From Data Projects to Data Products: Essential Skills for AI Leaders

In this episode of Data Analytics Chat, we welcome Elena Alikhachkina, a four-time Chief AI and Data Officer with Fortune 500 companies, and a board advisor. Elena shares her journey in data analytics and AI spanning over 25 years, covering key transitions in her career and the importance of blending business acumen with technical expertise. 

The discussion focuses on the critical shift from data projects to data products, highlighting the need for product skills in the age of AI. Elena emphasises the importance of understanding the business, building customer-centric solutions, and investing in both technical and soft skills for AI leaders. Her insights provide a roadmap for anyone looking to excel in data and AI roles in today's dynamic business environment.

00:00 Introduction to the Factory Process and Data Science Challenges
01:00 Welcome to Data Analytics Chat with Elena Alikhachkina
01:33 Ena's Career Journey and Insights
04:14 The Importance of Understanding Business Processes
09:17 Shifting from Data Projects to Data Products
11:08 The Need for a Product Mindset in AI
15:06 Challenges in Adopting a Product Mindset
16:39 Encouraging Proactivity and Collaboration
18:08 The Role of Education in Driving Change
22:34 The Need for Reeducation in Technology and Data
22:50 Embedding Data and AI in Business Curriculum
23:14 The Changing Role of Data in Leadership
24:01 The Importance of Soft Skills in Tech
25:31 Trust in Data and Technology
26:30 Essential Skills for the Future
28:20 The Shift to Product-Centric Roles
29:50 Bridging the Gap Between Business and Tech
30:16 Investing in Personal Development
31:19 Understanding the Customer's Needs
34:00 The Value of Product Thinking in AI
35:47 Career Growth Through Business Understanding
41:03 Final Thoughts and Call to Action

Thank you for listening!

Transcript

elena: 0:00

So you need to see the process at the factory, how all machines are connected where the break potentially could be happening. And unfortunately, many data s scientists don't do it so they sit in the office, they get the data, and they start building the model. And I would honestly say only maybe 10% of the people are able to clearly formulate the customer problem, right? Because they can say, oh, I did customer segmentation. I'm sorry, this is not the problem. So we still give out ourself an excuse saying that. Technology is enabler, right? So technology is not an enabler anymore. Technology is a new way of doing the business

ben parker: 0:51

Data and AI careers are no longer built just around projects. They're built around products, outcomes, and long-term own ownership. In today's episode of Data Analytics Chat, I'm joined by Ena Ali Ka Keena four times Chief AI and data officer with Fortune 500 companies and a board advisor, and we're exploring how the shift from data projects to data products is reshaped. PIN careers for data analytics leaders in the age of AI and why product skills are becoming essential. Ena, welcome to the show.

elena: 1:28

Thank you so much, Ben. I'm so happy to be here.

ben parker: 1:30

Me too. It is gonna be obviously a fascinating question. What, I guess before we dive in though, what do you wanna give listeners a quick introduction to who you are and the types of work you've been leading?

elena: 1:41

Absolutely. So I'm in data analytics and AI for over 25 years. I had an international career, which was quite of lucky. I have been. Crossing the borders, working for companies, the global companies across the globe started as as many of us from engineering education. But I did get my additional education, which have been more like on the business side. Definitely have been fortunate enough to work for, as you mentioned, for four fortune 500 companies. Started in publishing for Dow Jones Youth Corporation. Moved into healthcare companies like Johnson, one of the most admired companies in the globe. So then Nestle Danon. And most recently its connectivity.

ben parker: 2:24

So you obviously work with some of the Yeah. Large, complex organizations there.

elena: 2:29

Yes, always have been working with large complex metrics organizations where you have multiple businesses, multiple franchises. We constantly have been growing by. Acquiring new companies or diversifying companies, right? So you can think about that. I'm not just traditional like chief data and ai. I did also a lot of nor and acquisitions and a lot of diversity of the companies, which adds additional work to the data space as well.

ben parker: 2:55

Yeah, so you obviously you've got a good blend then of business and data skills, which is obviously, I think, key today.'cause obviously you've got, you do have leaders where they come from all the business side and others that come from all the data side. So you've got the bl, the nice, the glue in the middle.

