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So you need to see the process at the factory, how all machines are connected where the break potentially could be happening.
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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.
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And I would honestly say only maybe 10% of the people are able to clearly formulate the customer problem, right?
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Because they can say, oh, I did customer segmentation.
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I'm sorry, this is not the problem.
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So we still give out ourself an excuse saying that.
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Technology is enabler, right?
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So technology is not an enabler anymore.
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Technology is a new way of doing the business
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Data and AI careers are no longer built just around projects.
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They're built around products, outcomes, and long-term own ownership.
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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.
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PIN careers for data analytics leaders in the age of AI and why product skills are becoming essential.
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Ena, welcome to the show.
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Thank you so much, Ben.
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I'm so happy to be here.
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Me too.
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It is gonna be obviously a fascinating question.
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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?
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Absolutely.
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So I'm in data analytics and AI for over 25 years.
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I had an international career, which was quite of lucky.
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I have been.
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Crossing the borders, working for companies, the global companies across the globe started as as many of us from engineering education.
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But I did get my additional education, which have been more like on the business side.
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Definitely have been fortunate enough to work for, as you mentioned, for four fortune 500 companies.
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Started in publishing for Dow Jones Youth Corporation.
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Moved into healthcare companies like Johnson, one of the most admired companies in the globe.
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So then Nestle Danon.
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And most recently its connectivity.
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So you obviously work with some of the Yeah.
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Large, complex organizations there.
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Yes, always have been working with large complex metrics organizations where you have multiple businesses, multiple franchises.
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We constantly have been growing by.
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Acquiring new companies or diversifying companies, right?
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So you can think about that.
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I'm not just traditional like chief data and ai.
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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.
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Yeah, so you obviously you've got a good blend then of business and data skills, which is obviously, I think, key today.
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'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.
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So you've got the bl, the nice, the glue in the middle.
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Yes, def definitely in the middle.
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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.
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And this was marketing how to sell the technology.
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And I got so much excited.
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The professor was absolutely amazing, professor.
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So that she basically convinced me after I got my engineering degree, join joining the PhD program.
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So I got my PhD in marketing and economics, which is really unusual combination, right?
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So engineering and marketing economics.
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But I think this type of skill is actually helping me because.
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You need to sell the technology to people, you need to explain technology to people, right?
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So you have to be a marketer and communicator.
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So this was one of the most defining moments in my career, I would say.
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Fascinating.
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Yeah, no, definitely.
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I think storytelling easier, obviously, especially in today, like you need to be able to communicate across all levels, business, tech, data.
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So then do you, is there been an experience that sort of most shaped how you approach leading AI initiatives?
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So I always think about what is starting the first step is what is really, we need to do for the business, right?
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You're gonna hear a lot like define the business problem.
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I think it's not good enough defining the business problem.
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You need to understand this, how the business is working, right?
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So where are the biggest pools are happening in the business, right?
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So if you and manufacturing company actually that how the product gets designed, right?
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Because if there is no product designed.
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There is no company, right?
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So then you think about how the product gets actually manufactured, right?
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So how the product is moving to the customer, right?
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So you need to to really experience all this all these moments, right?
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And as a leader, what I usually start my journey always I go directly to customers.
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I go directly to the factory.
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I talk to people who are doing the business every single day, and I'm not asking them is what technology you need, right?
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So I'm observing how the process is happening.
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What is possible to do, right?
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So the conversation is about them.
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It's not about me.
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What technology I can implement, right?
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So I'll give you one example is like in manufacturing, one of the biggest use case, it's predictive maintenance, right?
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It's maintenance like a, the factory, it's super expensive, right?
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So if you stop the factory line you're gonna lose millions of dollars or maybe even hundreds of millions of dollars, right?
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You need to have a kind of prediction when you should stop it, right?
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And when you look at this task.
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Purely as a data exercise it's not gonna work, right?
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So you need to see the process at the factory, how all machines are connected where the break potentially could be happening.
