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

1 - > elena: So you need to see the process at the factory, how all 2 - > machines are connected where the break potentially could be 3 - > happening. 4 - > And unfortunately, many data s scientists don't do it so they 5 - > sit in the office, they get the data, and they start building 6 - > the model. 7 - > And I would honestly say only maybe 10% of the people are able 8 - > to clearly formulate the customer problem, right? 9 - > Because they can say, oh, I did customer segmentation.

10 - > I'm sorry, this is not the problem. 11 - > So we still give out ourself an excuse saying that. 12 - > Technology is enabler, right? 13 - > So technology is not an enabler anymore.

14 - > Technology is a new way of doing the business 15 - > ben parker: Data and AI careers are no longer built just around 16 - > projects. 17 - > They're built around products, outcomes, and long-term own 18 - > ownership. 19 - > In today's episode of Data Analytics Chat, I'm joined by 20 - > Ena Ali Ka Keena four times Chief AI and data officer with 21 - > Fortune 500 companies and a board advisor, and we're 22 - > exploring how the shift from data projects to data products 23 - > is reshaped.

24 - > PIN careers for data analytics leaders in the age of AI and why 25 - > product skills are becoming essential. 26 - > Ena, welcome to the show. 27 - > elena: Thank you so much, Ben. 28 - > I'm so happy to be here.

29 - > ben parker: Me too. 30 - > It is gonna be obviously a fascinating question. 31 - > What, I guess before we dive in though, what do you wanna give 32 - > listeners a quick introduction to who you are and the types of 33 - > work you've been leading? 34 - > elena: Absolutely.

35 - > So I'm in data analytics and AI for over 25 years. 36 - > I had an international career, which was quite of lucky. 37 - > I have been. 38 - > Crossing the borders, working for companies, the global 39 - > companies across the globe started as as many of us from 40 - > engineering education.

41 - > But I did get my additional education, which have been more 42 - > like on the business side. 43 - > Definitely have been fortunate enough to work for, as you 44 - > mentioned, for four fortune 500 companies. 45 - > Started in publishing for Dow Jones Youth Corporation. 46 - > Moved into healthcare companies like Johnson, one of the most 47 - > admired companies in the globe.

48 - > So then Nestle Danon. 49 - > And most recently its connectivity. 50 - > ben parker: So you obviously work with some of the Yeah. 51 - > Large, complex organizations there.

52 - > elena: Yes, always have been working with large complex 53 - > metrics organizations where you have multiple businesses, 54 - > multiple franchises. 55 - > We constantly have been growing by. 56 - > Acquiring new companies or diversifying companies, right? 57 - > So you can think about that.

58 - > I'm not just traditional like chief data and ai. 59 - > I did also a lot of nor and acquisitions and a lot of 60 - > diversity of the companies, which adds additional work to 61 - > the data space as well. 62 - > ben parker: Yeah, so you obviously you've got a good 63 - > blend then of business and data skills, which is obviously, I 64 - > think, key today. 65 - > 'cause obviously you've got, you do have leaders where they come 66 - > from all the business side and others that come from all the 67 - > data side.

68 - > So you've got the bl, the nice, the glue in the middle. 69 - > elena: Yes, def definitely in the middle. 70 - > And I think this was one of the, my, like a defining moment of my 71 - > career because when I educated with my engineering degree the 72 - > last year of our, my co college education and engineering we got 73 - > really unusual subject, which was marketing. 74 - > And this was marketing how to sell the technology.

75 - > And I got so much excited. 76 - > The professor was absolutely amazing, professor. 77 - > So that she basically convinced me after I got my engineering 78 - > degree, join joining the PhD program. 79 - > So I got my PhD in marketing and economics, which is really 80 - > unusual combination, right?

81 - > So engineering and marketing economics. 82 - > But I think this type of skill is actually helping me because. 83 - > You need to sell the technology to people, you need to explain 84 - > technology to people, right? 85 - > So you have to be a marketer and communicator.

86 - > So this was one of the most defining moments in my career, I 87 - > would say. 88 - > ben parker: Fascinating. 89 - > Yeah, no, definitely. 90 - > I think storytelling easier, obviously, especially in today, 91 - > like you need to be able to communicate across all levels, 92 - > business, tech, data.

93 - > So then do you, is there been an experience that sort of most 94 - > shaped how you approach leading AI initiatives? 95 - > elena: So I always think about what is starting the first step 96 - > is what is really, we need to do for the business, right? 97 - > You're gonna hear a lot like define the business problem. 98 - > I think it's not good enough defining the business problem.

99 - > You need to understand this, how the business is working, right? 100 - > So where are the biggest pools are happening in the business, 101 - > right? 102 - > So if you and manufacturing company actually that how the 103 - > product gets designed, right? 104 - > Because if there is no product designed.

105 - > There is no company, right? 106 - > So then you think about how the product gets actually 107 - > manufactured, right? 108 - > So how the product is moving to the customer, right? 109 - > So you need to to really experience all this all these 110 - > moments, right?

111 - > And as a leader, what I usually start my journey always I go 112 - > directly to customers. 113 - > I go directly to the factory. 114 - > I talk to people who are doing the business every single day, 115 - > and I'm not asking them is what technology you need, right? 116 - > So I'm observing how the process is happening.

117 - > What is possible to do, right? 118 - > So the conversation is about them. 119 - > It's not about me. 120 - > What technology I can implement, right?

