You’re listening to IoT Leaders, a podcast from Eseye that shares real IoT stories from the field about digital transformation, swings and misses, lessons learned, and innovation strategies that work. In each episode, you’ll hear our conversations with top digitization leaders on how IoT is changing the world for the better. Let IoT leaders be your guide to IoT digital transformation and innovation. Let’s get into the show.
Nick Earle (00:31):
Welcome to the latest episode of IoT Leaders, the podcast that aims to demystify the complex, intriguing world of IoT. My name is Nick Earle, your host and I am the CEO of Eseye, a global IoT company. And today, I’m delighted to have as my guest on IoT Leaders, Dr. Satyam Priyadarshy. And Satyam works for Halliburton and his title is a chief data scientist and he’s also a technology fellow at Halliburton. And if that’s not enough, he’s involved in several startups and is a senior fellow at Mason University on the side of security as well as an adjunct professor at Georgetown University so definitely a busy man. And Satyam, very welcome to IoT Leaders podcast.
Dr. Satyam Priyadarshy (01:28):
Thank you, Nick. Thanks for the invite certainly. Yeah, so a little bit of an addition to the profile, I’m no longer with Georgetown but I moved on and I’m now with Virginia Tech and Oklahoma State and a University in India. So …
Nick Earle (01:45):
So what happened to them?
Dr. Satyam Priyadarshy (01:49):
As a former academics, you never leave academics, you see.
Nick Earle (01:54):
Yeah, I don’t know how you find time for all that but thank you again for finding time for this half-hour show that we’re doing together today. So Satyam, it’s a big subject and when we spoke prior to this, it’s a pretty wide-ranging subject as well, so let’s try and break it down into pieces. Maybe I can just ask you perhaps just a little bit about your own background before we dive in. What is your journey like, how did you get to where you are today of being chief data scientist for Halliburton and perhaps for those people who don’t know Halliburton … Most people do but there will be people who don’t, maybe just a little bit of overview of what Halliburton does in the oil and gas industry.
Dr. Satyam Priyadarshy (02:41):
So first of all, let’s talk about Halliburton then I’ll talk about my journey. Halliburton is 102-years-old company. It’s one of the world’s largest energy services company. I think one of the first patent the company filed was in areas of cementing by Mr. Halliburton and that’s the company named after him. And it was over the decades in the hundred years, it has gone through lot of expansion in different fields but primarily, it is what you call a company which actually collaborates and engineers solutions to maximize the asset value.
Nick Earle (03:18):
Dr. Satyam Priyadarshy (03:19):
Now, it’s a very important part to remember, it’s about maximizing the asset value. In olden days, we would just call the hydrocarbon as an asset but in today’s world, data becomes another asset.
Nick Earle (03:33):
Okay, so it’s not just what’s in the ground and in the ocean but the data is increasingly becoming an asset which is where we’re going to go, right?
Dr. Satyam Priyadarshy (03:42):
Absolutely. So and as said, the company is global, therefore about 50,000 people around the world, and pretty much representing most nationalities in the world. And of course, the challenging task of drilling completion, exploration, all are very complex scientific and engineering based, so lot of highly skilled people are in the company as well. So it’s a great place in that sense because you get to interact with mathematics engineers, scientists, physicists, geophysicists. If you look at the spectrum of talent that is there in the company, it’s significant.
Nick Earle (04:20):
Dr. Satyam Priyadarshy (04:21):
Nick Earle (04:21):
Your own journey?
Dr. Satyam Priyadarshy (04:23):
On my own journey, so I did my PhD in quantum mechanics. I applied to biophysics when I was pretty young and, if you think in a very simple terms, solar cells we make an efficiency of 15% to 25% but nature makes solar cells with almost 100% efficiency. So my PhD was trying to understand what is going on inside a chloroplast from a quantum mechanical point of view. But I think I was way too ahead of the curve because it just can’t compute anything because it involved a computation of 5,000 by 5,000 metrics and on a non sparse metrics, you can’t compute easily because there are no supercomputers then. And even today, non sparse metric is the challenge. And then I switched on and I was trained by advisor. I think I owe my success to him a lot because he trained me in such a way that you think of a problem not as single problem, it’s a multiple problem issue and you should always be open to interesting multiple problems at the same time.
