COMMUNICATION
INTELLIGENCE
Data is Everything

Data is Everything

 In today’s and tomorrow’s world, data define everything

In today’s episode we talk to Ieva Martinkenaite, VP Analytics & AI at Telenor as well as Omair Ahmed Khan, VP Data Analytics/AI Projects & Governance at Deutsche Telekom about everything surrounding data. Topics covered range from what is needed to work with data, what is the unlocked potential becoming available while working with data in your business and how you can find a common language to talk about data with people who don’t work actively work with it but use it

So, what did they discuss about the importance of data in businesses?

Svitlana Bielushkina

Welcome to the human centric Podcast. Today I have a very special feeling and I can describe it as I am going to kindergarten, expecting a Santa to come and bring the presents; ah, do you remember that excitement? That’s exactly how I feel today. Because the key discussion will be around data, data mindset, data literacy, data culture and data driven leadership. And working in HR, I realized what tremendous impact this trend has, and I have two amazing guests. For you listeners, the guests connecting here from Bonn and from Oslo, Norway. We have Omair Ahmed Khan, who is our VP analytics at Telenor group. And we have Ieva Martinkenaite, who’s also VP data analytics in Deutsche Telekom. Welcome. Hello there. So, Ieva, Oimar, tell us a bit about yourself. How did you come to VP analytics; you have almost similar positions in two different companies, is this correct? 

Ieva Martinkenaite, Omair Ahmed Khan

Hello, it’s nice to be here!

Ieva Martinkenaite

Thanks a lot. Yeah, that’s actually a great question. I was contemplating a little this morning. I mean, how did I come to this place? And you know, I started really back to kind of my childhood, I said, like what I was interested most in my life was uncertainty. I actually can thrive and uncertainty. This is something I love. This is where my passion is. And I was always interested in something I didn’t know. So what I didn’t know before I came to Telenor, which was six years ago, I didn’t know much about technology. Actually, I have a background in strategy and organizational theory. So I’m an orc person. But what I always had passion about is technology. So I said, okay, I need to learn about this stuff. When I told him, I understood that telcos’ technology would be based on data in the future. So I came to research, doing my stuff. And then, pretty fast, after a couple of months, maybe a year, I got an opportunity to learn more about data and AI. And I was sort of part of the journey to build capabilities in Telenor to set up collaborations with research. I currently lead a team of data scientists and machine learning engineers at Telenor R&D, where we develop capabilities, solve problems, and show the future of talent.

Svitlana Bielushkina

Listeners, I was working in Telenor before I joined Deutsche Telekom. And we have been in the same leadership program. And I remember you ever when you walked to me, and you put sticky notes on my back where you wrote soulmates? And now I really understand, I’m also thriving in uncertainty. That’s what we have in common. But tell me a bit more. You’re not Norwegian. Are you an example of diversity?

Ieva Martinkenaite

I’m not Norwegian. I’m not a programmer. Likewise, I’m actually the youngest in the management team. Furthermore, I am almost the only woman, I would say almost, we have also comms manager in the management team. You know, my employees could be my father. So I am sort of an example of low range situation, right? Like, who is she? And yeah, this is where I am. 

Omair Ahmed Khan

Okay. So for me, it’s been actually a bit of restlessness. So I always love a challenge. In my career, every two or three years, I’ve always taken on the biggest challenge, which I wasn’t aware of, because I wanted to solve it. I am a chemical engineer by education, so what am I doing in data? That’s exactly what I did when I started, I would try something. Once I’ve done this for three years, I’m bored, let’s try something different. Then I realized that data is everything when I was head of data for an SAP program and DHL a couple of years ago. In today’s and tomorrow’s world, information defines everything. That’s what made me fall in love with it. And I did that, and I set up a large data organization there. And then with DT, I realized is always sitting on a goldmine of data, there’s so much we can do and how little we have tapped of this. And that excites me. The fact that there is so much potential out there this untapped, like this gold reserve we are sitting on where we have not done much of it. That excited me. And here I am. And I really enjoy inspiring, challenging, pushing people to understand how they can do their jobs better day to day based on the information that they have at hand. And that’s what I don’t do at trying to push the boundaries and trying to get people to understand, realize and tap the potential of this cool risk in a marriage. I’m Indian. I work for Deutsche Telekom. And I don’t speak German. So if I can imagine, that’s also an interesting paradigm. But I think there was a time I would have never thought of joining Deutsche Telekom. Yeah. But now I think it’s become a really international organization where diversity is really appreciated. And it’s like, and I really feel at home here. So I’ve been here for three and a half years, I should learn German for other reasons, my daughter goes to German school. But I did not feel that it’s something which blocks me from doing what I do every day. I think the organization really is gone leaps and bounds. And we are now truly international organization in that sense. And that way, it’s good. Yeah.