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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.
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So we do need people to experience the real work before they actually build in any technology and models.
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So this always have been my approach.
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Or if I'm in retailer environment like for example, working for Johnson Consumer Business.
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I have been traveling all, all countries all around the globe.
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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.
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How people buy my product.
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Where is my product in the store?
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How they can find it, right?
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What is the next product sent into my product?
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So seeing all of this is helping me to better understand how I can help the business.
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Yeah.
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And I like that.
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'cause you are, it's more you're thinking outside the box.
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You're not in your role.
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You're thinking as a, a.
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overall picture, aren't you?
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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?
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You need to take a lot but also you need to ask deeper questions, right?
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So what I'm observing is that we stopped early on questions, right?
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So even.
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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?
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But they don't go deeper to understand where the break is happening, what expectations from the factory, what challenges factory having right.
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So listening to customer should be significantly increased, right?
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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.
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And I would honestly say only maybe 10% of the people are able to clearly formulate the customer problem, right?
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Because they can say, oh, I did customer segmentation.
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I'm sorry, this is not the problem.
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So you need to understand why you have been doing the customer segmentation.
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What exactly changed for the business by you doing the customer segmentation.
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Yeah, and I think nowadays data roles are going that way as well.
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And we've got the tools can do the heavy lifting, especially like the data scientists now.
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It's, if you can understand the business problems, that's where you are getting the value.
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'cause you can add so much more knowledge and expertise in that domain.
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And again, like obviously, like we used to hire data scientists say a couple years ago, and that's more so techie focused.
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But now like obviously the game's completely changed and now businesses are looking for that strategic thinking creative thinking business domain knowledge.
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Absolutely a hundred percent.
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So it's so this is the the danger, which I'm warning in my people through my newsletter like record, because I do.
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Believe that analytics people have to record the career they have right now.
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Is that you should not be sitting still.
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Because the, your technical skills, I'm not gonna, I'm not gonna serve you.
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They already not serving you, right?
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So you do need to get additional, maybe additional education, right?
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So you need to get additional understanding of the business.
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You need to invest in yourself to understand the business, right?
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Because it's interesting that AI is challenging the people who are front line of ai, right?
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So data analytics, AI people are forced.
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Challenge right now in in the career because they do need to have different skills.
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Yeah.
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So then obviously there's the shift from data projects to data products Now.
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So in your view, what does that shift really mean for data analytics teams on the ground?
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Yes, it's really interesting.
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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?
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What is interesting is 90% of these conversations are gonna be arguing about technicality of the term, right?
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So is this architectural decision is this a certain container or having the like an agreement with your consumer, whatever it is, right?
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So 90% of conversation is gonna be about the technical side, how architecturally you do it, right?
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And I think this is what our community is doing completely wrong because the product is coming from the customer.
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Understand the first step is spend time with the customer, understand how the data is changing the business.
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And designing around the customer, right?
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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?
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They all come with concept that you need to have a refrigerated architecture, right?
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So technically this is absolutely correct.
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But what nobody's saying that federated architecture 90% of success is not technical.
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It's actually about how we work, right?
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And how we work.
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We have to work around the customer need, right?
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So this could be internal customer, right?
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If we serving sales organization, marketing organization we serving for example, manufacturing, right?
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Or this could be around external customer.
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If we build in the product, which we actually sell to to the market, to external, right?
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This is really unfortunate side when I'm seeing the data product I actually mean the product mindset, right?
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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?
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We invested massively in training people.
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In a product mindset.
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So we did not train them in a technical architecture, right?
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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?
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So this is the thing I'm seeing right?
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So why I believe that the product mindset is absolutely must have for ai, right?
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So this is how I explain to to business executives, right?
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So what AI is doing for the business.
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Without ai we always have been growing like like a linear business, right?
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So you design your product, manufacture product, shipped to customer, serve your customer, right?
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So this is the line.
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Every single time we want to get more product shipped, we want to get more customers.