121 - > So I'll give you one example is like in manufacturing, one of 122 - > the biggest use case, it's predictive maintenance, right? 123 - > It's maintenance like a, the factory, it's super expensive, 124 - > right? 125 - > So if you stop the factory line you're gonna lose millions of 126 - > dollars or maybe even hundreds of millions of dollars, right? 127 - > You need to have a kind of prediction when you should stop 128 - > it, right?

129 - > And when you look at this task. 130 - > Purely as a data exercise it's not gonna work, right? 131 - > So you need to see the process at the factory, how all machines 132 - > are connected where the break potentially could be happening. 133 - > And unfortunately, many data s scientists don't do it so they 134 - > sit in the office, they get the data, and they start building 135 - > the model.

136 - > So we do need people to experience the real work before 137 - > they actually build in any technology and models. 138 - > So this always have been my approach. 139 - > Or if I'm in retailer environment like for example, 140 - > working for Johnson Consumer Business. 141 - > I have been traveling all, all countries all around the globe.

142 - > So when I come to Singapore or I come to China the first what I'm 143 - > doing, I'm going to the store and I see. 144 - > How people buy my product. 145 - > Where is my product in the store? 146 - > How they can find it, right?

147 - > What is the next product sent into my product? 148 - > So seeing all of this is helping me to better understand how I 149 - > can help the business. 150 - > ben parker: Yeah. 151 - > And I like that.

152 - > 'cause you are, it's more you're thinking outside the box. 153 - > You're not in your role. 154 - > You're thinking as a, a. 155 - > overall picture, aren't you?

156 - > As opposed to just sitting, seeing through your what you 157 - > currently see, you need to, obviously you've got a lot to 158 - > take on, haven't you? 159 - > elena: You need to take a lot but also you need to ask deeper 160 - > questions, right? 161 - > So what I'm observing is that we stopped early on questions, 162 - > right? 163 - > So even.

164 - > Let's say data scientist is gonna come to the factory, and 165 - > first of all, what I'm observing, they're gonna start 166 - > asking really technical questions and people like, 167 - > confused, like what you are asking me, right? 168 - > But they don't go deeper to understand where the break is 169 - > happening, what expectations from the factory, what 170 - > challenges factory having right. 171 - > So listening to customer should be significantly increased, 172 - > right?

173 - > And even when I interview people for different type of jobs and I 174 - > probably interviewed over a thousand people in my career for 175 - > different type of roles. 176 - > And I would honestly say only maybe 10% of the people are able 177 - > to clearly formulate the customer problem, right? 178 - > Because they can say, oh, I did customer segmentation. 179 - > I'm sorry, this is not the problem.

180 - > So you need to understand why you have been doing the customer 181 - > segmentation. 182 - > What exactly changed for the business by you doing the 183 - > customer segmentation. 184 - > ben parker: Yeah, and I think nowadays data roles are going 185 - > that way as well. 186 - > And we've got the tools can do the heavy lifting, especially 187 - > like the data scientists now.

188 - > It's, if you can understand the business problems, that's where 189 - > you are getting the value. 190 - > 'cause you can add so much more knowledge and expertise in that 191 - > domain. 192 - > And again, like obviously, like we used to hire data scientists 193 - > say a couple years ago, and that's more so techie focused. 194 - > But now like obviously the game's completely changed and 195 - > now businesses are looking for that strategic thinking creative 196 - > thinking business domain knowledge.

197 - > elena: Absolutely a hundred percent. 198 - > So it's so this is the the danger, which I'm warning in my 199 - > people through my newsletter like record, because I do. 200 - > Believe that analytics people have to record the career they 201 - > have right now. 202 - > Is that you should not be sitting still.

203 - > Because the, your technical skills, I'm not gonna, I'm not 204 - > gonna serve you. 205 - > They already not serving you, right? 206 - > So you do need to get additional, maybe additional 207 - > education, right? 208 - > So you need to get additional understanding of the business.

209 - > You need to invest in yourself to understand the business, 210 - > right? 211 - > Because it's interesting that AI is challenging the people who 212 - > are front line of ai, right? 213 - > So data analytics, AI people are forced. 214 - > Challenge right now in in the career because they do need to 215 - > have different skills.

216 - > ben parker: Yeah. 217 - > So then obviously there's the shift from data projects to data 218 - > products Now. 219 - > So in your view, what does that shift really mean for data 220 - > analytics teams on the ground? 221 - > elena: Yes, it's really interesting.

222 - > So if you are gonna go now to social media and you're gonna 223 - > start reading a lot of posts about what is data product, 224 - > you're gonna see so many conversations, right? 225 - > What is interesting is 90% of these conversations are gonna be 226 - > arguing about technicality of the term, right? 227 - > So is this architectural decision is this a certain 228 - > container or having the like an agreement with your consumer, 229 - > whatever it is, right? 230 - > So 90% of conversation is gonna be about the technical side, how 231 - > architecturally you do it, right?

232 - > And I think this is what our community is doing completely 233 - > wrong because the product is coming from the customer. 234 - > Understand the first step is spend time with the customer, 235 - > understand how the data is changing the business. 236 - > And designing around the customer, right? 237 - > So what I see is that like a massive implementation of the 238 - > data product architecture started and it's actually driven 239 - > by major hyperscalers like Databricks, Microsoft Fabric 240 - > snowflake, right?