Svitlana Bielushkina

And I can confirm that. But tell us a bit more. What is this happening in telecommunication? What is this boss about data? What do you observe today and what you do to see tomorrow in this field?

Omair Ahmed Khan

So the thing is, every telco company today is asking this question, what is our future? Are we going to be just a commodity company like electricity or water? Because right now it’s become commoditized. And right now, they’re all asking themselves the question, what is our future, and then they realize the acid that they’re sitting on the air, realize that there are a lot of data driven models and lots of things you can do to really diversify your portfolio and go into tomorrow. So that is why most companies are asking these questions, and many of them are focusing on data as a thing, because if you look at what we have, we have a lot of data. And if you use that in a safe and secure way, you can have a lot of business models. And a lot of way you can add more personalized services to customers and many, many more things. Because there’s a risk that we become commoditized. And the second thing is if you look at it, this is my personal belief a couple of years ago, the telcos are primed to be in the cloud market. They were kind of sleeping. And then you had people like Amazon and others came and killed us in that market. So right now, we’re sitting on this next big thing. And people realize that they have to work on it to provide better services, customers products, and then that will define the future rather than being just commoditized. And highly regulated as just a pipe provider. Yeah, so that’s where it’s a transition phase for most telco companies right now to redefine themselves. What does it mean to be a telco?

Ieva Martinkenaite

I fully support that view. Omar is exactly the same here in Norway in Southeast Asia, where in the Nordics where Telenor operates, it’s exactly what you’re saying. So we are on the edge of redefining who we are, is almost like, you’re going to ask like DNA? I mean, who we are, are we a technology company? I mean, it’s a question that I think most telcos are asking yourself, what does it really mean? So this is also a sort of type of dimension where we are having, so what does it mean, if we are defining ourselves as a technology company? Well, we need to empower the data that we have, we sit on this big chunk of gold. And then we need to also change our skill base. I mean, let’s face it, if we are to become a future technology company, we need to empower data. And we need to have new people, people to rescale is important, but also having new people, those who understand software, who can build software, those who can, you know, build models, find insights in data, productive, fie those models and for solving business problems and serving our customers better. And I think the third, I don’t know which one is third, or the second significant aspect, when suddenly telcos realize that they’re not owning a full value chain anymore. There are partners in all parts of our four old value chain that are disrupting us, but also that are collaborating and competing with us at the same time. So it’s sort of an interesting move to nine in the telco business. So how do we collaborate and compete with Amazon? And also, the other telcos? A smaller or bigger ones. I think that’s a very interesting transition time that we are experiencing.

Svitlana Bielushkina

You mentioned the skill set; so the new skills emerging and given that it’s a human centric podcast, I’m going to double-click on that because every time I hear about data and data science to me sounds like science. Hence, like PhD in physics sounds difficult, it sounds super difficult. But, you make it so easy. You’re looking at your backgrounds you have a coming from an organizational strategy, and you know from your background somewhere, can you demystify to our listeners? What kind of skills you need to have to be future proofing to work in the area of data tomorrow? Just a couple of more words, what exactly is it,

Ieva Martinkenaite

I can start by saying the rule of three, I use the rule of three and here it is the first one, we need to have a data engineer, a person who will actually understand a will build a data lake, but also understand the data, what type of data you need to build models and process it. Number two is a data scientists and data scientists is not necessarily the one who has a PhD, not necessarily but those who build models. And number three expert domain expert, you will never be able to build data products without understanding a problem to solve. And that’s the rule number one, rule of three. This is the team, this is the future teams that I see another rule that I’m using, you need to have three times more doers than managers in the teams, we’re gonna have to learn that, that we need more doers, the ones who built data, the ones who process data, the ones who build models, and the ones who productive, I’d put it back to the production. So think this data engineer or software engineer should be also added in as the one who take those insights into products. So, of course, I’m coming more from the kind of groundwork, the ones who do stuff, the build stuff. But I think we need more and more those people who could actually take our data, understand the problem, we can use technology for solving it, move it very fast to production in these very fast iteration cycles.

