What The Funk?

0:00 We are back on what the funk with one of my all time favorite colleagues. Mr. Jimmy, Sebastian Jimmy. We haven't talked in a little while. I know we've kind of exchanged a few emails. Um, you

0:13 were at seven lakes before I started and we're there afterwards. You must have put in like almost a decade. Yeah. Hey, Jeremy. Um, it's great to see you. Uh, good morning and good to be on the

0:26 what the funk podcast Um, and yes, yeah, I, you know, we spend a lot of time together at talent lakes. I joined in 2012 and left in 2021. So about nine and a half years is a long time. Man,

0:40 we're going to have to dig into that because things change so much in that time. You, you, when you started, what were there? Like 10 people. That's right. Yeah, about 12, I would say. And

0:51 you are instrumental in bringing me on in my first interview process. You were always really skeptical of salespeople. which I appreciate. So I think I did enough to win you over, but in the early

1:02 days there, you and I actually spent a lot of time together. You were customer facing. I think you did a lot of the internal management, but we're also very good in front of customers. You picked

1:12 up oil and gas very quickly. So those were some of my kind of favorite days in my seven lakes experience was getting on the road with you, doing work with you, burning the midnight oil for sure.

1:23 And then I think as the company grew a little bit, you actually started having more defined roles, right? You sort of stepped into a chief product officer role. I was kind of heading up sales,

1:33 individual contributor on the sales side. And then since then you've been at Albertsons and doing some sort of executive level

1:42 analytics work. But I want to go way, way before that. Like who's Jimmy Sebastian? Where are you from? How did you end up in California? Where'd you go to school? What led to us first crossing

1:54 paths in 2012 and I want to hear the full story. Who Jimmy Sebastian is? Sounds great, Jeremy. I will start at the beginning. So I'm originally from India, the south of India, a state called

2:08 Kerala, which is like the southernmost west tip of India. And, but as I was growing up, and my father had a transferable job. So, you know, he would stay in a place for about three, four years

2:21 and then we would get transferred. So

2:25 I did travel all over South and the east of India. So I was in places like Calcutta, Madras Chana, as it called now, in

2:34 Kochin, which is my hometown in India. And as I grew up, you know, in Metro, so I did get a good understanding of the diversity of India and went to school, so finished high school and then went

2:50 to my engineering degree at IIT Madras, which is one of the premier

2:57 in India for engineering and then where I met a bunch of great people and you know still connected to them and it's been great and actually the peer group is a really great part of what the college

3:10 life is all about. And then I did my MBA shortly thereafter from the Indian Institute of Management in Calcutta And then from there on I decided that you know as I was looking at majors or areas to

3:23 go into I decided that you know whether it was a choice between finance or information technology for example. And so sales and marketing were not really my great forte so I kind of gravitated

3:38 towards these areas And anyway so I chose information technology and went into data data warehousing data mining so I joined PricewaterhouseCoopers in their consulting division for data warehousing

3:51 and data mining in Calcutta. And then from there on. I moved shifter to Bangalore and I had a stint with the company called I2, which is now called Blue Yonder. So it's a supply chain planning

4:04 company, it's used by many, you know, finer retail manufacturing and other sectors too. For their supply chain, demand planning, forecasting, inventory planning, et cetera. And so that was a

4:16 great time. I was consulting in the Asia Pacific region in Singapore and Thailand. So that was the first time I was going outside India It was a great experience to other cultures and other cuisines,

4:27 so it was great. And then at that time, around that time, I met my wife. And after a few months, we decided to get married. And then after getting married in 2002, I joined the same company as

4:42 my wife, which is the UST And there are information services providers such as, you can think of it as a small

4:53 TCS, like the Tata Consulting or Infosites, a smaller company similar to that, but yes, exactly. So that space. And so all along, I was building my career around data as a consultant, as an

5:07 analyst and a project manager. And then came, so by the time I was already in LA, and, you know, the family grew, we had kids, and we are now settled in the suburb of LA called Acini Valley,

5:22 which is about 40 miles north west of LAX, right? And, yeah, continued my journey along the data side, and that's when, and I was leading the PI practices and, you know, the operation, the

5:38 ticket master. That's when the entrepreneurial bug bit me. Like, I really wanted to do something, which is with a much smaller company, you know, starting off things, really wanted to build

5:50 something together and wanted to put my data and analytics background to some good use. And that's how Shiva Rajagopal and who is the CEO when he contacted me. If we were a very small company at

6:02 that time, primarily services and like I said earlier about 12 people. And I joined and I, you know, but I didn't join thinking that we'll continue the consulting business. I wanted to build the

6:14 products company. So my intention was always to come in and build the products company. So after we, you know, when we were there, we had a few clients, which was, you know, Bill Barrett in

6:27 Denver, you know, at that time that the company was one of our customers.