elena: 3:11

Yes, def definitely in the middle. And I think this was one of the, my, like a defining moment of my career because when I educated with my engineering degree the last year of our, my co college education and engineering we got really unusual subject, which was marketing. And this was marketing how to sell the technology. And I got so much excited. The professor was absolutely amazing, professor. So that she basically convinced me after I got my engineering degree, join joining the PhD program. So I got my PhD in marketing and economics, which is really unusual combination, right? So engineering and marketing economics. But I think this type of skill is actually helping me because. You need to sell the technology to people, you need to explain technology to people, right? So you have to be a marketer and communicator. So this was one of the most defining moments in my career, I would say.

ben parker: 4:05

Fascinating. Yeah, no, definitely. I think storytelling easier, obviously, especially in today, like you need to be able to communicate across all levels, business, tech, data. So then do you, is there been an experience that sort of most shaped how you approach leading AI initiatives?

elena: 4:20

So I always think about what is starting the first step is what is really, we need to do for the business, right? You're gonna hear a lot like define the business problem. I think it's not good enough defining the business problem. You need to understand this, how the business is working, right? So where are the biggest pools are happening in the business, right? So if you and manufacturing company actually that how the product gets designed, right? Because if there is no product designed. There is no company, right? So then you think about how the product gets actually manufactured, right? So how the product is moving to the customer, right? So you need to to really experience all this all these moments, right? And as a leader, what I usually start my journey always I go directly to customers. I go directly to the factory. I talk to people who are doing the business every single day, and I'm not asking them is what technology you need, right? So I'm observing how the process is happening. What is possible to do, right? So the conversation is about them. It's not about me. What technology I can implement, right? So I'll give you one example is like in manufacturing, one of the biggest use case, it's predictive maintenance, right? It's maintenance like a, the factory, it's super expensive, right? So if you stop the factory line you're gonna lose millions of dollars or maybe even hundreds of millions of dollars, right? You need to have a kind of prediction when you should stop it, right? And when you look at this task. Purely as a data exercise it's not gonna work, right? So you need to see the process at the factory, how all machines are connected where the break potentially could be happening. And unfortunately, many data s scientists don't do it so they sit in the office, they get the data, and they start building the model. So we do need people to experience the real work before they actually build in any technology and models. So this always have been my approach. Or if I'm in retailer environment like for example, working for Johnson Consumer Business. I have been traveling all, all countries all around the globe. So when I come to Singapore or I come to China the first what I'm doing, I'm going to the store and I see. How people buy my product. Where is my product in the store? How they can find it, right? What is the next product sent into my product? So seeing all of this is helping me to better understand how I can help the business.

ben parker: 6:42

Yeah. And I like that.'cause you are, it's more you're thinking outside the box. You're not in your role. You're thinking as a, a. overall picture, aren't you? As opposed to just sitting, seeing through your what you currently see, you need to, obviously you've got a lot to take on, haven't you?

elena: 6:57

You need to take a lot but also you need to ask deeper questions, right? So what I'm observing is that we stopped early on questions, right? So even. Let's say data scientist is gonna come to the factory, and first of all, what I'm observing, they're gonna start asking really technical questions and people like, confused, like what you are asking me, right? But they don't go deeper to understand where the break is happening, what expectations from the factory, what challenges factory having right. So listening to customer should be significantly increased, right? And even when I interview people for different type of jobs and I probably interviewed over a thousand people in my career for different type of roles. And I would honestly say only maybe 10% of the people are able to clearly formulate the customer problem, right? Because they can say, oh, I did customer segmentation. I'm sorry, this is not the problem. So you need to understand why you have been doing the customer segmentation. What exactly changed for the business by you doing the customer segmentation.

ben parker: 7:59

Yeah, and I think nowadays data roles are going that way as well. And we've got the tools can do the heavy lifting, especially like the data scientists now. It's, if you can understand the business problems, that's where you are getting the value.'cause you can add so much more knowledge and expertise in that domain. And again, like obviously, like we used to hire data scientists say a couple years ago, and that's more so techie focused. But now like obviously the game's completely changed and now businesses are looking for that strategic thinking creative thinking business domain knowledge.