Omair Ahmed Khan

So I relate to what you’re saying, I’ll give you an example. From an analogy. It’s like driving a car. Yeah. So the guy who rides a Formula One, race is also driving a car, the person who’s doing a rally is also driving a car, and you have driven to the office, you’re also driving a car, I’m also driving a car to 18 Wheatstone to drive a car. The thing is, you don’t need to learn how to become a Formula One driver. Yeah, I don’t need to learn how to become a radical driver. So the thing is, in the data value chain, there are different people at different stages of expertise which are needed. But what is very important is all of us should know how to hold the steering and put the gear and go one step forward. If you don’t have the basics, then you cannot become a normal driver or a Formula One or rally driver for that matter. So this is where also I look at it, all of us need to have the basic fundamentals of understanding and realizing what kind of data we have, what kind of data can we leverage, then what you do with it, then comes the expertise level. And as ever said, then you have different people in the organization who serve the need for the different purpose. Now, for example, you are an HR person, you don’t need to become a data scientist, but you need to understand the potential of what you could get from your data. So when you talk to a data scientist, you don’t talk to different languages, the data scientist understands your domain, and he’s an expert in doing that. So it’s not about everybody having the same level of expertise, then the company becomes that. So you have to realize that we all need the basics. And then you go to the level of expertise, depending on your role and your performance in the organization. But what is important is if all of us don’t know how to drive, then there’s no question of becoming a racing car driver or a Formula One driver or anything else. So that’s where I look at. So the basics have to be there. And then it’s all about tools. You know, for me a lot of software, I believe, is common sense plus index, you put the common sense part of it first saying that, okay, I have access to this information from this, I could get this. You don’t need to know how you have people in the organization who can do the hole for you. But if you understand the what, and that will make life very easy. A lot of our leaders in the company don’t think of it that way. They think data science is a niche, there are few nerds sitting in the corner over their small team over there is our data. And more often than not Yeah, the data guy comes and says I have an answer 0.2 And he’s super ecstatic and the business like What do you mean by 0.2? Yeah, yeah. So there is this gap in terms of language, which has to be improved. And then once you have the basics, you know, you can reach out to people who can then help you with the expertise. So again, for me, the philosophy is not everybody has to get the like whatever said if you need to get data in a particular place, you have the engineers who come in who understand, but the domain expert does not explain to you what the data is and why layover, as an engineer does know what to do a data scientist has no clue because he does not understand the context. All levels are required in that sense to sum it up, listeners. 

Svitlana Bielushkina

So again, was he ever was talking about the roles in the data domain. So by data engineer, data scientists, high building models, and then domain experts, it would be the product owners, the people who would be driving services, be it financial services, or TV services. Thus, that would be domain experts, who would have to understand the problem to solve and create value for the customer. And together, those are the key roles and what she was saying on that, is that the language of data, data literacy, it becomes more or less for everyone, yes, we have to understand the language of data.

Omair Ahmed Khan

Yes. So if you all speak the fundamentals, and it is not a very big barrier, everybody understands the fundamentals. And then depending on your interest, and also you can see if you want to go deeper or deeper, but there are also people in the organization who do that, you know, as an example. So the getting everybody on one common plane is very important. The biggest thing is realizing what you have, what information you have at hand, and what can you do with that information? What could you do? And once you realize that, then the business starts driving the data and towards driving the analytics and AI and such, like a lot of cases right now, unfortunately, I don’t know if you agree with me, technology is trying to drive it. Yeah, which is where I think we miss our technology is trying to drive the business, it’s actually the other way around. Because the realization of it is the business part of it, you know, and as long as the business don’t get it, it will never get the shape that we need to. So we need more and more people to understand the basics. And then you have the experts who can do it.

Ieva Martinkenaite

And then here’s care Councilman org design hat. Because we’re usually when I started the project with a business with my data scientists and engineers, I asked this very simple question. Do you have end to end control? And then they’re looking at me and say, What do you mean? I mean, the first access to data, we often say we have lots of data. But when we actually start digging, we realize that our data is in different pockets in different places, different ownership, different governance structures. So it’s a mess. I’m not talking about talent, I’m talking about our general challenge, and we are in this transformation. The second answer I’m getting, Oh, yeah. But there’s the guy in technology, who is responsible for getting the new tools on, who’s building the data lake. And when the business and commercial are the ones who own the problems. So here is the design or the design issue, right? If you don’t have End to End Control, for getting the data, pre-processing the data, building the model iterating with a Problem Owner putting in production in one ownership, we lose a lot of time. So our go-to market, our issue with data products, is still high. So I agree that, you know, this basic data literacy, understanding what data we have, understanding how we can solve problems, current concerns we have in business to serve our customers better. To optimize how we operate our networks to offer new products. This is a necessary step. And I think I would add this org design thing. That’s the next step. Right? So how can you build those data products seamlessly, with fast go to market,