6:32 And we had a couple of customers in Houston as well, right? And then as the company grew, in about 2015, we, I said like the organic growth of the company. And I think at that time is when you

6:46 joined. So I forget which year you joined Jeremy Maybe it was 23rd, 2013. Yes, that's right. Go after a while, you know, Shiva was like doing all the sales and I said, that's not really

6:57 sustainable. And he also felt that, right? We needed somebody to have the sales focus, right? So that's how we, you know, you joined. And I don't know if you had that impression, but I didn't

7:10 have anything or skeptical about salespeople. I was quite happy to bring somebody focused because, you know, then Shiva can focus on other areas. I mean, he needed the bandwidth But we did, I

7:22 think we did work together, or maybe in the interview process, I was like really probing, but I forget, I don't remember any of those conversations. But what I do. I think,

7:33 yeah, so there's so much going on here that I have to rewind a little bit. I don't think that, so what Seven Lakes was doing was not new for you because you'd been in the data reporting in

7:46 analytics space outside of oil and gas and saw how transferable it was.

7:53 me and really any salesperson coming in, it was sort of revolutionary. All of the reporting and integration at that point was point to point, that sort of spaghetti architecture. Data management

8:04 and data warehousing was a little bit of a foreign concept outside of really just the majors and oil and gas. So it wasn't just a, definitely wasn't a product sale, right? It was a very

8:15 consultative sale that resulted in a services engagement And I do think it took a level of not product pushing, right? Like very consultative, very proactive. And it went really well. I mean,

8:30 2013 was kind of the first year. For me, culturally, it was a little bit different, right? Like I was the only white guy in the company, everywhere else that I'd worked before that, everybody

8:41 was a white guy, especially in a hot gas, besides maybe a few of the developers in the back office, you'd have some diversity But for me, Seven Lakes was really eye-opening to. A, it was a lot

8:53 of first generation people in the US like yourself that really had a tireless work ethic and a real passion to succeed. And at a small company, that can be a blessing and a curse. A blessing

9:07 because you can scale very fast. One employee could be equal to 15 employees at competitive organizations. But at the same time, because it's such a small company, you bring in a hurry Or you

9:18 bring in a shrievon or a Simone, where can they go? There's only so much of a ceiling where you can grow within a small organization that you basically have to sort of hire yourself, promote

9:30 yourself, by creating a new product line, or a different area or discipline. And I think a lot of people followed your lead on that because I saw you hands-on creating certain products. But let's

9:43 go back to moving to the US. So you were in Calcutta you're doing a little bit of work in Thailand. How and why Los Angeles, did you move over there for work? Did you get a work visa? How did

9:57 this all work out where all of a sudden you wake up one day and you're living in Simi Valley outside of LA? Yeah, yeah, interesting question. So I did, my always had the idea of coming over to

10:12 the US, even as I was working in India. And originally actually I was recruited

10:21 to I2 or the supply chain company to be placed in Dallas, right? So I was kind of set on coming over here. And that's when the dot com bubble burst or the aftermath of the dot com bubble bursting

10:34 led to a lot of like retentions and all, you know, tough situation as far as recruiting is concerned in the US, right? So the company was not able to get me to the US. And so then that's where I

10:46 started, ended up exploring the APAC region, right? But since I had that in mind, so the next opportunity I got with the UST, so they had big offices and we were consulting, doing a lot of

10:59 analytics consulting for Anthem Blue Cross, which is the health insurer. At that time, it was called El Point, right? So then that's how we came over to the US. And also the icing on the cake

11:10 was that my wife and I were in the same location, usually when working couples come over from India because of the work situation One may be in one city and the other person is in different city and

11:22 then you have to do a lot of traveling, right? And so this was really convenient and fortunate for us that we were both in LA and the customer base or the client base and the number of projects here

11:33 were many. So then we could work on it. And so we both came here working in different departments for Anthem Blue Cross or consulting for Anthem Blue Cross while being part of USD. So that was the

11:45 story behind coming over.

11:49 Because of the client location being here, we came to a place called Woodland Hills, which is in the San Fernando Valley. It's like about 30 minutes from where I'm right now. And after a while,

12:01 as the family grew, I decided to move to a little bit of a less urban or quieter place called Simi Valley. And that's where we are. Yeah, it's very nice out there. My first exposure to Thousand

12:14 Oaks, Westlake Village, Simi Valley was when I was interviewing with Seven Lakes. And I remember sitting in traffic, of course, I think I'm of the 101 or the, whatever, the five or the 405.