elena: 8:29

Absolutely a hundred percent. So it's so this is the the danger, which I'm warning in my people through my newsletter like record, because I do. Believe that analytics people have to record the career they have right now. Is that you should not be sitting still. Because the, your technical skills, I'm not gonna, I'm not gonna serve you. They already not serving you, right? So you do need to get additional, maybe additional education, right? So you need to get additional understanding of the business. You need to invest in yourself to understand the business, right? Because it's interesting that AI is challenging the people who are front line of ai, right? So data analytics, AI people are forced. Challenge right now in in the career because they do need to have different skills.

ben parker: 9:16

Yeah. So then obviously there's the shift from data projects to data products Now. So in your view, what does that shift really mean for data analytics teams on the ground?

elena: 9:29

Yes, it's really interesting. So if you are gonna go now to social media and you're gonna start reading a lot of posts about what is data product, you're gonna see so many conversations, right? What is interesting is 90% of these conversations are gonna be arguing about technicality of the term, right? So is this architectural decision is this a certain container or having the like an agreement with your consumer, whatever it is, right? So 90% of conversation is gonna be about the technical side, how architecturally you do it, right? And I think this is what our community is doing completely wrong because the product is coming from the customer. Understand the first step is spend time with the customer, understand how the data is changing the business. And designing around the customer, right? So what I see is that like a massive implementation of the data product architecture started and it's actually driven by major hyperscalers like Databricks, Microsoft Fabric snowflake, right? They all come with concept that you need to have a refrigerated architecture, right? So technically this is absolutely correct. But what nobody's saying that federated architecture 90% of success is not technical. It's actually about how we work, right? And how we work. We have to work around the customer need, right? So this could be internal customer, right? If we serving sales organization, marketing organization we serving for example, manufacturing, right? Or this could be around external customer. If we build in the product, which we actually sell to to the market, to external, right? This is really unfortunate side when I'm seeing the data product I actually mean the product mindset, right? So many enterprises included myself when I was working in Johnson, actually one of the few, and I would say most innovative companies started thinking about this product mindset for all technical teams, right? We invested massively in training people. In a product mindset. So we did not train them in a technical architecture, right? We trained actually how you identify the customer need, how you segment your customers how you prioritize what you, how you decide what features worth building, what features you, you should not be building, right? So this is the thing I'm seeing right? So why I believe that the product mindset is absolutely must have for ai, right? So this is how I explain to to business executives, right? So what AI is doing for the business. Without ai we always have been growing like like a linear business, right? So you design your product, manufacture product, shipped to customer, serve your customer, right? So this is the line. Every single time we want to get more product shipped, we want to get more customers. We add more resources, right? So we create, we like buy another factory. We create another call center for customers, right? So what AI is doing, AI is helping to create. I call it like acceleration loop in each cycle, right? So basically instead of eating additional resources, building the product, AI can actually help me to build the product faster, right? So then AI can help me, like with predictive man maintenance, quality control to do better manufacturing. So I can actually do more, by creating this kind of, AI loop, right? But what people are missing is that AI loop has a, like a four, four dimensions, four definitions, and you really have to close the loop, right? What is the most important closing this loop is actually including the people knowledge closing the loop, right? Because what is happening in many AI applications right now is that people get an answer. They just store this answer on, in Excel file. This is the reality, right? So like what you do when you get an answer from GPT, oh, I just copied to Excel, right? So that the knowledge is not going back to the system, right? The product skill is exactly this loop. How you build the knowledge, how you build the knowledge around the customer, how you build the knowledge around around the process, right? We massively need this type of skill with all technical data, AI type of organizations, right? So AI cannot be project, it's not the project because, because you cannot create the loop if you're in the project, right? So you need to constantly learn constantly.