Omair Ahmed Khan

I think you hit the nail on the head. Data Availability, and access, is the biggest plague for every large organization who’s not data centric by default. So this is the biggest benefit that the Googles and Amazons and Facebook have because they were born based on data. And they have already realized that if you don’t structure data upfront and have it available, you will never be successful. This takes more than 50% of time for any use case is to find the data and get access to it. Forget about processing it. I have so many data scientists who spend 70% of their time waiting. Yeah. And they difficult retain them for because for them, they’re like, you know, you don’t give me data. What can I do? Yeah, they do make synthetic data, they play around with what you have on the internet or whatever, but having data available, it’s like trying to cook a meal and not having the ingredients. Yeah, you know, you go home and say, All right, I’m going to make a great meal for my family. And then you say, Oh, I have no ingredients. What do I do? I need to wait till somebody gets it from the store. I go get it myself, and that’s exactly where we are right now. So having information available to the people securely in a controlled way which is allowed as per the law and that is the biggest play. If you solve that. I can make a claim that we will reduce our time to market by 50% off the board, I have so much data on This data for data. But this is exactly what every single company has. And this is the biggest transformation, we need to make sure all the employees have access to the information they need to do their jobs better.

Svitlana Bielushkina

And this will be happening today, right now in our organizations. I have to understand the basics of data and speak the same language, so we can connect, and we can solve the problems together. Do you see any other implications on the softer part of the human part, which is happening as a part of this transformation? What about leadership?

Ieva Martinkenaite

Well, my own example of it, I’m lucky in telling her I said to my boss, I said to my boss of the boss, I said, look, I’m lucky because I was able to hire smarter people than me. I always aim for that I said, if you were smarter than me, and I can learn from you, as a leader, I will be the best as I mean, I will be award rewarded. So I think the leaders of the future are the ones who trust, respect, and give freedom to people that actually understand a field and these respects’ software, this will be data, this will be machine learning, modeling. I mean, you name it. So our role as leaders, is to leverage that knowledge, you know, trust the knowledge of our people, and also play the bridge builder role. If you asked me what role I play, oh, VP of analytics, yeah, you’ll find it, I’ll link it. And that’s not important for me. What’s essential, I’m a bridge builder. And I’m a connector translator, between the business layer and analytics layer, which is still facing it, it’s still a very, it’s not yet there has completely, totally resonate with what you’re saying. 

Omair Ahmed Khan

I always tell people, I’m the dumbest guy in the room, you should always hire people who are smarter than you, and then support them and give them an environment where they can excel. So at the end of the day, you know, if you ask our role is role rock remover, all I do day to day is understanding this person has this problem, and they’re not able to get access to this information, talk to people remove the roadblock, let them go ahead. Because, as leaders, let’s be very honest, I cannot teach my data scientist how to do data science. He’s an expert in that, or she is an expert in that they know a lot more than I do, because they do a day to day. That’s not what I do. But I understand the organization the way it works, and then you have to make sure you find the clean path for them. So I think it’s also important for leaders to understand that there is an access issue, there is an information blockage problem. And as long as you address that, then you see the best coming out of these people. You can see the sparkle in their eyes when they’re like, Wow, we have everything we need to do what we can do. It’s amazing.

Svitlana Bielushkina

I’m smiling here because it resonates with me. And also in our last episode is Jonathan Abrahamson. I heard of digital and Deutsche Telekom. That’s exactly what he says. He said, I’m hiring smarter people than I am. And I’m leveraging on the knowledge, and I’m connecting, and I’m being the bridge. So that is a trend. Yeah, but say a bit more about the softer competence of leaders, which becomes really important in this data driven machine learning AI worlds. You mentioned, networking, you mentioned connecting the dots, what other leadership qualities standouts in a data driven company?

Omair Ahmed Khan

I think there’s a lot of people somehow notate AI and data to also appear cost-cutting or, you know, it’s a bit of, I think it’s also important for us to realize and tell people, it’s not about what you can save. So what more you can do, it is a poker term, you know, you save money left on the table. So it’s always the case that you need to make your people understand that there’s a lot more you can produce an output based on the information and the access that you have. And then you rather inspire them to do more and become more because, you know, if the same number of people produce two times the revenue, there’s not a question of the, you know, your increases in such like, so I think I don’t believe that there’s this big negative, yes, AI is automation and all that is happening, but a lot of things that people can do, and they have to take out the stigma. And, guys, if you realize what you can do with the information that you have, then there’s a lot of scope for you to grow and go ahead. And I think the mindset really matters over there. And you know, this we have this rescaling program, I hired 17 People of this rescaling into my team, and I was blown away with the result. People who have this, you know, not know it all, but learn it all culture. It’s amazing. And I’ve seen people from HR from finance and many other areas, who changed the job description when they are 50 or 55 years old. And now they become data scientists, and they’re excelling it. Really I’ve got superb feedback, and even my own All team members who are this super experienced data scientists are like, wow, we did not expect this kind of change coming up. And that’s really positive. So I think there’s a lot of scope for people. It all depends on attitude, if you want to learn new things, if you happen to learn it, because all of this is new. That’s the good part. When you and I and every one in university, these things didn’t exist. Yeah, these are all news. Everybody’s learning.