12:27 One of those highways. Yeah. We're just sitting. The 405 is the one on the US. It's so bad. But I remember thinking, this could be kind of nice. Like working, you know, I'm always going on

12:42 business trips to the oil cities So I think I live in the best one with Denver being. being that. But then, you know, you've got Houston, you've got some Calgary, you've got Oklahoma, those

12:51 places are fine. But it's like, Oh, you know what, now I can sprinkle in some Los Angeles into this, you know, maybe, maybe go down to Hollywood for an afternoon, maybe take one of the Canyon

13:01 roads and go down to Malibu and stick with it in

13:05 the sand on a business trip. Like, this seems kind of cool. So that was, that was pretty fun for me. And I think you also get exposed to a, a different talent pool just in, in such a large city.

13:19 But you, you're still there. You still live there. And in 2012, right? So did you know Shiva before this? I mean, there's a billion people in India or whatever, I'm guessing. Yeah,

13:30 it's, it's funny how you, it's, it's an interesting point and actually a very coincidental thing. So Shiva contacted me over LinkedIn, right? So he was, as you were mentioning, he was looking

13:40 for talent in the region and analytics talent then he just found me or linked it. He was going by maybe looking at my educational background, you know. So, okay, somebody from IIT already knows

13:53 that they have gone through a certain level of rigor in terms of their academics and abilities, right. So, that's probably how he found me, or he found me through LinkedIn. And as we, you know,

14:06 it turned out he had later, as we started talking, it turned out that he and I had gone to the same

14:14 middle schools. He and my dad and his dad knew each other, you know, the families knew each other, but I had not met his parents, right, at that time. But families knew each other. And we also

14:27 lived, or the families also lived back in India, not far from each other. And so, after, while we were in school together, we were not in the same year, and I had moved on, and to, you know,

14:39 to go to a different city, but he was still there. And so, our paths didn't really cross when we were in school. or in elementary middle school and high school, but later, you know, it all came

14:49 together. And so that also gave me a level of comfort in that, okay, you know, this is somebody, and he probably even him, okay, this is somebody that the families know each other. So because

14:60 you're trusting this person, and even I'm, as I'm joining here, we are looking at, and I'm thinking, should I join this company? I don't know, it's a small company, it's unknown, what do we

15:08 do, or how do we know? And so I think from his perspective, and from my perspective, it gave us a lot of comfort factor that the families knew each other, and we had such a long history, even

15:19 though we did not really talk or meet at that time.

15:25 That's really cool, it was a, it's too coincident. I don't think I knew that. I mean, I guess I knew that there was some, I would assume that when he was looking, he was looking for somebody

15:36 who had IIT experience. I didn't know the rest, IIT plus Business Intelligence Exactly. Makes sense. Exactly. And then Los Angeles. So you're narrowing the pool down a little bit. But now

15:50 you're talking about even like deeper connections. And I think at the time that you joined, like you said, two, maybe three clients. But I knew the company had something. It was pretty well

16:02 established at that point, at least with all the work that Seven Lakes was doing with Lin, there was a lot to go to market with It was far less purely conceptual,

16:14 I should say, and really some unique things that frankly companies still don't have a great handle on today, whether it be like, remember the well profitability dashboard, the profit. Yeah, yeah,

16:31 yeah, that we just built together, yes, yeah, all of the profitability analysis from multiple perspectives, like financial perspective, operational perspective, and then also looking at the,

16:39 you know, the, the leases and everything, and then considering all those factors to figure out. which ones we would keep operating, right? Or the operator should keep operating and which ones

16:48 about the abandoned or shut down. It was really, one of the companies I work with now, Zino Technologies does something similar and incorporates some of the elements that we had, like almost like

16:60 the LOS slider. If you say, okay, well, if natural gas prices go up or down, then how does this potentially affect my lease operating statement? But these are things that we were doing 10 years

17:12 ago that I don't think the industry still has a full grasp on now. And the challenge, of course, is analytics and data, and I want to kind of transition to talking about that, at least as I see

17:24 it, which is the narrow lens of oil and gas, anything that you do from an analytics and reporting standpoint is really the end result of a successful data management project. And I'm curious how

17:35 you view some of the differences in your career that you've seen at companies like Ticketmaster You have true big data at Albertsons, which is retail. How do things differ from oil and gas or would

17:47 you say like to distill it down? It's actually somewhat similar. Like I'm curious from your perspective as a data analytics expert. How do these industries look similar? How are they completely

17:58 different? How are things handled? Is it the same? Is it vastly unique per industry? Like give me some of what's in your brain from being an analytics and data guy multiple different industries.