ben parker: 14:00

Yeah, and I think I mean with that data, product hires and it's, it is I guess it's more challenging for businesses now'cause you have got to, obviously, depending on your team, if you've got, if it's a sort of first, if you've got, lacked that knowledge in say like we did one placed someone a couple years ago in insurance, they need that actuary knowledge. That's another component to the skillset that you need. Don't you need the data science skills, the actuary knowledge, and then the techie stuff. And again, like I say, if you like for Johnson, you need that pharma knowledge'cause that's where you're gonna get the value, isn't it? So it's gonna be interesting to see how. Because obviously you've got people that jump different industries, haven't you? So whether it's gonna get more further, say five, 10 years down the line, whether you're gonna need to be like an expert in say, pharma insurance, financial services. It's gonna be fascinating to see how that plays out because the tech skills. Yeah. Let's say you've got all these tools now that can help you do the coding. It's now gonna become, how can you add value to your strategic creative knowledge?

elena: 15:01

Abs. Absolutely. So te tech skill is not differentiator anymore, right? But what is also interesting, because my experience is with traditional enterprises, right? So traditional enterprises is like healthcare, banking, manufacturing, right? So these are traditional enterprises and there is a completely different world, which is actually. Google Meta, Amazon, right? In these type of companies, they adopted the product mindset from the day one, right? So this is why they're so successful. By building the product, building like personalization in Amazon or the way how meta is operates and right. So I have been hiring people actually. From this type of Es in my teams just to help to build the skill. But what I also is finding quite of really different, and some, sometimes it is upsetting, right? Because if you go to internet you're gonna see enormous amount of communities. There are meta community, there is Amazon community, there is a product sense community. There is a product we can community, right? All of this. Places where people are loaning from each other, right? What is interesting is if you look to traditional enterprises, people who are working, healthcare and other kind of industries, right? They're not talking to each other. They're really silent. And they're not taking the step to learn the new skill. What I'm seeing with Amazon type of people, they are so proactive. They go ahead and take a new skill. They take a product class, right? And in enterprise, we almost have to force people to take this step, right? And and unfortunately. They actually gonna sacrifice career if they're not gonna do it, right? So what I have been doing lately, like in my this record post I was trying to wake up people saying, wake up. You have to speak up. You have to go to, to talk to your customer. You have to present yourself. You cannot be silent, go outside of your comfort zone, right? So this is what like must have, and I'm trying to promote as much as possible.

ben parker: 17:00

Yeah, no, definitely.'Cause even like now obviously data scientists is an example, all like machine learning engineers, like they can be heavily technical and Yeah, you, they might be able to like, yeah, create these like amazing technical features, but if it don't like align with the business products is. Not gonna be as value driven as like the techie would expect. So I think you've gotta, this is where, yeah, like you said, you've gotta adopt this product mindset now just to like how actually can I add value to the business as opposed to making this product maybe technical flat, technically flesh. But it's gonna be interesting to see how, obviously leaders adapt this way, new way of working.

elena: 17:44

And it is needed at all levels. So because I have been talking to chief Information officers who actually. Argue with me in saying there is no difference between product and project. I was like, there is a big difference actually being a product organization and project organization, right? So there are education needed at the highest level of technology leadership. But also if we go to the colleges, right? I mean my son is in a college right now, so he's a second year computer science and he has a minor in business as well. And I do have a lot of friends whose kids like the same age and the going through the colleges, right? So what is happening right now is the last five years colleges graduated over a million. Students in computer science and analytics only analytics alone it's almost like 500,000 people across the US right? And what these people are coming to the market except with a lot of money, they still have to pay back to the college, right? They come in with some technical skills. And even these technical skills are quite of outdated because most of the colleges are still teach in R Power bi. They're not even explaining how you can use the AI tools, right? But but they also, colleges do not teach the data strategy. Colleges do not teach the any product management skills. There are no classes about the soft skills. And it's interesting, so when I'm interviewing students coming, like for internships, right? You can see amazing cv with a lot of projects and you are asking how you did this project, right? So that usually the answer is. Or I just got the data set, or I signed up for platform, something like VIN Analytics and which is a great platform by the way, right? But it's actually, I stayed silent in my room and I have been doing, the stuff my own right. So what I usually send to students, it's including my son. It's go outside of your dorm, go, knock the door of the small business, go to nonprofit organization and say What I can do for you, let me understand what exactly I can do on the ground. So you cannot put in your CV enormous amount of technically correct. But not, nonsense customer type of projects, right? So this a new skill and majority of tech people, data people, they feel really uncomfortable going this outside of, this kind of, zone, right? Teaching my own son and his response is, mom, this actually works. I was like, yes, I told you. Go outside of your car zone. Talk to people. And he was like, you know what? I'm amazed people are responding. I'm, I was like, of course they need help, right? So be more proactive and and this is the way how you can get your first job.