Svitlana Bielushkina

Yeah, so what you are saying America’s ability to develop people, its ability to inspire for the future roles to come. And what a man is talking about is the res killing a dodgy telecom where you can apply for our AI Academy. And based on your motivation, and learning agility, your success, and your risk killed retrained, and you get a job with the data fields. Right after graduation, yes, more or less, and you start working on stuff.

Omair Ahmed Khan

Yes, the first batch finished by the first of June. So I have got 25th graduates, 17 of them are in MIT. And I’m very exciting, super, super results. And now we have the second batch coming out, we will have more than 100 people in this area very soon; this is really cool. So how long does it take to rescale, our program right now is six months off the job. So six months, we have a university grad program that you really go through. And then we have DFK AI, one of the German authorities who actually gives you a certificate at the end of six months, that’s off the job, then six months on the job, you actually work. And then you learn skills, because there’s a difference between what you learn in university and what you do. So the whole program is one year, six months off the job where you have pure learning, and then six months on the job, but you’re actually working, and then you’re fully productive. As I told you, the results are very nice, because we have these people who have experience in the company. They’re not shy. So if you’re a university, or you come to the company are afraid to ask questions, these guys come and challenge my guy saying, You know what? I learned something differently. Why are you doing it like this? And then it makes the experienced guys also question themselves. Okay. Alright, I need to think again. So that kind of, you know, collaboration. It’s been really cool. I’ve seen the results. And I’m very proud that all the guys went, and now they are part of programs and then working on real life projects. No more theory stuff. They are delivering value to Deutsche Telekom. Yeah. Wow.

Svitlana Bielushkina

Anything from your site to add on the softer part of the leadership and maybe what talent or is doing to have the leaders you need to have the data driven company in to develop talents in Telenor?

Ieva Martinkenaite

There’s this demystification of AI ongoing, and I’m so happy about it. So suddenly telcos, and legacy business understood, you know what, this AI is not, you know, something of a myth that we cannot solve, actually, let’s get back to basics. Let’s start building our skill base, rescaling upskilling. Hiring, let’s start really investing in our data. Let’s start prioritizing problems we can solve with these new tools. You know what, I’m the happiest person on earth now because I suddenly realize that, well, all this hype about AI. General Purpose AI is sort of disappearing. All of these moonshot projects like self-driving cars. Even Elon Musk doesn’t have like a proper Robo taxi in California up and running. So I mean, this all hype is diminishing. And what’s happening now also in Telenor is that we are realizing that we need to get our data pipelines in place, we need them on protozoal axis we are running these programs with for rescaling. I mean, our own people are we select those people for advanced machine learning journeys, we have those data science journeys for leaders that do not ask them to really have good core coding skills. But just to get that literacy up. And this is absolutely fantastic. So I think so much work to do now maybe even different, me and Nomad, because now suddenly we are in the role of, you know, really upskilling people helping telcos to build that literacy base and data. And this is a fantastic journey, I think, to go forward.

Svitlana Bielushkina

When you were preparing for the interview for this podcast, as you have also mentioned, and I found it quite interesting approach in talent or that you’re developing not just individuals and people who are eager and who are interested to do that. But you are developing teams. Yeah. Tell us a bit more about how do you do that and why teams?

Ieva Martinkenaite

Correct. So we have programs where we put teams of people, a couple of guys from the business units from commercial units, a couple of data scientists and engineers, we make as I said, the rule of Three small teams, where we believe that the breakthroughs are happening in sharing knowledge and building products among diverse experts. So I always say that I don’t think that we will move a needle, if we have three, four Ferrari, right drivers and Telenor exceptionally good data scientists. Instead, we should have hundreds of people who can steer the wheel. And who could understand, you know what? For my problem that I’ve been pondering about for 20 years, suddenly, I have some support in Telenor and in the line organization, he could actually help me to solve something that Amira said before. I mean, data scientists will never be able to solve a business problem without understanding it. They can crunch the data, they can give you some scars, they can make you some prediction algorithms for you. But you know, there are no Why were we doing it? Well, the problem we saw. So what we do in Telenor, we are realizing that you know what, for training, and building knowledge, we need more and more team expertise. This is where it, I think, coming to be the future, because you know, we’re not going to work alone, we can have the best data scientists in the world. But they won’t solve our problem.