18:11 Yeah, it's a great question There's actually a lot of different angles to look at, but let me try to address some, the key ones, I think. All of these, the commonalities that they do have data,

18:23 obviously,

18:25 but the volume and the variety and the type of data is different. In oil and gas, you do generate a lot of data. While, let's say you do some operations like drilling, real-time drilling

18:39 operations, right, or real-time monitoring. such as KEDA, and in those areas is where you generate a lot of data, but then you don't, you try to synthesize it and try to or summarize it and then

18:55 to take certain key information, extract some information out of it, upon which you can act, because usually sensors, some sensor is looking at how well it's performing, let's say a pressure

19:09 sensor Then most of the time, you're getting a lot of data, but there is no action to be taken or there is no problem and you don't need to store all that data. So you only need to look for

19:19 anomalies and then you take that. In other industries like retail, in algorithms, you have millions of transactions happening every day. So there is a lot of different data and you do have to take

19:30 each and every input there, because it's talking about the customer, what they're buying, where they're buying, what are the products they're buying together. So the information that needs to be

19:43 and the variety of it is different. And in Ticketmaster, again, it's thinking about the consumer behavior, like what concerts are you going to, right? How are you engaging with? And the value

19:57 of that may be to different customers or different, the clients of Ticketmaster, but such as the promoters or the arenas or the artist groups, right? They may want to understand their fans better.

20:09 So there's a lot of like value added, monetization type of work that you can extract out of the company's data and then provide it. So let's put it this way. One way, one difference that I see is

20:21 that the kind of data and what you do with it in terms of processing the volume is different. And the second is, how do you internally, externally monetize that data is different? In typically,

20:32 noiling, yes, we don't really monetize that data. We use it for internal purposes, right? And on the production side, the volume of data is still small.

20:41 Our focus is how do we keep operating and make sure that our assets are performing and producing to the fullest potential, right? This is the focus of the data. But what a common theme, Jeremy,

20:53 and where I think, like you said, there are still problems. I mean, whatever we were working on 10 years ago, those problems still exist. And the reason they exist is that companies are not

21:09 figured out the right way to utilize this data for their business purposes, right? If the business analytics and business intelligence is fully integrated into the day-to-day operations of the

21:20 company, of somebody, let's say there's an analyst that's looking at an production analyst, right? If they are able to really leverage the data that is being produced by the systems and the

21:30 analytics and utilize it in their data decision-making the right way, then the company reaps great rewards and this is similar for other industries too, right? And where most companies fail is that

21:43 there's a lot of tools. You'll find in every company has business intelligence tools, reporting analytics, dashboards. But how well are they used in the company? And how well is the use of

21:57 analytics embedded into the psyche of the company or into their day-to-day operations? That's where what companies fail or they succeed And that's where differentiates a great operator from a

22:12 mediocre poor operator.

22:15 I love that. So I think that that is - I mean, you did it in about 2 and 12 minutes, which is pretty impressive. But yes, you're looking for outliers. You're looking for anomalies. You're

22:26 looking for spikes and decreases, whereas the data that you're collecting from retail or Ticketmaster, for example, is for future marketing to that person. Yeah, that's not what you do at oil and

22:40 gas, right? You're looking at how to, can I take the information that I have here and become a little bit more predictive in the future? How can I avoid some of these pitfalls going forward or

22:52 assuming that they're going to happen? How can I reduce the amount of time that then the well will go down? Because we know it's going to happen. Let's become predictive with it.

23:01 You actually kind of planted a seed in my head, which is interesting. So I'm not sure if you observe this as much toward the end of your time at Seven Lakes, but earlier on in my time at Seven

23:10 Lakes, there was a ton of spot fire. Like, spot fire had really dominated the analytics world. And Tim, my former podcast partner, worked at Tibcombe, was one of the initial demo guys for spot

23:17 fire. So you're here to this podcast and to where his heart was on the analytics side. But there was kind of a shift I started to see around 2017-2018 away from using spot fire

23:34 for all of your analytics and reporting and doing more with Power BI. And my thought was that was probably because it was just cheaper. And then maybe in some ways, it was easier for the back

23:46 office, the accountants, the finance people, some of the engineers to digest that information in a more simplistic platform like Power BI. But sort of listening to what you said, I almost wonder

23:59 if there's a lack of confidence or comfort in the data itself that the end user of that data is saying, well, why do I need a really expensive analytics platform if I don't fully trust or view the

24:13 data as the source of truth, right? And I think this all sort of brings it back to - and you were big on this, too, and part of why Seven Lakes went down this whole path of the - I forget what it

24:23 was called, but the

24:26 Master Data Management Project, which was sort of like clients started asking about that, but that's really where it all starts for a company that has 5, 000 wells. And then you have various

24:38 different source systems, none of whom who talk to each other. And then different people in the office living in silo is not talking to each other. It becomes very difficult to reconcile all of

24:49 your data and trust the data across the organization. And that can lead to lots of different challenges. But one of the things I always admired and appreciated with you, I think because of your

24:59 background in data, was we can build anything beautiful on the front end in any of the tools that you want or an HTML5 dashboard, but we have to get down to the data. So some of the fuzzy logic and