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We add more resources, right?
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So we create, we like buy another factory.
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We create another call center for customers, right?
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So what AI is doing, AI is helping to create.
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I call it like acceleration loop in each cycle, right?
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So basically instead of eating additional resources, building the product, AI can actually help me to build the product faster, right?
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So then AI can help me, like with predictive man maintenance, quality control to do better manufacturing.
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So I can actually do more, by creating this kind of, AI loop, right?
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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?
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What is the most important closing this loop is actually including the people knowledge closing the loop, right?
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Because what is happening in many AI applications right now is that people get an answer.
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They just store this answer on, in Excel file.
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This is the reality, right?
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So like what you do when you get an answer from GPT, oh, I just copied to Excel, right?
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So that the knowledge is not going back to the system, right?
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The product skill is exactly this loop.
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How you build the knowledge, how you build the knowledge around the customer, how you build the knowledge around around the process, right?
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We massively need this type of skill with all technical data, AI type of organizations, right?
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So AI cannot be project, it's not the project because, because you cannot create the loop if you're in the project, right?
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So you need to constantly learn constantly.
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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.
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That's another component to the skillset that you need.
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Don't you need the data science skills, the actuary knowledge, and then the techie stuff.
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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?
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So it's gonna be interesting to see how.
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Because obviously you've got people that jump different industries, haven't you?
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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.
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It's gonna be fascinating to see how that plays out because the tech skills.
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Yeah.
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Let's say you've got all these tools now that can help you do the coding.
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It's now gonna become, how can you add value to your strategic creative knowledge?
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Abs.
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Absolutely.
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So te tech skill is not differentiator anymore, right?
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But what is also interesting, because my experience is with traditional enterprises, right?
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So traditional enterprises is like healthcare, banking, manufacturing, right?
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So these are traditional enterprises and there is a completely different world, which is actually.
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Google Meta, Amazon, right?
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In these type of companies, they adopted the product mindset from the day one, right?
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So this is why they're so successful.
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By building the product, building like personalization in Amazon or the way how meta is operates and right.
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So I have been hiring people actually.
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From this type of Es in my teams just to help to build the skill.
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But what I also is finding quite of really different, and some, sometimes it is upsetting, right?
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Because if you go to internet you're gonna see enormous amount of communities.
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There are meta community, there is Amazon community, there is a product sense community.
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There is a product we can community, right?
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All of this.
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Places where people are loaning from each other, right?
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What is interesting is if you look to traditional enterprises, people who are working, healthcare and other kind of industries, right?
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They're not talking to each other.
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They're really silent.
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And they're not taking the step to learn the new skill.
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What I'm seeing with Amazon type of people, they are so proactive.
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They go ahead and take a new skill.
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They take a product class, right?
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And in enterprise, we almost have to force people to take this step, right?
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And and unfortunately.
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They actually gonna sacrifice career if they're not gonna do it, right?
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So what I have been doing lately, like in my this record post I was trying to wake up people saying, wake up.
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You have to speak up.
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You have to go to, to talk to your customer.
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You have to present yourself.
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You cannot be silent, go outside of your comfort zone, right?
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So this is what like must have, and I'm trying to promote as much as possible.
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Yeah, no, definitely.
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'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.
00:17:19.701 --> 00:17:25.101
Not gonna be as value driven as like the techie would expect.
00:17:25.340 --> 00:17:38.846
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.
00:17:39.175 --> 00:17:43.996
But it's gonna be interesting to see how, obviously leaders adapt this way, new way of working.
00:17:44.596 --> 00:17:46.756
And it is needed at all levels.
00:17:46.786 --> 00:17:51.625
So because I have been talking to chief Information officers who actually.
00:17:52.006 --> 00:17:55.546
Argue with me in saying there is no difference between product and project.
00:17:55.996 --> 00:18:01.036
I was like, there is a big difference actually being a product organization and project organization, right?