241 - > They all come with concept that you need to have a refrigerated 242 - > architecture, right? 243 - > So technically this is absolutely correct. 244 - > But what nobody's saying that federated architecture 90% of 245 - > success is not technical. 246 - > It's actually about how we work, right?

247 - > And how we work. 248 - > We have to work around the customer need, right? 249 - > So this could be internal customer, right? 250 - > If we serving sales organization, marketing 251 - > organization we serving for example, manufacturing, right?

252 - > Or this could be around external customer. 253 - > If we build in the product, which we actually sell to to the 254 - > market, to external, right? 255 - > This is really unfortunate side when I'm seeing the data product 256 - > I actually mean the product mindset, right? 257 - > So many enterprises included myself when I was working in 258 - > Johnson, actually one of the few, and I would say most 259 - > innovative companies started thinking about this product 260 - > mindset for all technical teams, right?

261 - > We invested massively in training people. 262 - > In a product mindset. 263 - > So we did not train them in a technical architecture, right? 264 - > We trained actually how you identify the customer need, how 265 - > you segment your customers how you prioritize what you, how you 266 - > decide what features worth building, what features you, you 267 - > should not be building, right?

268 - > So this is the thing I'm seeing right? 269 - > So why I believe that the product mindset is absolutely 270 - > must have for ai, right? 271 - > So this is how I explain to to business executives, right? 272 - > So what AI is doing for the business.

273 - > Without ai we always have been growing like like a linear 274 - > business, right? 275 - > So you design your product, manufacture product, shipped to 276 - > customer, serve your customer, right? 277 - > So this is the line. 278 - > Every single time we want to get more product shipped, we want to 279 - > get more customers.

280 - > We add more resources, right? 281 - > So we create, we like buy another factory. 282 - > We create another call center for customers, right? 283 - > So what AI is doing, AI is helping to create.

284 - > I call it like acceleration loop in each cycle, right? 285 - > So basically instead of eating additional resources, building 286 - > the product, AI can actually help me to build the product 287 - > faster, right? 288 - > So then AI can help me, like with predictive man maintenance, 289 - > quality control to do better manufacturing. 290 - > So I can actually do more, by creating this kind of, AI loop, 291 - > right?

292 - > But what people are missing is that AI loop has a, like a four, 293 - > four dimensions, four definitions, and you really have 294 - > to close the loop, right? 295 - > What is the most important closing this loop is actually 296 - > including the people knowledge closing the loop, right? 297 - > Because what is happening in many AI applications right now 298 - > is that people get an answer. 299 - > They just store this answer on, in Excel file.

300 - > This is the reality, right? 301 - > So like what you do when you get an answer from GPT, oh, I just 302 - > copied to Excel, right? 303 - > So that the knowledge is not going back to the system, right? 304 - > The product skill is exactly this loop.

305 - > How you build the knowledge, how you build the knowledge around 306 - > the customer, how you build the knowledge around around the 307 - > process, right? 308 - > We massively need this type of skill with all technical data, 309 - > AI type of organizations, right? 310 - > So AI cannot be project, it's not the project because, because 311 - > you cannot create the loop if you're in the project, right? 312 - > So you need to constantly learn constantly.

313 - > ben parker: Yeah, and I think I mean with that data, product 314 - > hires and it's, it is I guess it's more challenging for 315 - > businesses now'cause you have got to, obviously, depending on 316 - > your team, if you've got, if it's a sort of first, if you've 317 - > got, lacked that knowledge in say like we did one placed 318 - > someone a couple years ago in insurance, they need that 319 - > actuary knowledge. 320 - > That's another component to the skillset that you need.

321 - > Don't you need the data science skills, the actuary knowledge, 322 - > and then the techie stuff. 323 - > And again, like I say, if you like for Johnson, you need that 324 - > pharma knowledge'cause that's where you're gonna get the 325 - > value, isn't it? 326 - > So it's gonna be interesting to see how. 327 - > Because obviously you've got people that jump different 328 - > industries, haven't you?

329 - > So whether it's gonna get more further, say five, 10 years down 330 - > the line, whether you're gonna need to be like an expert in 331 - > say, pharma insurance, financial services. 332 - > It's gonna be fascinating to see how that plays out because the 333 - > tech skills. 334 - > Yeah. 335 - > Let's say you've got all these tools now that can help you do 336 - > the coding.

337 - > It's now gonna become, how can you add value to your strategic 338 - > creative knowledge? 339 - > elena: Abs. 340 - > Absolutely. 341 - > So te tech skill is not differentiator anymore, right?

342 - > But what is also interesting, because my experience is with 343 - > traditional enterprises, right? 344 - > So traditional enterprises is like healthcare, banking, 345 - > manufacturing, right? 346 - > So these are traditional enterprises and there is a 347 - > completely different world, which is actually. 348 - > Google Meta, Amazon, right?

349 - > In these type of companies, they adopted the product mindset from 350 - > the day one, right? 351 - > So this is why they're so successful. 352 - > By building the product, building like personalization in 353 - > Amazon or the way how meta is operates and right. 354 - > So I have been hiring people actually.

355 - > From this type of Es in my teams just to help to build the skill. 356 - > But what I also is finding quite of really different, and some, 357 - > sometimes it is upsetting, right? 358 - > Because if you go to internet you're gonna see enormous amount 359 - > of communities. 360 - > There are meta community, there is Amazon community, there is a 361 - > product sense community.