Dr. Satyam Priyadarshy (05:29):
And as a result, in fact during my PhD, he said not to work on, he won’t give me PhD unless I publish more on the topic, which was really good because it opens up your mind to different problems and so I went and switched careers. And careers, I went to post doc like the two post docs in Australia in a totally different field like glassy dynamics and lipid membranes. And then I came to US and did work in DNA electron transfer and nonlinear optics and many other topics. So we have a very special model for DNA electron transfer. But then I did a lot of super computing but if you think the foundation is all in data, it would be quantum mechanist, you’d generate a lot of data. And here, I was the generator of the data as well as analyzing that data for really complex problems.
Dr. Satyam Priyadarshy (06:22):
And so I switched my career from there to become a technologist at a company called AOL which is America Online. I was lucky there that seven years, I went from individual contributor to becoming head of research in 2005 and set up AI Center of Excellence in 2005 in Beijing and Bangalore. And then I did my MBA during that time and switched my careers again to become an executive, turnaround some companies, got involved in startups. And as a result, one day, I got a call from Halliburton or a recruiter from oil and gas and they said they’re looking for someone like this with this experience. I am having fun for the last seven years in Halliburton when I set up my Center of Excellence for big data, data science in digital in 2014 when nobody was thinking about it in the industry.
Nick Earle (07:21):
There’s enough material there for about 20 podcasts. From Halliburton to big data. So let’s just pick that last subject and go deep because you said there was very interesting, you said that nobody was thinking about it awhile ago. And in the subject of IoT of course, a lot of people say I actually think product from IoT is the data. Although it would seem to me that certainly in the field that you’re in, there’s no shortage of data. There may well be a shortage of insight as to what it actually means but it seems like the amount of data that we’re now technically able to create, I mean it’s not a dripping tap or even a running tap, it’s not even a hose pipe, it appears to be almost like a tsunami and it’s only going to get more and more and more. I guess your work must rotate around that in terms of how on earth you make sense on insights and interpret this data that is coming out as in the context of the business value of the data itself.
Dr. Satyam Priyadarshy (08:34):
Absolutely. So people can challenge my comment that people are not thinking about it but that is to an extent, too. But the industry has been generating data no doubt about it. But if you look, talk to experts, they will say that even today, we’re talking about 2021, people will say data is of not good quality, data is not complete. So if you think of where the world is and if we look at data native companies which AOL was one of them and now the Googles of the world, you can call them, if they also had the same problem of data not complete and of bad quality then they would not be making money, right? Because your money comes by analyzing the data, building recommendation and doing whatever it is. Now if an engineering firm, we keep saying the data is not of good quality then what have we done to fix that? And that’s where I think we’ll come back to more details later. I think the IoTs or the industrial IoT will really make sense because that can actually improve the quality of the data.
Dr. Satyam Priyadarshy (09:40):
Now, industry collected the data no doubt, a significant amount from exploration to drilling and completion and HSC and all the other areas but if you look at it, the data was collected in the field. It was transmitted to the back office at a certain frequency so not all the data was ever moved to back office. But if it was moved, it was moved in a later stage so to say. So there was no real time so to say analysis of it to the extent that it could have been done. Now, if you look at even the last 20 years, evolution of the technology. For example, search technology. Like we all depend on it right? For all our practical life, we depend on it. But when it comes to implementation in the energy sector, that has been really poor. If you go to any of the big energy companies, and you want to search for a say a hereditary pump that you have used for the last 10 years and you have repaired it 20 times and if you want to search for that repair log, you will not find it.
Nick Earle (10:41):
We hear these stories a lot about, it’s almost a paradox in a way, that the companies that arguably could benefit the most from the data like this device is broken 20 times on the run, it’s now broken for the 21st time, can I just find out what to do and that would save me a lot of money? And there’s so much money involved in oil exploration. For example, it’s massively capital intensive, it’s a very expensive business, it’s all about collapsing the time. You hear these stories and then you also hear not just Halliburton but large companies in general often are not the ones who have captured the learnings, captured the knowledge and they are just to some extent behind the curve. Why is that? Is it a cultural thing, do you believe is it a volume of data thing, is it that they were focusing on something else as a priority like finding the oil and gas, why do you think that is?
Dr. Satyam Priyadarshy (11:42):
I think my take on that is that while this technology evolved, the industry did not spend well on actually understanding this technology well. So whoever came and said here is my search platform, you can implement it to search your corporate website and that’s what got implemented. Nobody looked at the use case, how it could really will be done for other areas on top of a database. So those two were not thought about very well, that how the value will come and also one of the … It’s a kind of a cultural thing and call it a business strategy. When you are in an operational mode, your goal is to fix the problem and move on.