Omair Ahmed Khan

Absolutely. I think it’s a very, very good point. Also, you mentioned organizational alignment, I think more, and more companies are moving data from it functions to the business functions, the ownership of data is growing more and more to the commercial functions and such like, because they believe that it is no longer an IT area, it’s like even digitally, you notice right now it’s coming out of it, and going more into the business areas, because they realize that it’s a business which drives it. And I completely agree with whatever is saying, if you don’t have enough people in the business who understand this, you might produce the best result, it goes nowhere. You know, all companies love to do POCs. And the point is how much of what you do actually hits the road. And to get it to hit the road, you need the business, you need the frontline staff, you need the business to take it to the road. Otherwise, it’s all theory. Very nice. It looks beautiful on a PPT, but it brings no value. So that’s why I think more, and more organizations are trying to say okay, data driven, the whole teams are now moving into the business function rather than just being a pure it, or, you know, technical service, so to speak.

Svitlana Bielushkina

If you imagine you have from the point where we currently are was give some advice to the younger talents as to the people that seem talent or in Deutsche Telekom, or even to the listeners outside of our companies. If they want to go into this area of data, they want to become much more data literate. They want to become data scientists tomorrow. What advice would you give those people how to start? Because that might sound scary as well. It sounds scientific. How do you start? What would you say?

Omair Ahmed Khan

One step at a time? I would say one step. The really difficult thing is to get No, I think getting off the couch is the most difficult thing than anything else. Believe me, I tell people, if I can do it, anybody can do it. So it’s not that difficult. It’s just a matter of time. And it’s a matter of attitude. Yeah, it’s like doing anything in life. If you feel you can do it, and you want to do it, you will do it. It is not difficult. It is not that complicated. And again, as I said, you don’t need to become a Formula One driver, you just need to know how to drive a car in the autobahn, and you’re good.

And it takes a year. Again, the thing is, in a year, we make you into a data scientist. And if you really want to become a data scientist, that’s invest. If you go to university, you spent four years. And right now you’re saying in a year, you can change your career. So we’re just still shorter than doing that. I saw it. Yes. It is intense in the six months is off the job six months, you’re actually working. It’s like you’re training and then you’re actually working. Anything less than that. You can be an analyst to understand data, but you will not become a data scientist. Yeah. So I don’t think everybody should aspire to become that as well. It depends on what you’re interested in, to what extent it is, if you do want to become a data type is your career shifter, then spend a year, really give it a year and you will do it. And the point is, this is the right time in the market because everybody’s rescaling. And they appreciate. Nobody has 20 years of data science experience because it doesn’t exist. Everybody’s new. There’s a lot of people around you who are doing it right now. And it’s great to do it. Yeah, it’s not that difficult to get started. And you will get there.

Svitlana Bielushkina

Thank you, man. You have anything to add from your site?

Ieva Martinkenaite

I know what to add. Yes. Start small you know, you don’t need to be a data sign it you will not be I’m not going to be a data scientist ever I don’t think, but that’s not my role. I think there are so many opportunities in the future of AI and organizations in the telco business as we’re getting that minimal level of literacy and data will be an asset. So think that’s one, start small. But I also what I say dream big, I think this is important to remind our people that there’s interesting stuff coming out a lot of these big legacy businesses, the telcos, electricity companies, the finance the banks, they will transform, they will need people with at least minimum data literacy skills. And guess what, those who are already started, just get the Udacity course just get any other course available. So it actually for free. Those will be more wanted. So just get started. I mean, and by the way, there’s so much help in the organization, we are all in this learning curve. So I would say that don’t be shy, we all in this. And certainly there will be more and more money put in the organization’s full learning data software, and stuff like that going forward.

Omair Ahmed Khan

I would recommend, you know, for people to there’s a TED talk, it’s called the 20-hour learning. So I would recommend you see that because what it says is, studies done across it takes 20 hours for somebody to be good or learn something, you will not be an expert, you know, you will not be the best data scientists on the planet. But for you to understand the basics of anything, it doesn’t take more than 20 hours, and it’s 20 hours is exactly the time you need to understand the fundamentals of it. See the TED Talk. It’s very nice. It basically says that, you know, we have here this 10,000 hours rule they have if you have to become an expert, you have to spend 10,000 hours. Yes, if you want to become a Tiger Woods, in golf, you need 10,000 hours. But if you spend 20 hours in playing golf, learning golf, you can have a team got so yeah, input barrier is not that high, get started, put the 20 hours and see where you get. 