362 - > There is a product we can community, right? 363 - > All of this. 364 - > Places where people are loaning from each other, right? 365 - > What is interesting is if you look to traditional enterprises, 366 - > people who are working, healthcare and other kind of 367 - > industries, right?

368 - > They're not talking to each other. 369 - > They're really silent. 370 - > And they're not taking the step to learn the new skill. 371 - > What I'm seeing with Amazon type of people, they are so 372 - > proactive.

373 - > They go ahead and take a new skill. 374 - > They take a product class, right? 375 - > And in enterprise, we almost have to force people to take 376 - > this step, right? 377 - > And and unfortunately.

378 - > They actually gonna sacrifice career if they're not gonna do 379 - > it, right? 380 - > So what I have been doing lately, like in my this record 381 - > post I was trying to wake up people saying, wake up. 382 - > You have to speak up. 383 - > You have to go to, to talk to your customer.

384 - > You have to present yourself. 385 - > You cannot be silent, go outside of your comfort zone, right? 386 - > So this is what like must have, and I'm trying to promote as 387 - > much as possible. 388 - > ben parker: Yeah, no, definitely.

389 - > 'Cause even like now obviously data scientists is an example, 390 - > all like machine learning engineers, like they can be 391 - > heavily technical and Yeah, you, they might be able to like, 392 - > yeah, create these like amazing technical features, but if it 393 - > don't like align with the business products is. 394 - > Not gonna be as value driven as like the techie would expect. 395 - > So I think you've gotta, this is where, yeah, like you said, 396 - > you've gotta adopt this product mindset now just to like how 397 - > actually can I add value to the business as opposed to making 398 - > this product maybe technical flat, technically flesh.

399 - > But it's gonna be interesting to see how, obviously leaders adapt 400 - > this way, new way of working. 401 - > elena: And it is needed at all levels. 402 - > So because I have been talking to chief Information officers 403 - > who actually. 404 - > Argue with me in saying there is no difference between product 405 - > and project.

406 - > I was like, there is a big difference actually being a 407 - > product organization and project organization, right? 408 - > So there are education needed at the highest level of technology 409 - > leadership. 410 - > But also if we go to the colleges, right? 411 - > I mean my son is in a college right now, so he's a second year 412 - > computer science and he has a minor in business as well.

413 - > And I do have a lot of friends whose kids like the same age and 414 - > the going through the colleges, right? 415 - > So what is happening right now is the last five years colleges 416 - > graduated over a million. 417 - > Students in computer science and analytics only analytics alone 418 - > it's almost like 500,000 people across the US right? 419 - > And what these people are coming to the market except with a lot 420 - > of money, they still have to pay back to the college, right?

421 - > They come in with some technical skills. 422 - > And even these technical skills are quite of outdated because 423 - > most of the colleges are still teach in R Power bi. 424 - > They're not even explaining how you can use the AI tools, right? 425 - > But but they also, colleges do not teach the data strategy.

426 - > Colleges do not teach the any product management skills. 427 - > There are no classes about the soft skills. 428 - > And it's interesting, so when I'm interviewing students 429 - > coming, like for internships, right? 430 - > You can see amazing cv with a lot of projects and you are 431 - > asking how you did this project, right?

432 - > So that usually the answer is. 433 - > Or I just got the data set, or I signed up for platform, 434 - > something like VIN Analytics and which is a great platform by the 435 - > way, right? 436 - > But it's actually, I stayed silent in my room and I have 437 - > been doing, the stuff my own right. 438 - > So what I usually send to students, it's including my son.

439 - > It's go outside of your dorm, go, knock the door of the small 440 - > business, go to nonprofit organization and say What I can 441 - > do for you, let me understand what exactly I can do on the 442 - > ground. 443 - > So you cannot put in your CV enormous amount of technically 444 - > correct. 445 - > But not, nonsense customer type of projects, right? 446 - > So this a new skill and majority of tech people, data people, 447 - > they feel really uncomfortable going this outside of, this kind 448 - > of, zone, right?

449 - > Teaching my own son and his response is, mom, this actually 450 - > works. 451 - > I was like, yes, I told you. 452 - > Go outside of your car zone. 453 - > Talk to people.

454 - > And he was like, you know what? 455 - > I'm amazed people are responding. 456 - > I'm, I was like, of course they need help, right? 457 - > So be more proactive and and this is the way how you can get 458 - > your first job.

459 - > ben parker: Yeah, no, I definitely think it's also if 460 - > you go to the business side, you understand their problems. 461 - > You can then obviously you've got the technical background, 462 - > you can then add so much more value. 463 - > And that's, it's that collaboration bit. 464 - > It's obviously data teams and business teams are getting 465 - > closer and closer.

466 - > Not quite close enough yet, but I think that's the way the 467 - > future's gonna be. 468 - > And that's where the businesses that do that is gonna have that 469 - > success. 470 - > So then we, I guess with the organizations that I guess are 471 - > moving towards like this more product thinking. 472 - > do you feel leaders struggle to adapt their ways of working?

473 - > elena: So we still give out ourself an excuse saying that. 474 - > Technology is enabler, right? 475 - > So technology is not an enabler anymore. 476 - > Technology is a new way of doing the business, right?

477 - > So technology is creating this business loops, right? 478 - > So that business can actually run really differently, right? 479 - > So this is definitely the lack of literacy, what I'm saying, 480 - > right? 481 - > So we do need to have a massive literacy on both and business 482 - > and the technical side.