Nick Earle (12:33):
Dr. Satyam Priyadarshy (12:35):
But if you look at the whole framework of data sciences, it’s that you want to do the science on the data. That means your historical data is your really good source to do that science experiment because your pump already broke …
Nick Earle (12:53):
Yeah, you have the data there.
Dr. Satyam Priyadarshy (12:54):
But now I can actually analyze that data and understand that for last five times why did it break so that I can avoid those situations or those conditions or can address those conditions well in advance before it happens the next time. The thought process has to be there, that historical data has significant value and that value could be measured. And in fact, when we did some experiments, I would call them experiments in early 2014, we showed that how much value it is there in terms of monetary value that the organizations or their industry has lost.
Nick Earle (13:31):
They’re sitting on an asset, a huge asset which is historical data but in general, people are not looking in that direction for the value. They’re looking in the other direction for all the new things, the shiny new toy, what they’re doing next, new implementations but actually, they have a lot of value in the legacy data that’s just sitting around.
Dr. Satyam Priyadarshy (13:55):
I’m sure in your interaction, you must have come to people asking the question who owns the data.
Nick Earle (14:03):
Dr. Satyam Priyadarshy (14:03):
Right? Now, if you think of as a business and you being a CEO so in principle, the company owns the data.
Nick Earle (14:07):
Dr. Satyam Priyadarshy (14:08):
Right? So why this question about who owns the data within a company? So when you start thinking that the data is owned by the company then you start analyzing it and that means people should start collaborating. In fact, I wrote an article 2015 I think called Data Democratization and initially, there was a push back saying we can’t talk about these things. But democratization doesn’t mean to give away. How can we look at the data? You’re in a workflow, you have first three steps worth of data, somebody has another next three steps worth of data. When you look at the whole workflow holistically then only you can create value. When you’re doing things in a silo, you really can’t create value too much.
Nick Earle (14:53):
Excuse me, Satyam. I mean this value … I think the image I got in my mind is that there’s gold and they’re under our feet, we just can’t get a hold of it. But there’s value in the data, there’s huge amounts of data, it’s owned by the company but it’s not like there’s any issues in accessing the data, it’s already their data. So where do you start? It seems like what you’re addressing, your role in Halliburton is to solve a very big problem but a very important problem, a very valuable problem to solve but it’s also a very broad problem. I mean there’s data everywhere. I mean it’s everywhere and we’re creating it at ever increasing values. What advice would you give to all the people on the podcast listening to this thinking yeah, I can’t use that in the same situation and we’ve got all these data and we’ve got all these new data we’re now getting, all this new technology and sensors. How do you go about getting a strategy to mind that value?
Dr. Satyam Priyadarshy (15:58):
So you see, what I have been doing for the last seven years, I’ll say from that practice and I’ve been doing this for other industries before so I know that it works. One is you have as a CEO and as a people under CEO, people already know where the serious problems are and have a tacit knowledge. They may not have a quantification of it precisely.
Nick Earle (16:21):
If you ask them, they’ve got a good idea.
Dr. Satyam Priyadarshy (16:23):
Exactly. Otherwise, they wouldn’t be in that seat. Right? So you look at the experts saying okay, in the last five years, six years, what thing could have really improved? And then you break down the problem into what you call the sprints as we call, you want to run that marathon but you want to break down in sprints and saying okay, I will take … Let’s take this example that I started with rotary pump. If you have deployed that pump for 20 times and it has broken I’ll say at a tacit knowledge perspective, it has broken after every three months of deployment. You have some idea that it breaks after three months. Now I want to really narrow down that problem of why does it happen? So you come up with a solution in a way that I can look at the data close to the three-month period, five days before or 10 days before or 20 days before and see what happened really in that operation.
Dr. Satyam Priyadarshy (17:24):
So you basic, what you do is you take a business problem which is you can say I will say $50 million let’s say and then you break it down the problem into chunks and saying I’m going to look at a problem which I can take a data of six months and if I can generate 2% of that savings then it’s a problem worth solving. So the concept that I say is that look at the data from a proof of value project not proof of concept. Concept is well known that data works, that data science works and the data that you have, actually how much value does it have. And then you start integrating data from many places saying for example it is a pump, you can connect weather data, you can collect if possible HR data, or collect your chemicals data, you can collect your deployment, repair log, parts log. Whatever it is, you start collecting different data sets saying whenever we replace a bearing of this model, it fails.