25:10 some of the integration API capabilities that you and your team created, were really some of the most valuable things that Seven Lakes had in that early kind of rapid growth analytics phase. And

25:20 then of course, like you said, the money came in, you were able to successfully raise, I think it was like a 20 million series A or something like that. And just push forward into actually

25:31 productizing some of the things. So talk to me a little bit about the journey you're running like a consulting services organization. Money comes in, now we want SaaS revenue, we don't care as

25:41 much about the consulting revenue. What then was your focus and your pivot to kind of like take it, what had become like a pretty decent sized company at that point from being a consulting firm to

25:52 now being a products company. Like what were some of the things that were challenging that you enjoyed about that? And how did that kind of fundamentally change the organization that you were

26:00 running going from services to product-centric? Right And so, as we were going through this journey of equipping companies with analytics, right? Like linear energy, like Bill Barrett, the

26:13 culture and others. What we found, Jeremy, and in many cases, we discovered it together. You and I, as we would talk to customers, is that the root cause is that there is no data, or there's

26:27 poor data capture, or there's a data quality problem, right? Because garbage in garbage out as the cliche goes, So, if there was a beautiful Spotify dashboard, but there was no

26:40 source data system, which was like good, and the source systems were not talking to each other or cross-referenced, so then the reports and dashboards that people spend millions on were not very

26:50 useful, right? And that's where analytics was failing, right? And so, as we discovered these problems, in some cases we discovered that they needed a source system for data capture, in some

27:01 cases we discovered they needed

27:06 a master data management system, they needed integration tools, they needed data quality tools. And using, and in some cases they also needed workflow tools for people to collaborate, right?

27:13 Like so, during a pre-spud planning process, they needed to collaborate, you know, some of that data was being generated there, you know, tracking permits, right, tracking, keeping track of

27:24 all the checklists that are needed for pre-spud planning So, what we discovered was there is a huge opportunity in. creating analytics and Workflows business workflow solutions, right? So that's

27:40 what we started putting together But then as we started conceptualizing and working on some of those what I had been pushing towards and realizing Along with you and Shiva is that we couldn't do this

27:50 just Organically growing right we can take the money that's coming from Projects and then put it towards it because we needed to really build some of these capabilities and the the window of

28:01 opportunity would could close pretty quickly Because our competitors were also you know coming in and trying to get into some of these areas So we needed to build out our product pretty quickly and

28:12 for that we needed external capital infusion Right. So that's where I really I pushed Shiva to say, hey, you know, we need to we can can do this. We need

28:24 Money from uh, we need funding. We need a capital raise and we need to also build out our team right in terms of the having. more focused revenue generation, more focused marketing, HR and

28:38 finance together. As we put together our team and we also need to grow beyond the US to have development centers in other places like India. And so follow all of this, this could not be handled

28:49 with just the money coming in from services. And so that's where we went out into the market looking for partners, private equity and venture capital type of partners to raise that money in order

28:60 that we address these business and market opportunities pretty quickly and build out all of these product lines.

29:08 And really made a pretty significant impact in doing so. The concept of

29:16 seven lakes to me at its core was always sort of a company that started with the data. Like some of the most beautiful and aesthetically pleasing visuals, whether it be what FDG was, which then

29:28 became joined with some of the dashboards, even with things like AFU workflow. which were more sort of user intensive as opposed to looking beautiful. But still, look and feel was very comfortable.

29:39 The UX that Seven Lakes put forward was awesome. But to me at its core, Seven Lakes really understood the data that mattered to people. And I think a lot of credit goes to you and your team for

29:50 placing such an emphasis on that. But it was really like, the shift I thought was pretty fascinating because there had been products built Like FDG was there, but it was really sort of like, okay,

30:05 the first step is instead of just going out in the field and sending your lease operators out and keying things in by on a computer, we're actually gonna give you an iPad to do that with. And that

30:15 was like a revolutionary concept 10 years ago. Believe it or not. And then it was, okay, well, we can automatically pull in your skated data and that's gonna come in in a different color. Okay,

30:25 that's cool. And then it was, we're actually gonna help you operate route by exception. That was a big step. Right, it's taking whatever data you had and then starting to uberize the oil field,

30:38 make recommendations of where you can go and where you should go and what makes sense. And instead of just driving in the same route, every single day, let's actually be a little bit more

30:49 responsive to what's happening in the field and then route people appropriately. And I think something you probably haven't seen this as much, but there's a big emphasis now on on emissions, right?