483 - > Because technical teams, when they. 484 - > Let's say build data products using Databricks. 485 - > Most of the teams are given excuse to business saying oh, 486 - > it's okay. 487 - > We can play the data product role.

488 - > You don't need to come, which is gonna technically build it up. 489 - > Never works. 490 - > We tried it multiple times celebrate it. 491 - > Multiple successes.

492 - > Year later everything goes back to, to the same kind of 493 - > behavioral, right? 494 - > So behavioral means that the literacy doesn't stick to 495 - > organization, right? 496 - > So we do need to change step by step, right? 497 - > So for for leaders who really need to drive this change, 498 - > right?

499 - > So from the technology side and business side, my advice is go 500 - > back to education. 501 - > Go back to education. 502 - > But don't take, if you are technical leader you don't need 503 - > to take another AI class. 504 - > It's good enough if you watch some YouTube, me videos, educate 505 - > you on the technology.

506 - > But what classes you need to get, you need to get into 507 - > communication. 508 - > You need to get into collaboration, you need to 509 - > actually negotiation, right? 510 - > So how negotiate benefits with the business, right? 511 - > So we do need the massive reeducation of technology and 512 - > data people.

513 - > At the same time, we do need to embed more let's say like a data 514 - > and AI education into business curriculum like all major 515 - > business schools. 516 - > Are not even teaching data strategy, which is shocking, 517 - > right? 518 - > So there's no classes on data strategy. 519 - > So how you can be the leader of the future if you don't even 520 - > know what role data is playing in organization.

521 - > ben parker: Yeah. 522 - > And also because also, businesses are struggling to 523 - > hire like data scientists, data engineers. 524 - > 'cause it's that again, the mindset is having that business 525 - > knowledge is like the role has changed and it's, there's not 526 - > many people that have adapted their ways. 527 - > So do you think businesses.

528 - > Should start investing more in this I guess like executive 529 - > training at an earlier age. 530 - > Because I've had guests one guest on here before and she had 531 - > got executive coaching at her like an early age or not? 532 - > No, sorry. 533 - > Like in early in her career.

534 - > And then that's ramped her up and now she's Chief data another 535 - > company. 536 - > So I think is it more being, just trying to get into that 537 - > mindset of being more proactive, learning these skills that. 538 - > Are gonna be helpful five, 10 years ahead. 539 - > elena: A absolutely right.

540 - > And I would love actually to provide like an analogy, right? 541 - > Because there are a lot of leadership development 542 - > frameworks, right? 543 - > You might know some of them. 544 - > There are like a strength finder.

545 - > There are working ingenious. 546 - > So there is a framework called disc, right? 547 - > All of this framework is actually look at the people 548 - > behaviorals, right? 549 - > And looking for example, who is actually excelling what, right?

550 - > So some, somebody, people are really great in making 551 - > connections. 552 - > Some people are really great in making deep dive research and 553 - > kind of understanding all facts and everything, right? 554 - > But all of these methodologies, old methodologies, they, they do 555 - > not talk about. 556 - > What is changing when technology is around?

557 - > I recently have been presenting at a manufacturing leadership 558 - > council which is like a really large organization in North 559 - > America covering almost all manufacturing organizations, 560 - > right? 561 - > And my topic was about what is the leader of the future, right? 562 - > So I did ask my audience, which majority of executive officers, 563 - > chief and transformation officers, I said, what is. 564 - > The one dysfunction of the team, right?

565 - > And everybody know from the old old leadership development the 566 - > number one dysfunction is trust. 567 - > And, but in all methodology, the trust is between people, right? 568 - > Between your manager, between leadership and the employees of 569 - > the company, right? 570 - > So this is the old version, but now data and technology is a 571 - > part of this, right?

572 - > So it's do we trust data? 573 - > Do we trust technology to make decisions for us? 574 - > And when we had this conversation in this big room of 575 - > senior executives. 576 - > The reaction was like, oh my God, this is absolutely right.

577 - > We cannot do the old way our leadership development because 578 - > we do need to include technology as a part of leadership 579 - > development and show how trust is changing, how partnership 580 - > between teams are changing how roles are changing in 581 - > organizations. 582 - > So when I speak about data ai literacy programs. 583 - > This is not about training tools. 584 - > Most programs you're gonna see, not most all of them, you're 585 - > going to see actually about how we train people to understand 586 - > data.

587 - > No, it's not about understanding data. 588 - > It's about this, how we can make people actually trust in data, 589 - > how they can identify when you should be trusting on not 590 - > trusting data, right? 591 - > And how you should be working together so you can actually 592 - > trust the data, right? 593 - > So really different center.

594 - > ben parker: So what then would you say like the for the 595 - > listeners, what skills that are becoming essential? 596 - > That people should maybe focus more. 597 - > 'cause obviously it's changed now. 598 - > Obviously there's so much, there's so much to learn, isn't 599 - > there?

600 - > With generative ai, it's all constantly evolved. 601 - > But is there any other key skills that people should pay 602 - > attention to for this? 603 - > Like I guess more of a product driven environment. 604 - > elena: This all comes into soft skills, right?

605 - > So this is your cultural agility. 606 - > This is your curiosity. 607 - > This is the skill to partner. 608 - > Because mainly tech people have a tendency to focus on doing 609 - > technical work versus partnering with somebody else who might be 610 - > doing this technical work, right?