ben parker: 20:34

Yeah, no, I definitely think it's also if you go to the business side, you understand their problems. You can then obviously you've got the technical background, you can then add so much more value. And that's, it's that collaboration bit. It's obviously data teams and business teams are getting closer and closer. Not quite close enough yet, but I think that's the way the future's gonna be. And that's where the businesses that do that is gonna have that success. So then we, I guess with the organizations that I guess are moving towards like this more product thinking. do you feel leaders struggle to adapt their ways of working?

elena: 21:13

So we still give out ourself an excuse saying that. Technology is enabler, right? So technology is not an enabler anymore. Technology is a new way of doing the business, right? So technology is creating this business loops, right? So that business can actually run really differently, right? So this is definitely the lack of literacy, what I'm saying, right? So we do need to have a massive literacy on both and business and the technical side. Because technical teams, when they. Let's say build data products using Databricks. Most of the teams are given excuse to business saying oh, it's okay. We can play the data product role. You don't need to come, which is gonna technically build it up. Never works. We tried it multiple times celebrate it. Multiple successes. Year later everything goes back to, to the same kind of behavioral, right? So behavioral means that the literacy doesn't stick to organization, right? So we do need to change step by step, right? So for for leaders who really need to drive this change, right? So from the technology side and business side, my advice is go back to education. Go back to education. But don't take, if you are technical leader you don't need to take another AI class. It's good enough if you watch some YouTube, me videos, educate you on the technology. But what classes you need to get, you need to get into communication. You need to get into collaboration, you need to actually negotiation, right? So how negotiate benefits with the business, right? So we do need the massive reeducation of technology and data people. At the same time, we do need to embed more let's say like a data and AI education into business curriculum like all major business schools. Are not even teaching data strategy, which is shocking, right? So there's no classes on data strategy. So how you can be the leader of the future if you don't even know what role data is playing in organization.

ben parker: 23:14

Yeah. And also because also, businesses are struggling to hire like data scientists, data engineers.'cause it's that again, the mindset is having that business knowledge is like the role has changed and it's, there's not many people that have adapted their ways. So do you think businesses. Should start investing more in this I guess like executive training at an earlier age. Because I've had guests one guest on here before and she had got executive coaching at her like an early age or not? No, sorry. Like in early in her career. And then that's ramped her up and now she's Chief data another company. So I think is it more being, just trying to get into that mindset of being more proactive, learning these skills that. Are gonna be helpful five, 10 years ahead.

elena: 24:01

A absolutely right. And I would love actually to provide like an analogy, right? Because there are a lot of leadership development frameworks, right? You might know some of them. There are like a strength finder. There are working ingenious. So there is a framework called disc, right? All of this framework is actually look at the people behaviorals, right? And looking for example, who is actually excelling what, right? So some, somebody, people are really great in making connections. Some people are really great in making deep dive research and kind of understanding all facts and everything, right? But all of these methodologies, old methodologies, they, they do not talk about. What is changing when technology is around? I recently have been presenting at a manufacturing leadership council which is like a really large organization in North America covering almost all manufacturing organizations, right? And my topic was about what is the leader of the future, right? So I did ask my audience, which majority of executive officers, chief and transformation officers, I said, what is. The one dysfunction of the team, right? And everybody know from the old old leadership development the number one dysfunction is trust. And, but in all methodology, the trust is between people, right? Between your manager, between leadership and the employees of the company, right? So this is the old version, but now data and technology is a part of this, right? So it's do we trust data? Do we trust technology to make decisions for us? And when we had this conversation in this big room of senior executives. The reaction was like, oh my God, this is absolutely right. We cannot do the old way our leadership development because we do need to include technology as a part of leadership development and show how trust is changing, how partnership between teams are changing how roles are changing in organizations. So when I speak about data ai literacy programs. This is not about training tools. Most programs you're gonna see, not most all of them, you're going to see actually about how we train people to understand data. No, it's not about understanding data. It's about this, how we can make people actually trust in data, how they can identify when you should be trusting on not trusting data, right? And how you should be working together so you can actually trust the data, right? So really different center.