Svitlana Bielushkina

You know, I haven’t seen the TED talk on that. But I in our Explorer journeys in GT, that’s exactly what we talk about. For our listeners who do have explored journeys, where we invite people to explore what data literacy what data, language is data analytics. And the first step is the most difficult is just starting, you know, you might be scared, you might feel you have no time. You might sometimes feel ashamed as well, because everybody is re-skilling and learning, and you are lost, like what’s happening rounds me What can I do? And picking these people up there and say they just start Yeah, start doing small 20 hours is enough. And then you see if you like it?

Omair Ahmed Khan

Yes. And I guarantee you if you actually spend the 20 hours, I’ve tried this myself, it has to be 20 hours, make a simple extensive write down how many hours you spend on it. And then at the end, if you see you have not learned enough, you can come back to me, but believe me, I have tried it. It really works. Because we don’t put in the 20 hours.

Ieva Martinkenaite

I want to add one thing, you know, it’s about incentives. I think it’s important for companies to make explicit incentives and learning I mean, what we do and so on, or we have this 40 hour challenge where everyone is put on everyone’s KPIs on top leaders and all of us. So basically, I think it worked for us, what are we seeing now? You know, because it’s hard to get from the couch, right? I mean, it’s you need to push people to get out there, I’m spending hours over the year for learning, I can choose whatever I want. There are some recommendations. But I think let’s put those explicit incentives on people to learn, because this is not kind of obvious. There’s a lot of stuff going on, we need to do there now. But put those incentives.

Omair Ahmed Khan

I do this also for my team, not for the organization, but we have a 20% target for the employees that they have to do training. And it’s 100%. If you train, it’s more than 100 people if you learn and taught somebody else. So it’s always in for all my team members. They have it in their annual target saying that you need to spend, if you want it, you have to learn yourself. And if you teach more than five people that I give you one 50% of the target, and it’s black and white, it’s up to you to take if it’s all yours. Yeah. And then the only ask, okay, what have you learned? And if you can show what you have done, and he said, I have learned this, I’ve trained five people there you go, you get the higher bonus than you were expecting. And we really push it we have to really incentivize. I agree with you. It has to be incentivized learning. People need a bit of a push, you know, ultimately, but the most sad thing is people who don’t learn at all get that ultimate post, which is not what we want. Yeah, so if we see this small dosage of pushing, which is more critical, right, love it.

Svitlana Bielushkina

Love it. Learning Culture is the key, and I love the 40-hour challenge. In the intelligent or in what you are doing here, oh man. And I think it will be more and more in the future. So they’re learning agility, how fast you can learn and unlearn and move on is the most profitable skill and ability a person can have. And if you put $1 into yourself into learning and skewing, you will get higher return on investment if you pull this $1 to any banks. Here. Time is running. I do have two more questions for you. One question came actually from our listeners, and who wanted to get your perspective on AI, that is coming our way. And there are influencers saying that AI is not intelligence because it’s compromised and biased by the programmers. So what do we need to do to make aI think more diverse? What knowledge and skills does it take to exclude the bias of AI in our decision-making? 

Ieva Martinkenaite

I can start a matter, you can continue, I have a very simple answer to that. Build diverse teams of data scientists and engineers. That’s the first requirement. Because you know, if you have only white male, from California building products that everyone used in the world, I mean, to check for biases, you can create so many compliance systems around and risk management systems. But at the end of the day, those are, you know, Formula One drivers in one place of the world, building those products is not the way forward. So diversity number one, and I’m doing it also here until to are building teams with diverse backgrounds, diverse gender, or different gender, different experiences, it’s not enough to have a computer science, you know, people need to build products, not at all, actually, is really bad. So you need to have biotechnology experts, mathematicians, physics, people, people from org science, building that diversity is the way forward, it’s not going to be easy. It’s not going to be fast. It needs time. I’m always an advocate of, you know, positive change and longtime implementation, rather than impact rather than we can put so many rules for that, but then we would stop it.