00:18:01.395 --> 00:18:05.296
So there are education needed at the highest level of technology leadership.
00:18:05.665 --> 00:18:08.395
But also if we go to the colleges, right?
00:18:08.816 --> 00:18:15.036
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.
00:18:15.326 --> 00:18:20.276
And I do have a lot of friends whose kids like the same age and the going through the colleges, right?
00:18:20.635 --> 00:18:27.800
So what is happening right now is the last five years colleges graduated over a million.
00:18:28.401 --> 00:18:36.990
Students in computer science and analytics only analytics alone it's almost like 500,000 people across the US right?
00:18:37.580 --> 00:18:43.990
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?
00:18:44.246 --> 00:18:46.286
They come in with some technical skills.
00:18:46.286 --> 00:18:53.135
And even these technical skills are quite of outdated because most of the colleges are still teach in R Power bi.
00:18:53.135 --> 00:18:56.256
They're not even explaining how you can use the AI tools, right?
00:18:56.685 --> 00:19:00.955
But but they also, colleges do not teach the data strategy.
00:19:01.840 --> 00:19:05.125
Colleges do not teach the any product management skills.
00:19:05.425 --> 00:19:08.185
There are no classes about the soft skills.
00:19:08.675 --> 00:19:13.415
And it's interesting, so when I'm interviewing students coming, like for internships, right?
00:19:13.846 --> 00:19:19.496
You can see amazing cv with a lot of projects and you are asking how you did this project, right?
00:19:19.885 --> 00:19:21.685
So that usually the answer is.
00:19:22.645 --> 00:19:30.246
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?
00:19:30.576 --> 00:19:37.306
But it's actually, I stayed silent in my room and I have been doing, the stuff my own right.
00:19:37.306 --> 00:19:40.635
So what I usually send to students, it's including my son.
00:19:41.451 --> 00:19:54.340
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.
00:19:54.441 --> 00:20:00.161
So you cannot put in your CV enormous amount of technically correct.
00:20:00.490 --> 00:20:03.480
But not, nonsense customer type of projects, right?
00:20:03.730 --> 00:20:12.161
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?
00:20:12.580 --> 00:20:18.171
Teaching my own son and his response is, mom, this actually works.
00:20:18.171 --> 00:20:19.820
I was like, yes, I told you.
00:20:20.451 --> 00:20:22.101
Go outside of your car zone.
00:20:22.191 --> 00:20:23.181
Talk to people.
00:20:23.330 --> 00:20:24.590
And he was like, you know what?
00:20:24.590 --> 00:20:26.270
I'm amazed people are responding.
00:20:26.300 --> 00:20:29.030
I'm, I was like, of course they need help, right?
00:20:29.300 --> 00:20:33.405
So be more proactive and and this is the way how you can get your first job.
00:20:34.006 --> 00:20:41.986
Yeah, no, I definitely think it's also if you go to the business side, you understand their problems.
00:20:42.976 --> 00:20:46.965
You can then obviously you've got the technical background, you can then add so much more value.
00:20:47.326 --> 00:20:49.215
And that's, it's that collaboration bit.
00:20:49.215 --> 00:20:53.175
It's obviously data teams and business teams are getting closer and closer.
00:20:53.596 --> 00:20:57.911
Not quite close enough yet, but I think that's the way the future's gonna be.
00:20:57.911 --> 00:21:01.520
And that's where the businesses that do that is gonna have that success.
00:21:02.361 --> 00:21:08.060
So then we, I guess with the organizations that I guess are moving towards like this more product thinking.
00:21:08.661 --> 00:21:12.915
do you feel leaders struggle to adapt their ways of working?
00:21:13.516 --> 00:21:16.846
So we still give out ourself an excuse saying that.
00:21:17.445 --> 00:21:19.576
Technology is enabler, right?
00:21:19.576 --> 00:21:22.276
So technology is not an enabler anymore.
00:21:22.276 --> 00:21:25.066
Technology is a new way of doing the business, right?