ben parker: 26:30

So what then would you say like the for the listeners, what skills that are becoming essential? That people should maybe focus more.'cause obviously it's changed now. Obviously there's so much, there's so much to learn, isn't there? With generative ai, it's all constantly evolved. But is there any other key skills that people should pay attention to for this? Like I guess more of a product driven environment.

elena: 26:53

This all comes into soft skills, right? So this is your cultural agility. This is your curiosity. This is the skill to partner. Because mainly tech people have a tendency to focus on doing technical work versus partnering with somebody else who might be doing this technical work, right? Collaboration, up and down, listening to customer negotiation. So these are the skills which absolutely needed for the future, right? So I'm currently recommending again. I'm speaking up for people on traditional enterprises, right? We have to wake up, we have to invest in ourselves into our leadership skills which is, I said collaboration, communication, listening to customer partnership, being curious, right? So being a agile. So these are type of skills are much needed for the future.

ben parker: 27:46

And I guess this is gonna be. A massive step for a lot of people in the tech world.'cause obviously traditionally they're really technically gifted. And this is gonna be coming outta their shell, isn't it really to get these soft skills?'cause this is a completely new thing for them because traditionally like tech people just love to sit in a room and code like it is really now you need to, you've gotta be that. Yeah. Like focused on your soft skills and I guess understanding problems now.

elena: 28:15

Yeah, no, ab absolutely right. I think we are just in the beginning of. Like a major changes in a career pass, right? So it's not gonna be like a linear career pass anymore, right? So it's gonna be more matrix career pass. It's not gonna be differentiation or technical people. And this are business people, right? So it's gonna be the new cohort of people. Which actually understand both sides, the business and the technical sides, and can actually run the business more technically. We already have great examples. I'm just going back to me, at the Amazon, right? So these are the new generation of leaders who grew up. Running the technology type of business, right? But now this technology type of business are coming to healthcare, come into banking, came into like manufacturing and all other insurances, all other industries, right? We are gonna see the massive, massive change. So this technical people, we said, right? But business people also have a gap, right? So this is an example. A guy now. It's going so crazy. I'm talking to development teams and they have so many requests from business leaders to develop all kind of agents, right? But when we talk to business people and I say, okay, how are you gonna evaluate your digital employee? So it's interesting response because it's like, what do you mean digital employee? I was like, this agent is actually gonna make your sales now. You are accountable, right? And you can see the reaction and the business person business person reaction because they were like, what do you mean this is like a CIO job? I was like no. This is your job because this is your digital employee, right? So we also need different type of skills on a, on the business side to understand how you manage technology as your new employees.

ben parker: 29:59

So would you say, obviously the rise of ai, is it accelerating or complicating the move towards more product centric roles?

elena: 30:07

It is accelerate. It's accelerate a hundred percent. Ben as I'm saying, is people wake up. It's like you, you have to invest in yourself and get this type of skills right? Start from, reading internet, watch YouTube videos, buy some books. I. You can see probably behind me, I have quite a lot of books actually about the product management because I personally have been investing in my own skills the last like 12 years, I would say, right? You have to invest in your education. It's gonna be like, must have for the future. This is the way for you to to actually, stand up around the crowd and get your leadership spot.

ben parker: 30:44

Yeah, so I guess for like leaders who I guess grew up in a more traditional analytics or engineering path, what's the best way for them to start building like true product capability?

elena: 30:55

So the first step, what I would recommend is. People is asking I want to, be in like a product. It's like you, you have some management skill. Skill, right? So people are always saying yes, I want to be a manager, okay? Before you want to be manager, be the leader first, right? So what does it mean? Be the leader first. Nobody's pre preventing you to be the leader, right? Do the first exercise. This is my recommendation. Go to your customer. And not just to make a, the call of saying what dashboard I can build for you or what, whatever model I can build for you. But go into discovery process. For example, ask your customer to take you to the sales call, right? Or to the sales trip, or go to the factory. So start understanding better the customer and the business, right? So this is doable. You can do it, right? So if customer's gonna say no, I'm not interested to take you, you can still do it, right? So go to the store, see how the business is working right? Find a way to learn more about the need of your business.