30:59 So one of my clients called Earthview, a really fascinating company, a young CEO, I see them poised for significant growth or really being able to do whatever they want, but they put like methane

31:12 emissions sensors on every pad. So you put like four of these sensors on each pad and they're trying to shift the paradigm in the same way that we did with operating by exception It's right now,

31:25 there's basically a fire truck that oil and gas companies are driving around in the field looking to put out a fire, they're looking for smoke. and then try to react to it. And this is basically

31:34 like, well, wouldn't it make sense if we put like smoke alarms in the field? So that instead of just having to drive around in a square, you can actually kind of route that fire truck directly to

31:45 where it needs to go. And companies are quickly coming along to this. And I think that's an industry that's poised for growth. And even the reporting, whether it be to federal or internal or

31:56 investor centric from an emissions analytics standpoint has a lot of legs to it and a lot of room to grow. But I think back on that time and it established a really important baseline for me from,

32:07 okay, let's capture the data. And now let's actually be a little bit more predictive and responsive to the data that we're seeing. Yes. And that to me was just fundamentally cool. I don't see

32:18 every company doing that. And my theory on that is, I don't think that they necessarily know how to use that data. How were you able to come up with these algorithms and kind of thought in the

32:30 background to take. so much of this data and then start to uberize or become predictive with it in the field. Was that from experience that you and Sachin and Hari and others had had? Or was that

32:42 just kind of fundamental to you for okay, we have the data now, what do we do with it? Yeah, so

32:48 it was an evolution Jeremy and really utilizing some of the industry trends or our background in other industries. I think that's where, if you think about it, I did not have an oil and gas

33:03 background coming in to the company, right? And as did some of the others, but like some other people like Shri one and Hari, they, you know, they came in from Shulumburji and Exxon, so they

33:13 didn't have that background. So we were a good mix of people with background in oil and gas and also background in other industries or like, let's say more broader industry trends that we were tuned

33:26 to. And what we did was just a, you know, staying very tuned to. What was happening in the market and what was happening in the software industry as an overall trend and new functionality being

33:39 put up. So we started off with analytics, then saying analytics needs to be embedded in the workflows, then looking at addressing some of the basic data quality challenges, right? And then with

33:48 FTG, looking at, okay, now this data needs to be captured. You know, it's that there is an issue in the data capture, the what you call it, the grease sheets, we're getting smudge, right?

33:58 And that information was not coming in correctly So that's where how can we provide an automated interface or a user-friendly interface to pumpers, right? And field technicians so they can capture

34:10 some of this data. And then the evolution was now we have, you know, we can integrate and bring in data from SCADA, like you said. So, you know, going from manual input to bringing in SCADA

34:20 data. And then the next step we were looking at was how can we read sensors and get that data through NFC, right?

34:30 So at every point in time, what we looked at was looking at all of the latest technology. And I think Shiva was also big on this. And it was just great that we were lock and step on that. That we

34:41 were looking at, okay, what was Amazon doing? What was Google doing? And these are not companies in the oil and gas space. And what new technologies are they putting it out? And then how can we

34:50 leverage some of that in our products? And so having that broader view and

34:59 experience outside of oil and gas also helped in bringing these new technologies and continuously evolving our product. And then thinking of once we have NFC, then we think of how do we integrate

35:12 our production planning? And then look at predictive, you know, by that time, I think about 2016 and 17 onwards, we had machine learning and predictive analytics becoming really big. And, but

35:25 in oil and gas, other than drilling, utilizing a lot of that. So that is a greenfield opportunity that we saw say, we should be able to take these predictive algorithms.

35:36 And the algorithms are really actually available from as packages or to be customized from vendors like Google or Amazon or AWS, for example. So we work with Pinaki, who is our chief architect, in

35:49 order to take some of those and then plug them onto the oil and gas data, and then be able to say predict downtime And we actually referred to some of the latest papers put out from USC and other

36:02 universities by petroleum engineering researchers on what should some of these parameters be for predictive analytics. So we were really doing some cutting-edge stuff in terms of reading those papers

36:13 and looking at those algorithms and then trying to code them into a product. So it was like a good mix of external technology, keeping up with the latest research, and then always looking out for

36:23 the customer and creating a product which was at the cutting edge. And this is where that even our smaller customers could leverage some of this latest technology, even though they did not have a

36:34 huge budget like a Nexon or a Chevron. Yes, yeah, in some ways it leveled the playing field, just even having that level of reporting. One of the things I think that Seven Lakes did really well

36:47 from an innovation side of things is you feel the request from a customer to build something and you build it But in the process of building it, you also start to understand their business and the

36:60 industry a little bit more, and then come with a recommendation for a phase two, right? Well, now we've built your report, but you wanna know what you can do with this report? You can do this.