611 - > Collaboration, up and down, listening to customer 612 - > negotiation. 613 - > So these are the skills which absolutely needed for the 614 - > future, right? 615 - > So I'm currently recommending again. 616 - > I'm speaking up for people on traditional enterprises, right?

617 - > We have to wake up, we have to invest in ourselves into our 618 - > leadership skills which is, I said collaboration, 619 - > communication, listening to customer partnership, being 620 - > curious, right? 621 - > So being a agile. 622 - > So these are type of skills are much needed for the future. 623 - > ben parker: And I guess this is gonna be.

624 - > A massive step for a lot of people in the tech world. 625 - > 'cause obviously traditionally they're really technically 626 - > gifted. 627 - > And this is gonna be coming outta their shell, isn't it 628 - > really to get these soft skills? 629 - > 'cause this is a completely new thing for them because 630 - > traditionally like tech people just love to sit in a room and 631 - > code like it is really now you need to, you've gotta be that.

632 - > Yeah. 633 - > Like focused on your soft skills and I guess understanding 634 - > problems now. 635 - > elena: Yeah, no, ab absolutely right. 636 - > I think we are just in the beginning of.

637 - > Like a major changes in a career pass, right? 638 - > So it's not gonna be like a linear career pass anymore, 639 - > right? 640 - > So it's gonna be more matrix career pass. 641 - > It's not gonna be differentiation or technical 642 - > people.

643 - > And this are business people, right? 644 - > So it's gonna be the new cohort of people. 645 - > Which actually understand both sides, the business and the 646 - > technical sides, and can actually run the business more 647 - > technically. 648 - > We already have great examples.

649 - > I'm just going back to me, at the Amazon, right? 650 - > So these are the new generation of leaders who grew up. 651 - > Running the technology type of business, right? 652 - > But now this technology type of business are coming to 653 - > healthcare, come into banking, came into like manufacturing and 654 - > all other insurances, all other industries, right?

655 - > We are gonna see the massive, massive change. 656 - > So this technical people, we said, right? 657 - > But business people also have a gap, right? 658 - > So this is an example.

659 - > A guy now. 660 - > It's going so crazy. 661 - > I'm talking to development teams and they have so many requests 662 - > from business leaders to develop all kind of agents, right? 663 - > But when we talk to business people and I say, okay, how are 664 - > you gonna evaluate your digital employee?

665 - > So it's interesting response because it's like, what do you 666 - > mean digital employee? 667 - > I was like, this agent is actually gonna make your sales 668 - > now. 669 - > You are accountable, right? 670 - > And you can see the reaction and the business person business 671 - > person reaction because they were like, what do you mean this 672 - > is like a CIO job?

673 - > I was like no. 674 - > This is your job because this is your digital employee, right? 675 - > So we also need different type of skills on a, on the business 676 - > side to understand how you manage technology as your new 677 - > employees. 678 - > ben parker: So would you say, obviously the rise of ai, is it 679 - > accelerating or complicating the move towards more product 680 - > centric roles?

681 - > elena: It is accelerate. 682 - > It's accelerate a hundred percent. 683 - > Ben as I'm saying, is people wake up. 684 - > It's like you, you have to invest in yourself and get this 685 - > type of skills right?

686 - > Start from, reading internet, watch YouTube videos, buy some 687 - > books. 688 - > I. 689 - > You can see probably behind me, I have quite a lot of books 690 - > actually about the product management because I personally 691 - > have been investing in my own skills the last like 12 years, I 692 - > would say, right? 693 - > You have to invest in your education.

694 - > It's gonna be like, must have for the future. 695 - > This is the way for you to to actually, stand up around the 696 - > crowd and get your leadership spot. 697 - > ben parker: Yeah, so I guess for like leaders who I guess grew up 698 - > in a more traditional analytics or engineering path, what's the 699 - > best way for them to start building like true product 700 - > capability? 701 - > elena: So the first step, what I would recommend is.

702 - > People is asking I want to, be in like a product. 703 - > It's like you, you have some management skill. 704 - > Skill, right? 705 - > So people are always saying yes, I want to be a manager, okay?

706 - > Before you want to be manager, be the leader first, right? 707 - > So what does it mean? 708 - > Be the leader first. 709 - > Nobody's pre preventing you to be the leader, right?

710 - > Do the first exercise. 711 - > This is my recommendation. 712 - > Go to your customer. 713 - > And not just to make a, the call of saying what dashboard I can 714 - > build for you or what, whatever model I can build for you.

715 - > But go into discovery process. 716 - > For example, ask your customer to take you to the sales call, 717 - > right? 718 - > Or to the sales trip, or go to the factory. 719 - > So start understanding better the customer and the business, 720 - > right?

721 - > So this is doable. 722 - > You can do it, right? 723 - > So if customer's gonna say no, I'm not interested to take you, 724 - > you can still do it, right? 725 - > So go to the store, see how the business is working right?

726 - > Find a way to learn more about the need of your business. 727 - > ben parker: Yeah, no, definitely. 728 - > I think it's. 729 - > elena: First step.

730 - > Yes. 731 - > ben parker: Yeah, no, I think if if you know the business, the 732 - > if, like even if you're a top leader, if you know the 733 - > business, if you can actually like say in a co say if you 734 - > worked in a co leader in a coffee shop, like you went and 735 - > worked actually on the coffee floor, understand the business 736 - > problems, you're gonna be able to apply that knowledge So much 737 - > more value, aren't you? 738 - > elena: Absolutely no ab, absolutely.