ben parker: 31:56

Yeah, no, definitely. I think it's.

elena: 31:58

First step. Yes.

ben parker: 31:59

Yeah, no, I think if if you know the business, the if, like even if you're a top leader, if you know the business, if you can actually like say in a co say if you worked in a co leader in a coffee shop, like you went and worked actually on the coffee floor, understand the business problems, you're gonna be able to apply that knowledge So much more value, aren't you?

elena: 32:18

Absolutely no ab, absolutely. So definitely be more curious, right? But do not. So what I see is technical people, they, they all including myself, right? So we all excited about solutions. We are solutions people, right? So we see the first answer and we immediately like, oh, I know how to solve it. No. Stop here. So you have to ask at least a hundred questions. Hold yourself on any solution, hold yourself, forget about solution, right? Hand have more deeper conversation, right? So so you can actually understand what is the real problem, because the first sentence you might hear. It's not gonna be the real problem. And maybe the fifth sentence you're gonna hear, it's not gonna be real problem. We have to have deeper conversations and maybe more, informal conversations, right? So I'll give you in my examples when I was, going on a sales trip, like in in the pharma for example, right? We visit some doctors, right? And I can absorb how the sales conversation is happening, right? So then we, we live in the office and salesperson is actually supposed to enter information about the visa to the system, right? And I'm seeing how this person is struggling to do this because there is no internet connection, right? So if I don't, don't see it, right? So I might not, I don't understand why we have missing fields. We have missing fields because people simply didn't have internet connection in the location. So this gives me the thinking is okay, if they don't internet connection, what I do about it, you know how I can help them, right? So you.

ben parker: 33:55

So obviously you've mentioned a couple of companies like Meta and about obviously getting it right. So when companies are getting product thinking right in data ai, what kind of business value shows up first?

elena: 34:07

So I think first what the business, first of all, you can definitely see much better agility and and efficiency, right? So you are gonna be able to de, to deliver the value much quicker, right? Because the product mindset means that you develop, you deve you you develop the value in incremental steps. So it's like you almost because it's a like Iranian cycle, right? So this is not the project, oh wait, several months and this is your answer, right? You are constantly gonna be able to work with your customer and create incremental value, right? So this is the first benefit, right? So the value is gonna start showing right away. Yours is, then you wait four months to finish your project and then your customer gonna look at the project and say, oh my God, this is not what I wanted. It's not gonna work in my environment. So this is the first benefit you're gonna see.

ben parker: 34:56

Okay, so do you feel, obviously these, with businesses, building teams, do you think. Obviously I've called you the glue. Should they be hiring more people that can act as a glue within a business? So then they bring the business and tech closer together.

elena: 35:12

Y Yes. Abso absolutely. We are already seeing right now that like having the business partnership is a requirement for all leadership type of roles, right? So do you want to be the technology director? Do you want to be the chief data officer? The first skill is. That you actually have to be able to build the business case. You have to be the partner, you have to understand the business, right? So these are the skills which are always, gonna be the skills for the senior leaders. Also I tell like a middle. Me, middle level man managers. This is a big cohort of people who come to me for career advice. I'm saying for you to grow up you're not gonna grow by IT and more tools, right? So you're gonna grow by it and leadership skills, better understanding of the business, right? So this is absolute requirement for you to grow.

ben parker: 35:59

Yeah, no, definitely. And I think it's gonna be, I think as AI matures, it's gonna get deeper and deeper, isn't it? Into every area of the business. It's, and that's just the way it's gonna get. So if you've got that, the knack. like you said to keep asking questions why? Getting deeper into the problem, you are gonna be in a better position to add value to the company.