37:09 And I think that's where the disruption and innovation starts to take place. Curious from your standpoint, like you, so you've been out of, you were in oil and gas for almost 10 years. Now you're

37:22 out, you've been in kind of the retail space. What do you think lies ahead for you? Do you think that you may end up back in oil and gas on the analytics side? Do you plan to start your own

37:31 consulting firm? You think you'll continue in retail? I'm sure it's gonna be data and analytics in some capacity for you. But like when you look at your go forward plan, like what does it look

37:41 like? What's in the cards for you, Jimmy? Yeah, so it's something that I end up doing in data and product. So data and product is my, or data was what I was originally about And then product is

37:56 something that I really fell in love with while at Seven Lakes building the company, building the product suite, right? So the job at Albertsons came about because it was at the confluence of data

38:07 product. You know, I was doing data products, you know, creation and management, creating templates, platforms for data products. So whatever I do in the future, and I'm looking at different

38:17 options like consulting, you like working with larger corporates and never say never to oil and gas.

38:25 keeping my eyes open and options open there too. But it'll be something to do with data and product and hopefully the confluence of the two, where I can help companies either as a consultant or as

38:42 an executive working in them to really leverage their data assets to the maximum, monetize them better and to the best possible ability if that is what the market demands. But also, I'm all about

38:57 building simple, intuitive products and this is something that we both in with your help and with others, we honed over at Seven Lakes where we were trying to build very complex technology but

39:09 present a simple interface to the customers so that it was easily adopted and that's what actually differentiated our products from some of our competitors that how easily could people with almost

39:20 zero training adopt these products and really learn them on the go, right? So I'm about building simple intuitive products that help companies leverage their data to the maximum. Well, you're one

39:33 of the best I've ever met at doing that. And I remember, if you remember, Ken Dalton at that point. Oh yeah. What he said, and sort of the challenge he threw to you and to Simone and Harry and

39:44 your team in general was like, it has to be, people need to get to their information in three clicks or less than seven seconds, otherwise we start to check it Yes, it's a little early, I mean,

39:55 you're tied up. Very high standards, and you're talking about massive amounts of data points in a fairly robust dashboard, but I have to be able to click into my region, into my, well, into my

40:08 key metric. In seven seconds or three clicks, it's like, okay, man, but you were able to do that, and it's a skill that I appreciate more and more as time goes on. It has to be sort of like,

40:20 look at Google, right? If you look at Google in and of itself, It's like, well, it's just a search engine. But then you think about everything that's going on behind the scenes to make it work

40:28 the way that it does That's why they have some of the best developers in the world And you always have to stay ahead because the next bang or whoever is always trying to come and eat your lunch

40:39 Something that I like to do on what the funk and I'm gonna put you on the spot I did not prepare you for this, but a little little rapid fire a little rapid fire Questions and you have to kind of

40:50 give me the first thing that comes to mind. So sounds exciting We'll start

40:56 We'll start with your favorite city to travel to in the us

41:04 Then we're

41:08 How about how about your favorite city to travel to international?

41:14 London

41:17 Okay, all right, I could see you there You know, it's nice Um, uh, Northern Indian or Southern Indian cuisine. Southern Indian.

41:31 No hesitation. No, how about,

41:35 you're an analytics guy. Let's see how you go with this. Michael Jordan or LeBron James? Michael Jordan. Love it. You're also, you know, my age. So we're a little bit older. We had enough

41:47 time to see Jordan. I get with that. I love it.

41:51 Some of the, we'll jump off of that topic I love how you answer everything quickly. You didn't have to deliberate. Some of the people that you kind of mentored have kind of built some really nice

42:02 careers for themselves in thinking people like Neresh Panda and Hurry and some of these others. Talk about what it was like to mentor some very skilled, hard-charging resources to balance. I know

42:16 where you want to get to, but I still need to keep you focused today. That's kind of a management hack that I think is really important.

42:25 immensely talented, hardworking people. And I remember this woman, I think her name was Sushma, Sushma Jee. She said, Tashiva, at one point, we're at a meeting with her. She said, you got to

42:36 be careful because all these IIT guys, they want to run their own company too. So how do you, how do you balance keeping people happy in the today with also balancing a level of excitement for,

42:48 you know, future career success and obtainment? Because I think you did a good job of

42:55 that. What was your secret? Right. So my secret really was thinking about it from their perspective, Jeremy, in that, like you said, all of these folks have immense potential. They're

43:06 extremely smart. I mean, most of them are smarter than me too. So I, and they have like all of this raw talent and raw energy, but they were a little unpolished, or they could do a little bit of

43:20 polishing, right? I mean, not that they were bad or anything, but they could like a diamond shines, really shines when you polish it, right? So that's how I think about it. They're really,

43:30 really superbly talented. And as you can see, many of them are in Google and Meta and all of these other top tech companies as well. You know, some of them gotten out of it. Amazon, Wal-Mart,

43:42 right? Exactly, exactly. So, and, you know, I

43:46 like to think that the, what I did with them while I was working, we were working together, you know, kind of guiding them along as they were doing engagement management or pre-sales or

43:58 implementations, right? Hopefully, you know, led to some of that or helped shape their careers. But what I really did think was, what could I offer to these resources? From my experience,

44:13 right? That would help them grow. Because I knew that they were not gonna be sticking with seven lakes forever, right?

