucial for a data scientist.

Dayle Hall:  
Right. From your time when you were graduating, and the training, the education that you had, does it make it harder actually being able to go from, okay, I've got all this training to applying it  in business? Was that a challenge? Or you just have to get to know the business, get to know the processes? How would you advise someone, say, okay, when you've done all this training, or your degree, whatever it is, how do you translate that into actually being able to do this in the real world for an enterprise?

Raoushan Ummulwara: 
Yeah, it always goes back to what I said, always think from a customer's perspective, from a consumer perspective, and always try to start from the problem, have a clear understanding of what is your business problem that you're trying to answer. And then you can go back to learning the business, coming from a consulting world. Not understanding what you're trying to solve itself creates a lot of problems in this world, in the data world, because there's so much data available nowadays. And if you do not know or if you don't have awareness, or if you don't know what are your limitations on your data, it poses a huge challenge. So always start with the problem. I come from an engineering background, so I say reverse engineering, always go back to the roots.

Dayle Hall:  
I like- and by the way, I think that could apply to so many different things. I think about we were talking before we started recording about kids in middle school, and I have a high schooler. And I think about whatever she's going to go off and do [inaudible]. I like that piece of advice, which is when you come out of academia or college education and you get into a job, if you started your first few weeks or first few months on the job, and someone gave you something to do and you started with, okay, well, what business problem are we trying to solve, that could apply to so many things.

Raoushan Ummulwara:
Yes. What is your business purpose? What are you trying to solve? Why are we trying to do this? Why not the other way? Always have those questions in your mind as a data scientist, as a data analyst. I started my career as an analyst. It always helps you define the problem. And then you can start from there to understand what is your business.

Dayle Hall:  
Yeah, that’s a good piece of advice. I will tell my daughter what you said when I get home today. This season of the podcast, we've talked a lot about generative AI. I'm sure there's some impacts that you're seeing. My last question for most of the guests this time is based around generative AI. So what I'm going to [inaudible] that to data science and the role that you have. Is there something you're seeing around generative AI that's going to impact your role? How do you think it's going to evolve? And what are you really excited about thinking of the capabilities of generative AI and how to leverage it? Is there something you're excited about seeing in your role within Chevron Phillips?

Raoushan Ummulwara:  
Generative AI is such a relatively young field. I’m amazed with the possibilities and with the rapid growth it brings. When it comes to companies, it's so much that it can- in terms of automation and in terms of knowledge also, it can take a company from the endless possibilities that I can think of. However, on the other side, companies also have to keep in mind about data security and data privacy. It also opens a big door for data security and privacy.

So there is always a balance in adopting a technology that is moving at such a fast pace versus also keeping your company secure, keeping your company data secure. You do not want to have those data leaks. You do not want to have those [inaudible] leaks. So having that healthy balance, having those proper governance, it goes back to governance, as having those proper governance controls, having those proper monitoring mechanisms will help you adopt the technology faster. Being afraid of the technology is not going to help you because everyone, irrespective of the field we are in, is going to adopt GenAI in some form or the other when we are evolving. But having those proper controls in place will help you adopt it faster, I feel.

Dayle Hall:  
Yeah. So net-net, you should take advantage of it, keep the controls in place to make sure you're not sending data where it shouldn't or letting that escape, and what we just talked about, if there's a business case generative AI could help solve, then you should be looking at it. But always come back, that's the theme for this podcast, always come back to the business case, what problem are you trying to solve.

Raoushan Ummulwara:  
Yes, exactly. You have put it wonderfully together.

Dayle Hall:  
I like to have that theme coming out. So when people go back and listen, hopefully- again, I just think about people out there and they're listening, too. I want them to take something from it. What they're definitely going to take from this apart from your other perspectives around all the areas we discussed is keep coming back to that. What are we trying to solve? What's the right technology? How do we use the data for it? And I love your perspectives around the different roles within the data science area. And I think having a good experience across all three would be super beneficial, I'm sure. But it's very different roles, and I think other people out there understanding these very different roles will probably give them a few more insights. So thank you.

Raoushan Ummulwara:
Thank you, Dayle, again. Nice talking to you on this podcast. Very passionate, someone who's very passionate about data, who has been in this industry for 17 years, who has seen different industries also evolve, something to keep an eye on, how GenAI in the data science world takes off in the next few years.

Dayle Hall:  
Yeah. Well, as you mentioned Susan upfront, and I appreciate that, I'm sure that other people are going to listen to this. You may even get some people reach out and say, she is someone that I really want to connect with and make sure we pay it forward to. So Raoushan, thank you so much for being on the podcast.

Raoushan Ummulwara:  
Thank you again. My pleasure.

Dayle Hall: 
Thank you to everyone out there listening. It was a great episode. We will hopefully get this out on the right day of the week, which will be on International Women's Day on Friday. I think it's the 8th. Until then, thank you for joining the podcast, and we'll see you on the next one.