elena: 36:20

Yeah, no, a abs absolutely, you go, you're gonna be like in a better position. And plus again, understanding how the business is working, right? So I think I'm, maybe I, I feel I'm lucky in my career, right? Because one of the major role when, when I actually, came to United States back in like in 2000. So I was hired by the Wall Street Journal, Dow Jones to establish basically data and analytics practice, right? Which didn't exist for digital. And I had the chance to work in a digital publishing. Before even digital transformation started, right? So this gave me opportunity to better understand how to work in this product model, right? So we did not call it the product model, right? But this almost an Amazon type of requirement, right? I remember one of the program I was driving is personalization, right? So like everybody. Have this theory that if you personalize customer experience, you are going get better better customer retention, you're going to get better subscription, right? So you're going to get longer time value, right? So this is like a hypothesis, right? There are two ways to look into this, right? So one way is like how we technically prove it, right? Can we actually prove that personalization is driving more usage, right? But then the business side comes is this gonna convert to p and l? And here you can hear a completely different story because I remember when I was, presenting to senior executives at at Dow Jones, and basically I said, you know what, here is my finding. So this type of realization is gonna convert to p and l and this one is not gonna convert. And you are gonna see the room of people like really senior chief product officer. She was like, oh my God, Juliana, you changed my mind. I did not even think this way. That this presentation is not gonna convert to p and l, which means that we don't want to invest, right? So technically you can think, oh yeah, let's do everything gonna be personalized, right? But if it's not gonna convert to your pro profit, basically, or your additional customer growth, then it does make sense to do. And I would tell you. It's rarely. You can see the people especially like a mid-level, who are crunching all these numbers are thinking this way. So they need to start more about how the business is made. What is, what is our profit, what is our loss, what is operational efficiency? To better connect all the science projects to real business.

ben parker: 38:48

Yeah, it's interesting to say that'cause I've had a lot of people on the podcast honestly speak to people where they've, it's. Got to point in their career and they've done like a sideways move to more into the business. And then further on in their career, it's really benefited them. So I think it's, there's a lot of different ways you can go in the future, especially for leaders. You could see. Aside, obviously I know everyone probably wants progression, but sometimes if you do a sideways move or even a step down just to understand the business, it's gonna benefit you in say, five years time. You're gonna just add being a bit of, I guess all more for all rounded type of individual, you understand business and obviously you have the tech side to your skillset.

elena: 39:29

A hundred percent. And actually some companies are doing this type of rotation. So for example, Johnson, actually we did quite of rotations, right? So like somebody from my team, like a business analyst. We sent her to the marketing organization for six months, right? And and she didn't like it. She was like, I don't want to be in marketing. No, go and learn what marketing is doing, right? Because, we working a lot with brands. You need to understand the brand structure, right? So this, these are like you, you basically need to give this type of skills to rat, rotate people. And the same. Take somebody from the business team and put them into technology organization. So for me is actually just maybe, how I came to to my role as a chief data ai. I started from the business, right? So when I was working in a publishing, I was in a product organization working directly with the business. And my, my fewest technical role, it's actually happened to be. When I joined Johnson, right? And this is where I started more being on the technical side, right? So I'm coming from the business to technical side, and this is actually helping me because I am helping the business people, the technical people to give more business focus, right? And we need both ways. We need people actually both ways to take this type of steps because there are a lot of really. Super smart and the people who are really understand the future of technology on the business side. But they feel that going back work for technology, it's almost like a step back. No, it's not the step back. You can be one of the best technical leaders, right? So if you come from the business to the technology.

ben parker: 41:01

Yeah. No, definitely. Brilliant. Okay, brilliant. I really appreciate your insights Anna, I guess any I love your passion, by the way. Any, any final words you'd like to leave with the listeners?

elena: 41:11

So I think my final word is is definitely I think I already said enough, is, wake up people. So unfortunately that AI is is gonna be, yeah, it's gonna put pressure on us. It's putting pressure on people who build ai. Gonna feel it first, right? Wake up invest in yourself. Nobody's gonna help you unless you help yourself, right? So there are so many resources available. As I mentioned, there are wonderful communities. There is a YouTube, there is a substack, right? But take care of yourself. So this is gonna be my last message because I love you so much. I want everybody in data analytics side to be successful, but please wake up.

ben parker: 41:49

Brilliant. Thank you for your conversation.

elena: 41:51

Thank you so much, Ben.

ben parker: 41:53

And I guess for thank you for listeners for listening. If you want more conversations with data experts, please make sure you follow or subscribe to Data Analytics Chat. And so don't miss future episodes and we'll see you next time.