739 - > So definitely be more curious, right? 740 - > But do not. 741 - > So what I see is technical people, they, they all including 742 - > myself, right? 743 - > So we all excited about solutions.

744 - > We are solutions people, right? 745 - > So we see the first answer and we immediately like, oh, I know 746 - > how to solve it. 747 - > No. 748 - > Stop here.

749 - > So you have to ask at least a hundred questions. 750 - > Hold yourself on any solution, hold yourself, forget about 751 - > solution, right? 752 - > Hand have more deeper conversation, right? 753 - > So so you can actually understand what is the real 754 - > problem, because the first sentence you might hear.

755 - > It's not gonna be the real problem. 756 - > And maybe the fifth sentence you're gonna hear, it's not 757 - > gonna be real problem. 758 - > We have to have deeper conversations and maybe more, 759 - > informal conversations, right? 760 - > So I'll give you in my examples when I was, going on a sales 761 - > trip, like in in the pharma for example, right?

762 - > We visit some doctors, right? 763 - > And I can absorb how the sales conversation is happening, 764 - > right? 765 - > So then we, we live in the office and salesperson is 766 - > actually supposed to enter information about the visa to 767 - > the system, right? 768 - > And I'm seeing how this person is struggling to do this because 769 - > there is no internet connection, right?

770 - > So if I don't, don't see it, right? 771 - > So I might not, I don't understand why we have missing 772 - > fields. 773 - > We have missing fields because people simply didn't have 774 - > internet connection in the location. 775 - > So this gives me the thinking is okay, if they don't internet 776 - > connection, what I do about it, you know how I can help them, 777 - > right?

778 - > So you. 779 - > ben parker: So obviously you've mentioned a couple of companies 780 - > like Meta and about obviously getting it right. 781 - > So when companies are getting product thinking right in data 782 - > ai, what kind of business value shows up first? 783 - > elena: So I think first what the business, first of all, you can 784 - > definitely see much better agility and and efficiency, 785 - > right?

786 - > So you are gonna be able to de, to deliver the value much 787 - > quicker, right? 788 - > Because the product mindset means that you develop, you deve 789 - > you you develop the value in incremental steps. 790 - > So it's like you almost because it's a like Iranian cycle, 791 - > right? 792 - > So this is not the project, oh wait, several months and this is 793 - > your answer, right?

794 - > You are constantly gonna be able to work with your customer and 795 - > create incremental value, right? 796 - > So this is the first benefit, right? 797 - > So the value is gonna start showing right away. 798 - > Yours is, then you wait four months to finish your project 799 - > and then your customer gonna look at the project and say, oh 800 - > my God, this is not what I wanted.

801 - > It's not gonna work in my environment. 802 - > So this is the first benefit you're gonna see. 803 - > ben parker: Okay, so do you feel, obviously these, with 804 - > businesses, building teams, do you think. 805 - > Obviously I've called you the glue.

806 - > Should they be hiring more people that can act as a glue 807 - > within a business? 808 - > So then they bring the business and tech closer together. 809 - > elena: Y Yes. 810 - > Abso absolutely.

811 - > We are already seeing right now that like having the business 812 - > partnership is a requirement for all leadership type of roles, 813 - > right? 814 - > So do you want to be the technology director? 815 - > Do you want to be the chief data officer? 816 - > The first skill is.

817 - > That you actually have to be able to build the business case. 818 - > You have to be the partner, you have to understand the business, 819 - > right? 820 - > So these are the skills which are always, gonna be the skills 821 - > for the senior leaders. 822 - > Also I tell like a middle.

823 - > Me, middle level man managers. 824 - > This is a big cohort of people who come to me for career 825 - > advice. 826 - > I'm saying for you to grow up you're not gonna grow by IT and 827 - > more tools, right? 828 - > So you're gonna grow by it and leadership skills, better 829 - > understanding of the business, right?

830 - > So this is absolute requirement for you to grow. 831 - > ben parker: Yeah, no, definitely. 832 - > And I think it's gonna be, I think as AI matures, it's gonna 833 - > get deeper and deeper, isn't it? 834 - > Into every area of the business.

835 - > It's, and that's just the way it's gonna get. 836 - > So if you've got that, the knack. 837 - > like you said to keep asking questions why? 838 - > Getting deeper into the problem, you are gonna be in a better 839 - > position to add value to the company.

840 - > elena: Yeah, no, a abs absolutely, you go, you're gonna 841 - > be like in a better position. 842 - > And plus again, understanding how the business is working, 843 - > right? 844 - > So I think I'm, maybe I, I feel I'm lucky in my career, right? 845 - > Because one of the major role when, when I actually, came to 846 - > United States back in like in 2000.

847 - > So I was hired by the Wall Street Journal, Dow Jones to 848 - > establish basically data and analytics practice, right? 849 - > Which didn't exist for digital. 850 - > And I had the chance to work in a digital publishing. 851 - > Before even digital transformation started, right?

852 - > So this gave me opportunity to better understand how to work in 853 - > this product model, right? 854 - > So we did not call it the product model, right? 855 - > But this almost an Amazon type of requirement, right? 856 - > I remember one of the program I was driving is personalization, 857 - > right?