Omair Ahmed Khan

Beautiful, I think we have to add to that, I think she said it very beautifully. The one thing I would like to add again, using my analogy is that you know, when you learn to drive a car, you can choose to drive a car in the right lane or the wrong lane, and you can hit somebody and kill somebody. But you can’t blame the car for it. Yeah, at the end of the day. So it’s very important that we educate our people on the ethical use of data and information for the right reasons. It’s very, very significant. It’s a choice. You know, when you drive by Congress, Atlanta, it’s your choice to go on the right side of the road or the wrong side of the road, you can decide to break the law, you can decide to, I don’t know, hit a deer on the way or whatever. Yeah. But you selected not to, because you realize that what is right, and what is wrong. Similar thing is data, it’s like you have a new thing. And with AI and such, like, you have the capability of doing good or bad with it. And we need to educate and companies need to have more and more of these thinking saying that, what is the right and ethical use for the best interests of the customers in the best interests of the company. And the fourth important thing is the best interests of the customers. Because right now, with information, you can really abuse it. And that is why the culture of human centered or customer centered are focusing on the ethical use of these things is very, very important. And I agree diversity is one great way of doing this because you have different mindsets, and the value system is different. It helps you to get that in there. Yeah. So that is why I think, you know, it’s a power. It’s a privilege that you get, and you should know how to use it wisely. And anything in life, you can use it for the right, or you can use it for the wrong, but you make a decision. And similarly, people in companies need to understand it starts from the top saying that, okay, what is our identity, we’re going to be somebody who’s ethical, we cannot use it for the right reasons, not for the wrong reasons. And when you drive that values all the way from the top not looking at short term goals, but more long term goals, then people understand that let’s use this in the right way, and not in the wrong way. I would say it’s a mindset, it’s an exercise, it’s how leaders show it to people.

Svitlana Bielushkina

It’s also intelligence because you really have to understand that its intelligence, its ethical behavior from the very top is by design, it should be diverse. So you’re designing your teams to make sure that your AI savers,

Omair Ahmed Khan

yes. Diversity bias and also using for good new technology. It’s very scary right now. So you know, again, like any other culture in the world, you have access to so much information, but you have to choose what kind of company you want to be, I think turned on a DD have decided it’s human centric and people over profit, and we really look at making sure that what we do is in the best interest of our customers first. Yeah. And that’s what drives it’s a company culture. I will not take the names of certain companies, will use that differently. Yeah, for them, it’s about how do we manipulate data to get the most likes and the most ads, but that’s not what we’re looking for. That’s also what you look at when you work for a company. What kind of models are there behind it? Yeah. Because at the end, you have to be accountable.

Svitlana Bielushkina

Exactly. Exactly. Great answer. Great answer. And the last question from me for now, because we are rounding off timewise in your amazing careers, and very diverse, coming from chemistry. And you know, I see and from Oregon strategy ever in your case, if you look back, what was the best or worst career advice somebody gave you on your journey?

Omair Ahmed Khan

Okay, so maybe I can start I have one, the best advice I ever got was, if you want to grow in your career, you should find ways to make yourself redundant. The first thing you do when you get a job, is finding somebody else who can do your job. If you’re not able to build the next line of leadership, you will never grow, you will stay where you are. Make yourself redundant. Yes, make yourself earn that it should be your goal. Because whenever you start something new, you need to go add value, get somebody else to do the job, so that you can do something that might be scary. It is scary for many people. But that has been my mantra, every time I’ve taken any job. I’ve always said who is next. And my goal is that if I go on a month vacation, the company should not miss me. Yeah. But then I’m confident enough that something else will come up. But I always feel that the only way to go in life is to find somebody else to do your job. So that was the best advice. I got the worst advice I got. I will also give you that one. If you want to grow in your career, look up. Don’t look down. This was by far the worst advice. I never took it. Because I realized that the only way you grow in life is if your team grows, then you grow. Yeah, it’s not possible that the team is failing and you are successful. That doesn’t work. It’s a very simple philosophy; make your team successful. Their growth is definitely linked to your growth as the team grows as they do better, and they achieve more. I always tell my team, saying that you guys make me look good. You guys make me look good.

Svitlana Bielushkina

So the best goes through teams. Thank you.

Ieva Martinkenaite

So yeah, one of the best advices I was given was that, you know, if you want to grow on the career, make sure that you wake up at nights, and you’re scared. Because if you don’t wake up nights, and you’re not worried, you’re not having dreams too big. I mean, your dreams are too shallow. And then you were never able to realize those, so you’re kind of dreaming big. Were these my advice? So I sleep well. But I mean, I sometimes get up at night, and I’m not getting up at night because I’m scared. And I’m thinking maybe I need to make a shift. So that was my best career advice. And the worst career advice I was given actually was quite similar to America. But maybe within the different CLI kind of way is solving politics first and then get the job done. And that was the worst for me to get the job done first, because I truly believe if you do the work well, with your team, he’s off.

Svitlana Bielushkina

Great advice. You know, I have to say that my feeling in the very beginning when I told you that I’m like in kindergarten expecting a big subject giving me presents, actually came true. It was a really amazing conversation. Thank you so much. I’m sure our listeners really enjoyed it. And it actually was my first episode having two telecommunication companies, talking honestly and openly about data culture, leadership, and how to train successful teams. Thank you.

Ieva Martinkenaite, Omair Ahmed Khan

Thank you. It was a pleasure!

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