Dr. Satyam Priyadarshy (18:29):
So you have to start asking questions from the data and then adding on more data sets. So you do it in a step by step fashion. So first phase should be less than around 16 weeks or less on a very small amount of data where you show yes, the data has some value. Then you add more data, then you do another small project called proof of value. Then you say I need these five more data sets to connect with. They could be under different silos and then you scale the problem. So by the third step, you already know how much value you’re able to generate either in cost savings or revenue generation or accuracy or efficiency or MPV. It is anything you do with data has always value. So where you may have heard people saying my digital project failed or my data science project failed, I don’t believe in that at all because no projects fail because every project has a value. You could not scale it, that’s a different issue.
Nick Earle (19:28):
Actually, some of the data that my company, Eseye, and every IoT company knows that actually the data on the failure rate for IoT projects is appalling. It’s some of the industry analysts have talked about 80% of IoT projects never make it past the POC. And the proof of concept, when you double click on that, it’s actually not really a technical problem. There are issues definitely to do with the device. Most people don’t know anything about the hardware design and don’t want to know anything about hardware design and that’s a gap that we failed when we were the module manufacturers for instance, the Quectels and Gemalto’s of the world, there’s a lot of … But also, it fails because they suddenly, it’s like turning a tap on, they suddenly start collecting a lot of data and that’s the point at which they just freeze. I mean they just can’t measure the quality of what they’re getting. They don’t know which data is important, which data is not important. They haven’t got an architecture for what are they processing at the edge, what data do they send back or do they send it all back to head office.
Nick Earle (20:45):
I think that will be much in the view of an oilfield or on a rig or something. The amount of data in terabytes, petabytes and beyond are massive. You can’t just afford to send it back to your corporate headquarters to crunch it, so you’ve got to do edge processing and they just didn’t think about problems of data architecture and big data or insight when they started their project. And so you do see what you’re saying in the general statistics on the industry, in that people, they don’t start off with trying to drill it down to let’s just find out one particular problem and we’re going to chase down that one particular problem. Often they start with a horizontal approach and say let’s collect data from as many things as possible and then we’ll work out what to do with our data and that’s when often, they just freeze and they think I don’t know what to do. So I’m collecting data but I don’t know what I’m going to do.
Dr. Satyam Priyadarshy (21:42):
Absolutely. But my philosophy has been very different. In today’s environment, when IoT sensors or any device that is generating the data, data can be in any format in today’s world because compute is so cheap. And in principle you could really put out a very what I should call powerful machines at the edge as well in a very small factor, so you don’t have to really back call everything.
Dr. Satyam Priyadarshy (22:17):
Whether it’s cloud or edge or whatever you want to call it, fog node, and you can do that. You can develop the algorithms or the models whether it’s scientific or augmented models and you can push them back to the field. Where the challenge comes is that we really can’t make automated yet because we have to really test everything we do in the field and any complex industry, you really have to test and validate more often than anything. It’s the models after all and more validation is done. But the size of the data should not be a concern because if that was a concern then as I said, data native companies will not exist. hat problem has been solved.
Nick Earle (23:01):
Because it’s accelerating fast enough so that it’s not an inhibitor just because of the amount of especially cloud, the amount of processing power that’s available, it’s not a blockage in the process is what you’re saying.
Dr. Satyam Priyadarshy (23:17):
Absolutely. It’s more about business problem that we want to solve and whether you really need to look at the data from one month or should I just only look at other one week because depending on the workflow we are talking, we could be really creating more value within one week’s period of work of data and it should be good enough for us. Or something we might have to look beyond it, so most of the things we want to eventually do in real time. And so that means you don’t really have to process petabytes of data all the time because we’re not … Any of this industry may be certifying these if they were to deploy 40,000 sensors, maybe they will get the data in terabytes because no IoT sensor sends data in gigabytes.
Nick Earle (24:00):
Yes. I noticed a statistic that … I don’t know how up to date this is but I’m probably speaking to the person who does. An oil rig, I heard and it was a few years ago, an oil rig or excuse me, oil refinery could have 10 million sensors in it. Now I’ve got a feeling that’s about a four or five year old statistic. Is that broadly there or too small, too big, what would you say?