858 - > So like everybody. 859 - > Have this theory that if you personalize customer experience, 860 - > you are going get better better customer retention, you're going 861 - > to get better subscription, right? 862 - > So you're going to get longer time value, right? 863 - > So this is like a hypothesis, right?

864 - > There are two ways to look into this, right? 865 - > So one way is like how we technically prove it, right? 866 - > Can we actually prove that personalization is driving more 867 - > usage, right? 868 - > But then the business side comes is this gonna convert to p and 869 - > l?

870 - > And here you can hear a completely different story 871 - > because I remember when I was, presenting to senior executives 872 - > at at Dow Jones, and basically I said, you know what, here is my 873 - > finding. 874 - > So this type of realization is gonna convert to p and l and 875 - > this one is not gonna convert. 876 - > And you are gonna see the room of people like really senior 877 - > chief product officer. 878 - > She was like, oh my God, Juliana, you changed my mind.

879 - > I did not even think this way. 880 - > That this presentation is not gonna convert to p and l, which 881 - > means that we don't want to invest, right? 882 - > So technically you can think, oh yeah, let's do everything gonna 883 - > be personalized, right? 884 - > But if it's not gonna convert to your pro profit, basically, or 885 - > your additional customer growth, then it does make sense to do.

886 - > And I would tell you. 887 - > It's rarely. 888 - > You can see the people especially like a mid-level, who 889 - > are crunching all these numbers are thinking this way. 890 - > So they need to start more about how the business is made.

891 - > What is, what is our profit, what is our loss, what is 892 - > operational efficiency? 893 - > To better connect all the science projects to real 894 - > business. 895 - > ben parker: Yeah, it's interesting to say that'cause 896 - > I've had a lot of people on the podcast honestly speak to people 897 - > where they've, it's. 898 - > Got to point in their career and they've done like a sideways 899 - > move to more into the business.

900 - > And then further on in their career, it's really benefited 901 - > them. 902 - > So I think it's, there's a lot of different ways you can go in 903 - > the future, especially for leaders. 904 - > You could see. 905 - > Aside, obviously I know everyone probably wants progression, but 906 - > sometimes if you do a sideways move or even a step down just to 907 - > understand the business, it's gonna benefit you in say, five 908 - > years time.

909 - > You're gonna just add being a bit of, I guess all more for all 910 - > rounded type of individual, you understand business and 911 - > obviously you have the tech side to your skillset. 912 - > elena: A hundred percent. 913 - > And actually some companies are doing this type of rotation. 914 - > So for example, Johnson, actually we did quite of 915 - > rotations, right?

916 - > So like somebody from my team, like a business analyst. 917 - > We sent her to the marketing organization for six months, 918 - > right? 919 - > And and she didn't like it. 920 - > She was like, I don't want to be in marketing.

921 - > No, go and learn what marketing is doing, right? 922 - > Because, we working a lot with brands. 923 - > You need to understand the brand structure, right? 924 - > So this, these are like you, you basically need to give this type 925 - > of skills to rat, rotate people.

926 - > And the same. 927 - > Take somebody from the business team and put them into 928 - > technology organization. 929 - > So for me is actually just maybe, how I came to to my role 930 - > as a chief data ai. 931 - > I started from the business, right?

932 - > So when I was working in a publishing, I was in a product 933 - > organization working directly with the business. 934 - > And my, my fewest technical role, it's actually happened to 935 - > be. 936 - > When I joined Johnson, right? 937 - > And this is where I started more being on the technical side, 938 - > right?

939 - > So I'm coming from the business to technical side, and this is 940 - > actually helping me because I am helping the business people, the 941 - > technical people to give more business focus, right? 942 - > And we need both ways. 943 - > We need people actually both ways to take this type of steps 944 - > because there are a lot of really. 945 - > Super smart and the people who are really understand the future 946 - > of technology on the business side.

947 - > But they feel that going back work for technology, it's almost 948 - > like a step back. 949 - > No, it's not the step back. 950 - > You can be one of the best technical leaders, right? 951 - > So if you come from the business to the technology.

952 - > ben parker: Yeah. 953 - > No, definitely. 954 - > Brilliant. 955 - > Okay, brilliant.

956 - > I really appreciate your insights Anna, I guess any I 957 - > love your passion, by the way. 958 - > Any, any final words you'd like to leave with the listeners? 959 - > elena: So I think my final word is is definitely I think I 960 - > already said enough, is, wake up people. 961 - > So unfortunately that AI is is gonna be, yeah, it's gonna put 962 - > pressure on us.

963 - > It's putting pressure on people who build ai. 964 - > Gonna feel it first, right? 965 - > Wake up invest in yourself. 966 - > Nobody's gonna help you unless you help yourself, right?

967 - > So there are so many resources available. 968 - > As I mentioned, there are wonderful communities. 969 - > There is a YouTube, there is a substack, right? 970 - > But take care of yourself.

971 - > So this is gonna be my last message because I love you so 972 - > much. 973 - > I want everybody in data analytics side to be successful, 974 - > but please wake up. 975 - > ben parker: Brilliant. 976 - > Thank you for your conversation.

977 - > elena: Thank you so much, Ben. 978 - > ben parker: And I guess for thank you for listeners for 979 - > listening. 980 - > If you want more conversations with data experts, please make 981 - > sure you follow or subscribe to Data Analytics Chat. 982 - > And so don't miss future episodes and we'll see you next 983 - > time.