00:21:25.066 --> 00:21:28.915
So technology is creating this business loops, right?
00:21:28.915 --> 00:21:31.826
So that business can actually run really differently, right?
00:21:32.256 --> 00:21:35.695
So this is definitely the lack of literacy, what I'm saying, right?
00:21:35.695 --> 00:21:39.915
So we do need to have a massive literacy on both and business and the technical side.
00:21:40.256 --> 00:21:41.965
Because technical teams, when they.
00:21:42.181 --> 00:21:44.901
Let's say build data products using Databricks.
00:21:45.151 --> 00:21:48.330
Most of the teams are given excuse to business saying oh, it's okay.
00:21:48.330 --> 00:21:51.240
We can play the data product role.
00:21:51.431 --> 00:21:54.401
You don't need to come, which is gonna technically build it up.
00:21:54.641 --> 00:21:55.480
Never works.
00:21:55.580 --> 00:21:58.161
We tried it multiple times celebrate it.
00:21:58.161 --> 00:21:59.451
Multiple successes.
00:21:59.730 --> 00:22:04.721
Year later everything goes back to, to the same kind of behavioral, right?
00:22:04.721 --> 00:22:08.411
So behavioral means that the literacy doesn't stick to organization, right?
00:22:08.861 --> 00:22:11.496
So we do need to change step by step, right?
00:22:11.855 --> 00:22:16.096
So for for leaders who really need to drive this change, right?
00:22:16.096 --> 00:22:22.635
So from the technology side and business side, my advice is go back to education.
00:22:22.726 --> 00:22:23.806
Go back to education.
00:22:23.806 --> 00:22:29.411
But don't take, if you are technical leader you don't need to take another AI class.
00:22:29.461 --> 00:22:34.090
It's good enough if you watch some YouTube, me videos, educate you on the technology.
00:22:34.431 --> 00:22:37.790
But what classes you need to get, you need to get into communication.
00:22:37.790 --> 00:22:42.441
You need to get into collaboration, you need to actually negotiation, right?
00:22:42.441 --> 00:22:45.441
So how negotiate benefits with the business, right?
00:22:45.951 --> 00:22:50.510
So we do need the massive reeducation of technology and data people.
00:22:50.631 --> 00:23:01.006
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.
00:23:01.760 --> 00:23:05.330
Are not even teaching data strategy, which is shocking, right?
00:23:05.330 --> 00:23:07.280
So there's no classes on data strategy.
00:23:07.520 --> 00:23:13.836
So how you can be the leader of the future if you don't even know what role data is playing in organization.
00:23:14.435 --> 00:23:14.615
Yeah.
00:23:14.621 --> 00:23:20.695
And also because also, businesses are struggling to hire like data scientists, data engineers.
00:23:20.726 --> 00:23:29.605
'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.
00:23:30.326 --> 00:23:31.451
So do you think businesses.
00:23:32.050 --> 00:23:37.070
Should start investing more in this I guess like executive training at an earlier age.
00:23:37.070 --> 00:23:45.740
Because I've had guests one guest on here before and she had got executive coaching at her like an early age or not?
00:23:45.740 --> 00:23:46.280
No, sorry.
00:23:46.340 --> 00:23:48.080
Like in early in her career.
00:23:48.421 --> 00:23:51.931
And then that's ramped her up and now she's Chief data another company.
00:23:52.320 --> 00:23:57.691
So I think is it more being, just trying to get into that mindset of being more proactive, learning these skills that.
00:23:58.290 --> 00:24:00.976
Are gonna be helpful five, 10 years ahead.
00:24:01.576 --> 00:24:02.701
A absolutely right.
00:24:02.701 --> 00:24:05.851
And I would love actually to provide like an analogy, right?
00:24:06.181 --> 00:24:10.590
Because there are a lot of leadership development frameworks, right?
00:24:10.641 --> 00:24:12.050
You might know some of them.