Dr. Satyam Priyadarshy (24:32):
These are numbers written by lot of people. I’m not so sure, I’ve never seen them before. Like I’ve not been to the field so I can’t say that for sure. But how do they calculate and what does the device mean, what does the sensor mean, are they really internet of things devices or are they any devices, right? So by definition, anything that is connected becomes an internet of a thing … Right? But if you have pumps that are running and generating data which is collected by hand, that’s not internet of a thing but it is a data.
Nick Earle (25:02):
Dr. Satyam Priyadarshy (25:03):
So when people write these kinds of articles, I don’t know how many of them have really counted what is connected and what is not connected.
Nick Earle (25:09):
Almost certainly nobody. We were in the IoT business and we have customers in the oil and gas. And let me tell you in terms of true IoT devices, as opposed to something with a controller in it that’s able to spit out data which isn’t IoT, in the sense of true IoT devices, it’s probably in the hundreds in practical terms today. I mean it’s nowhere near that. It might be in the future with maybe with 5G when we start getting private 5G networks in these locations but even then, I think the word or the phrase IoT has been stretched to cover everything electronic and that’s not what we’re talking about. In fact, it’s counterproductive to think that that’s what we’re talking about.
Dr. Satyam Priyadarshy (26:02):
Absolutely. And I think again, it’s the future of the world where everything becomes digital, maybe that is when we will get that kind of sensors, the count of sensors and connectivity but we know we’re close to that. And if we look at oil and gas industry, they’re talking about digital oilfields 25 years ago or something and integrated reservoir management some 30 years ago. So what is integrated and which digital oilfield really exists? Because foundation, when you think of digital oilfield, if everything is connected and you are doing real time automation then it becomes really value. But the pieces of the puzzle are automated, no doubt about it but we don’t have it fully holistic automated digital oilfields.
Nick Earle (26:45):
Another thing people said, and that allows us to transition to perhaps one of the final big subjects was again you talked about 30 years ago, 20 years ago but let’s just say five years ago, people would say the answer to this has nothing to do with human as you were saying. People would say, by now or in the future shortly, it’s all going to be artificial intelligence. It’s going to be machine wherein machines will take over, the humans will let go, they’ll stand back. We’re worrying about what we’re going to do for job because it’s all going to be the machines are going to beat the humans at the analysis. They can learn about the pumps breaking, they can learn about the resolution and they can give you from reactive to proactive preemptive.
Nick Earle (27:38):
I mean that’s what they see in people’s cars, they see it break and so you take it somewhere to fix and then it would be reactive and proactive. A light will go on saying it’s going to break like it’s going to run out of oil so put oil in before it breaks and preemptive. The Tesla, you get the car and they get in the car, it’s like the iPhone, while you were asleep, software update downloaded. We fixed a whole bunch of issues, you know even you had but don’t worry, you’ll never have them anyway. Have a nice day. So by now, we’re going to be in this world where … or at least entering into this world where machines are taking over. Now, I know when we spoke previously, and I can see you smiling now. I know when we spoke, you have your doubts whether or not about the world of AI. In fact, you even said to me you didn’t even like the phrase AI because of your experience. Maybe you could expound on that a little bit.
Dr. Satyam Priyadarshy (28:35):
Yeah, so artificial intelligence by itself as a subject has been there for 50 plus years. And if you look at even the applications of algorithms that are developed, it has been used by oil and gas industry for last 45 years whether it’s neural network, whether it is regression, all right? It doesn’t matter which algorithm you’re talking about. The world changed on the technology side and the compute side and artificial intelligence is just a subject. It’s like my analogy that I always explain. We never say that we are eating chemistry or we are wearing chemistry, where our clothes are made out of chemicals. Food is made out of chemicals. There’s some chemistry going on, right? Application of chemistry that we’re talking about. So in the same way, it’s an application of artificial intelligence whether it’s related to audio, whether it’s related to video, whether it’s related to data, whether it’s related to text. That is what you’re talking so there’s no box called artificial intelligence.
Nick Earle (29:39):
The thing, there’s no thing.