44:22 But, I mean, exactly, I mean, well. they would come in and they would build a product and they would lead that product. But after a while, their ambitions could grow, right? And it's never in

44:31 anybody's interest to kind of limit those ambitions, right? And saying it. So my point was have them, you know, it's a give and take. So I invested into them in terms of whatever I knew of how

44:44 you would do client management, right? A customer expectations management, really, you know, figuring out getting to the, some of the principal leadership principles, like action for, you know,

44:57 a bias for action, really digging deeper, taking ownership, trying to do more with less, right, managing customer expectations, but keeping customers' interest always in mind. So some of these

45:08 leadership principles is what I inculcated in them and then the rest was up to them. So they learned they internalized it and then they grew. And at some point they said, well, okay, you know,

45:19 it's time to move on but in that time, so they were. really good and we have kept in touch after too, in that, you know, I felt like I gave them something. Me and you and Shiva and everybody,

45:31 it's not just me, the only person mentoring them, right? But all of them learned something from us and I learned something from them in terms of, okay, how do you manage such talent, right? And

45:41 it's always the learning. So that's what I took away from all of that.

45:46 Empathetic leadership, right? And then also coaching to the level of, these people are, in many cases, like men's level geniuses, but being able to actually channel that focus and capability

45:59 while maintaining professionalism and not having a level of arrogance is important. And I give you a lot of kudos for that. Those were fun times, man. I think back very fondly to those kind of

46:11 early days at seven likes, you know when you're building something special and I would say if anything, My only regret is in some ways the company grew like too fast there was a massive amount of

46:24 demand and it's just really hard to keep up with that where you feel like everything you're doing is like a week behind. Not necessarily from a delivery perspective, but oh man, we need to hire

46:36 some people, right? And then you're rushing to hire people 'cause you really needed them last month. Okay, yeah, we think that this person's gonna be on this project, but that person hasn't

46:43 accepted their offer yet, right? So it was insane and it kind of set the bar for me for what a fast growing rocket ship could look like And candidly, I haven't been with a ton of companies since

46:56 then that have had the same rate of growth or trajectory. I mean, from a revenue perspective, from a client account perspective, from a new products added, like, oh, hey, guess what? Now we

47:06 have a pre-drill workflow and rig scheduler application, which I loved. I still think, I don't know where that product is if it still exists today. That was a beautiful product that the industry

47:17 still has not solved. And I know that was one of yours.

47:21 Right. And it was a little wild time. Yeah, it was a wild time, it was a fun time, and I look back very fondly on the times that we spent together. Here's my last question for you, right,

47:32 before we jump on. Yeah. What advice would you have for your younger self? If you were to sit down right now with 21-year-old Jimmy Sebastian, what would you tell him?

47:47 What I would say is the

47:50 world is full of opportunities. And you can, you don't have to worry too much about which path you would choose, right? Just take it, be in the moment, live the moment and then make the most of

48:08 the moment without thinking too much into the future and not looking back too much into the past, with any regrets or like, could I, should I have kind of a thing? Just enjoy today. make the most

48:21 of today and then things will fall into place. And whatever you do, that is your best life. So this is

48:30 the advice that I would have given myself because I figured this out over the years. It would have made it easier if I had known it earlier.

48:39 Yeah, I think that's good. It's that's wise and this is why we called you Jimmy G. You're a at wisdom at a very young age Jimmy, where can people find you on LinkedIn or websites or any of that

48:53 stuff? Yeah, so the easiest way to reach me is through LinkedIn. I mean, I have frequently checked my LinkedIn and

49:00 my messages or my email address. I don't have a personal website. My email address is Jimmysubashinagmailcom or

49:07 you can find me on LinkedIn

49:10 and just connect with me, looking forward to connecting with everybody, all of your audience too.

49:16 I love it I think this will probably set off a little bit of.

49:21 a series. I think I'll do a little bit of a seven like series. I was kicking this around with Srivon. I think I'm going to have him come on. Well, you know, I think so. I mean, once she's kind

49:31 of more established and where her business is at, I don't know what she was doing. I heard he's running like a real estate tech company right now. So I'll have to reach out to him and figure that

49:39 out. But those were fun times, Jimmy, you were emblematic a lot of a lot of the rapid growth success and effort. And you should be proud of that Now it was really fun having you on today, my man.

49:50 Thank you. Thank you so much, Jeremy. It was like a pleasure being here. And also, I was just thinking about it. We were having so much fun and time flew. We didn't realize it. So thank you

50:00 for all the good times. Thank you for having me on the podcast.