00:24:12.050 --> 00:24:13.641
There are like a strength finder.
00:24:14.191 --> 00:24:16.221
There are working ingenious.
00:24:16.270 --> 00:24:18.760
So there is a framework called disc, right?
00:24:19.121 --> 00:24:23.290
All of this framework is actually look at the people behaviorals, right?
00:24:23.290 --> 00:24:27.201
And looking for example, who is actually excelling what, right?
00:24:27.201 --> 00:24:30.320
So some, somebody, people are really great in making connections.
00:24:30.320 --> 00:24:37.101
Some people are really great in making deep dive research and kind of understanding all facts and everything, right?
00:24:37.530 --> 00:24:41.971
But all of these methodologies, old methodologies, they, they do not talk about.
00:24:42.661 --> 00:24:44.550
What is changing when technology is around?
00:24:44.921 --> 00:24:55.800
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?
00:24:56.201 --> 00:24:59.141
And my topic was about what is the leader of the future, right?
00:24:59.560 --> 00:25:06.820
So I did ask my audience, which majority of executive officers, chief and transformation officers, I said, what is.
00:25:07.330 --> 00:25:09.851
The one dysfunction of the team, right?
00:25:10.090 --> 00:25:15.826
And everybody know from the old old leadership development the number one dysfunction is trust.
00:25:16.506 --> 00:25:20.895
And, but in all methodology, the trust is between people, right?
00:25:20.895 --> 00:25:25.846
Between your manager, between leadership and the employees of the company, right?
00:25:25.846 --> 00:25:31.695
So this is the old version, but now data and technology is a part of this, right?
00:25:31.695 --> 00:25:33.155
So it's do we trust data?
00:25:33.756 --> 00:25:36.276
Do we trust technology to make decisions for us?
00:25:36.276 --> 00:25:40.506
And when we had this conversation in this big room of senior executives.
00:25:41.355 --> 00:25:44.776
The reaction was like, oh my God, this is absolutely right.
00:25:44.826 --> 00:25:59.986
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.
00:26:00.036 --> 00:26:04.425
So when I speak about data ai literacy programs.
00:26:05.070 --> 00:26:07.111
This is not about training tools.
00:26:07.211 --> 00:26:13.480
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.
00:26:13.901 --> 00:26:15.760
No, it's not about understanding data.
00:26:16.030 --> 00:26:24.040
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?
00:26:24.040 --> 00:26:28.480
And how you should be working together so you can actually trust the data, right?
00:26:28.871 --> 00:26:29.980
So really different center.
00:26:30.580 --> 00:26:37.270
So what then would you say like the for the listeners, what skills that are becoming essential?
00:26:37.601 --> 00:26:39.461
That people should maybe focus more.
00:26:39.490 --> 00:26:41.080
'cause obviously it's changed now.
00:26:41.080 --> 00:26:42.580
Obviously there's so much, there's so much to learn, isn't there?
00:26:42.580 --> 00:26:44.560
With generative ai, it's all constantly evolved.
00:26:44.560 --> 00:26:50.351
But is there any other key skills that people should pay attention to for this?
00:26:50.351 --> 00:26:52.901
Like I guess more of a product driven environment.
00:26:53.500 --> 00:26:56.201
This all comes into soft skills, right?
00:26:56.201 --> 00:26:57.911
So this is your cultural agility.
00:26:58.721 --> 00:26:59.980
This is your curiosity.
00:27:00.580 --> 00:27:02.201
This is the skill to partner.
00:27:02.800 --> 00:27:11.721
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?
00:27:12.111 --> 00:27:16.351
Collaboration, up and down, listening to customer negotiation.
00:27:16.711 --> 00:27:21.181
So these are the skills which absolutely needed for the future, right?
00:27:21.181 --> 00:27:22.766
So I'm currently recommending again.
00:27:23.421 --> 00:27:27.371
I'm speaking up for people on traditional enterprises, right?