Dr. Satyam Priyadarshy (29:42):
And when these articles come out saying artificial intelligence in Tesla, is that the same box, can I put it on my computer for doing my statistical analysis? No, right? So it’s not thing and that’s where the confusion is. But irrespective of that okay, it’s a field and it’s important field and it allows you to analyze things that human beings by themselves could not do. That scale repeated task that can be optimized and even can eventually when self learning algorithms will be there, more mature then maybe things can actually improve like you can see those examples in robotics a little bit where the robot can learn and things like that. But where are we? We are far away from it in terms of application. Maybe in some different sector, things that we don’t know. Who knows what’s going on there? But in a practical world, that’s nowhere close to it because it requires in principle all the tacit knowledge that is sitting in your head and all the people who are actually in the field. What to do when, that is in people’s and engineers’ head after 20, 30, 40, 50 years of experience …
Dr. Satyam Priyadarshy (30:53):
And if you look at the history of knowledge management in the companies, we have never figured out a way to capture the tacit knowledge.
Nick Earle (30:59):
Tacit knowledge, yeah.
Dr. Satyam Priyadarshy (31:01):
And that tacit knowledge is what is really what algorithms will need to really make a decision.
Nick Earle (31:07):
So back to your example about the pumps, we talked about asking the engineer who perhaps have been around for ages, if I was going to investigate, that we click on one area, what would it be? That experienced engineer would say you really want to have a look at these pumps because they break in three months and it’s a really big issue and that comes from just that experience, that tacit knowledge. The chances of the computer system saying that to you are probably pretty low and then when they do present with the data, they need that tacit knowledge, that experience, that thing that we can’t codify to actually interpret the data and prioritize the actions. So you’re saying I guess it’s a combination of the two.
Dr. Satyam Priyadarshy (31:55):
Absolutely. A very important combination especially in the oil and gas industry, having worked and consulted in almost seven, eight verticals before I came here. And I can tell you in oil in gas industry, the people have so much knowledge because the processes are complex irrespective of what section of the workflow we talk about. In India though, the energy sector, if you look at it, it’s a very what you call science and engineering driven industry. And so a lot of these people have so much tacit knowledge in them that really needs to be captured and can be taken advantage of. For example when you are drilling right, if you think of it, the person can feel and say I need to rotate this much, the drill bit.
Nick Earle (32:39):
Dr. Satyam Priyadarshy (32:40):
Now, to get the algorithm to do that, you have to really look at so many things. First, you have to understand what is the force coming back. You see? And then you have to really analyze what I did in the past few scenarios like that. So it will take some time and that’s where I’m saying that this … But on the other hand, those people who are experts, they have this sense that they hear the sound or they feel the vibration and thus, I do this. Now they don’t believe the data, the data or people don’t have the same knowledge as the engineer so they have to come together. And I feel that the people who have the tacit knowledge, they can be trained with the data knowledge and that is what I call the talent transformation process.
Nick Earle (33:31):
Not the other way around?
Dr. Satyam Priyadarshy (33:31):
The other way around is hard because you can’t get the field experience, right?
Nick Earle (33:35):
Dr. Satyam Priyadarshy (33:36):
You can only get what the field people tell you.
Nick Earle (33:39):
Dr. Satyam Priyadarshy (33:40):
But the field people have so much, so you really have to show them a graph saying this is what happening here, what do I do and then you codify that.
Nick Earle (33:49):
One last question there. Because this opens up so many different questions. The combination of the field experience to give you the tacit knowledge and the ability to make that instinctive human judgment that says this is right or this is wrong, the vibration, it feels right, doesn’t feel right, we don’t know where it comes from but we can do it. And then the machine playing its role, analyzing things very quickly as well. Does that mean in your role at Halliburton, do you train people, do you recruit different types of people for the world that we’re heading into? Do you look for certain types of degrees? I’m reminded listening to … Not in the field of oil and gas, but the conversations certainly I’ve been involved in for many years, lots of different industries that I’ve worked in, people are saying MBA students are useless because they don’t have any of the practical experience.
Nick Earle (34:48):
Whereas of course, the MBA schools will think that they train the future lead and everyone’s got all the knowledge they will ever need because they got an MBA. Now, you take that and you’ve now applied it to an oil engineer. Often like in the case of our daughter, our daughter went to work for several years then did an MBA and actually, she felt at least she was much more valuable at the end of it, than if she’d have done it the other way around. So do you in Halliburton get to make recommendations in terms of what types of people you employ given this world that we’re in already and heading more into or do you actually run internal training courses on how to get this combination of the machine and the tacit and the human working in harmony?
Dr. Satyam Priyadarshy (35:36):
Yeah, that will answer all those parts in an interesting way. So as an academic professor, all my students, for MBA especially, I tell them there’s no point doing MBA after bachelor’s. Get a couple of years experience then do an MBA then you know what mistakes I did or what they didn’t do.
Nick Earle (35:53):
She got it right.
Dr. Satyam Priyadarshy (35:55):
Then the value of MBA becomes really important. Otherwise, you’re like taking any other course. You passed it and you’re done. And that’s my first recommendation to most people. In terms of since my background was not a direct oil and gas so I know that it’s all about generating value from the data and I think in that way, most of the team that I have built initially are all people from different fields of science or engineering or other areas. So I have a PhD in atomic physics/astrophysics. I have a PhD in chemical engineering, mathematics, economics, things like that. So they can think totally differently. But then you pair them up with the tacit knowledge people, the subject matter experts, that helps. And then over the years, we actually developed our own training program not only for individual contributors but all the way to the leadership because one is you have to really keep these people also in house. There’s interesting challenges in the world and especially the data science people, they’re high in demand.
Nick Earle (37:05):
They’re high value.
Dr. Satyam Priyadarshy (37:09):
Exactly. So the way my philosophy has been that for all the data scientists, give them interesting problems, don’t give them and put them in this box and do just one problem. If they are doing multiple problems at the same time, there’s no problem with that. In fact, they love it because then they can think of it, I have this kind of data or this algorithm’s working. But I have this kind of data, why is this not working? So they have their own compare and contrast going on within themselves. And then they’re interacting with different domain experts so to say. And that helps them really think beyond a simple problem and then it’s an exciting environment to work.
Dr. Satyam Priyadarshy (37:45):
And that’s how we have grown this center in Bangalore and in Houston, and Columbia and any other places. We are working with so many people. But the training part we developed is because the same people who are actually working on a problem, they are actually teaching the house of this field to the domain experts. So when they ask questions, they learn from the domain expert, why are they asking this question, why can’t I find this? And when the domain experts say how to do mathematically or why is it like this, they can explain it. And then this synergy is significant. And just in last two years, I think we have trained over thousand people in the industry.
Dr. Satyam Priyadarshy (38:29):
And hence, I don’t really worry about the talent pool side of it. In fact, one of the hats I wear is the managing director of India center and in the last year, I’ve hired about a hundred people from all different fields. So you know it’s a fascinating area to work in and I think the potential is significant. As I said, the opportunities are significant because we have only scratched the surface of the industry. And if we really have the desire to build full implementation of IoT sensors properly, getting the 5G network, or beyond 5G working which will reduce the cost to move the data and moving the speed to the connectivity, then I think we will have to build what is called a digital twin of digital twins. And so that’s a fascinating field and of course, the tacit knowledge is not going anywhere. You call it augmented analysis going on.
Nick Earle (39:34):
Yeah. It’s a fascinating story, it’s a fascinating journey and it’s also reflective in some way of Halliburton’s journey as a company into moving more into data and data into services for the clients and as you say, all the efficiencies. And then the whole subject of digital twins is something that we do plan to cover as well in a future podcast. But for the moment, we better leave it there because we covered so much ground so I think I can just finish by Satyam, thanking you for your time in sharing with our listeners about your journey and your thoughts on how to go about it and for also assuaging those people who are perhaps who are out there being concerned about whether machines will take over, that actually you don’t believe that they will and that we’re all going to have plenty to do going forward in future years.
Nick Earle (40:29):
So with that, I just want to say thanks to everyone for listening. You’ve been listening to the IoT Leaders podcast with me your host, Nick Earle. If you have any feedback or questions on it, remember that we do have an email address which is iotleaders@eseye. That’s E-S-E-Y-E, dotcom so we’d love to hear from you any suggestions or any subjects that you would like us to cover. As you know from this particular podcast, we can actually go very broader or even vertical into industry and we’d love to hear from you as to what you’d like to have a discussion about, even when you feel you’d like to be a guest on the show. So let’s leave it there. Satyam, thank you very much for your time. And for our listeners, we’ll see you and talk to you on the next episode.
Dr. Satyam Priyadarshy (41:15):
Thank you, Nick. Bye now.
Nick Earle (41:18):
Thank you. Bye-bye.
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