00:27:27.671 --> 00:27:42.000
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?
00:27:42.000 --> 00:27:43.320
So being a agile.
00:27:43.320 --> 00:27:45.776
So these are type of skills are much needed for the future.
00:27:46.375 --> 00:27:47.726
And I guess this is gonna be.
00:27:48.596 --> 00:27:51.266
A massive step for a lot of people in the tech world.
00:27:51.296 --> 00:27:54.355
'cause obviously traditionally they're really technically gifted.
00:27:54.726 --> 00:27:58.086
And this is gonna be coming outta their shell, isn't it really to get these soft skills?
00:27:58.115 --> 00:28:09.905
'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.
00:28:10.746 --> 00:28:10.955
Yeah.
00:28:10.955 --> 00:28:14.705
Like focused on your soft skills and I guess understanding problems now.
00:28:15.306 --> 00:28:17.256
Yeah, no, ab absolutely right.
00:28:17.256 --> 00:28:19.740
I think we are just in the beginning of.
00:28:20.340 --> 00:28:23.431
Like a major changes in a career pass, right?
00:28:23.431 --> 00:28:27.290
So it's not gonna be like a linear career pass anymore, right?
00:28:27.290 --> 00:28:29.631
So it's gonna be more matrix career pass.
00:28:29.941 --> 00:28:33.030
It's not gonna be differentiation or technical people.
00:28:33.030 --> 00:28:34.441
And this are business people, right?
00:28:34.441 --> 00:28:36.780
So it's gonna be the new cohort of people.
00:28:37.070 --> 00:28:44.131
Which actually understand both sides, the business and the technical sides, and can actually run the business more technically.
00:28:44.230 --> 00:28:45.911
We already have great examples.
00:28:45.911 --> 00:28:48.211
I'm just going back to me, at the Amazon, right?
00:28:48.211 --> 00:28:51.556
So these are the new generation of leaders who grew up.
00:28:52.175 --> 00:28:54.695
Running the technology type of business, right?
00:28:54.935 --> 00:29:04.576
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?
00:29:04.796 --> 00:29:06.826
We are gonna see the massive, massive change.
00:29:07.135 --> 00:29:09.145
So this technical people, we said, right?
00:29:09.306 --> 00:29:11.855
But business people also have a gap, right?
00:29:11.855 --> 00:29:12.941
So this is an example.
00:29:13.240 --> 00:29:14.171
A guy now.
00:29:14.435 --> 00:29:15.846
It's going so crazy.
00:29:15.846 --> 00:29:21.550
I'm talking to development teams and they have so many requests from business leaders to develop all kind of agents, right?
00:29:22.000 --> 00:29:27.851
But when we talk to business people and I say, okay, how are you gonna evaluate your digital employee?
00:29:28.270 --> 00:29:31.901
So it's interesting response because it's like, what do you mean digital employee?
00:29:32.171 --> 00:29:36.431
I was like, this agent is actually gonna make your sales now.
00:29:37.111 --> 00:29:38.461
You are accountable, right?
00:29:38.621 --> 00:29:45.641
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?
00:29:45.641 --> 00:29:46.330
I was like no.
00:29:46.330 --> 00:29:50.141
This is your job because this is your digital employee, right?
00:29:50.441 --> 00:29:58.560
So we also need different type of skills on a, on the business side to understand how you manage technology as your new employees.
00:29:59.161 --> 00:30:06.510
So would you say, obviously the rise of ai, is it accelerating or complicating the move towards more product centric roles?
00:30:07.320 --> 00:30:08.250
It is accelerate.
00:30:08.340 --> 00:30:10.111
It's accelerate a hundred percent.
00:30:10.161 --> 00:30:12.181
Ben as I'm saying, is people wake up.
00:30:12.181 --> 00:30:16.740
It's like you, you have to invest in yourself and get this type of skills right?
00:30:16.740 --> 00:30:20.780
Start from, reading internet, watch YouTube videos, buy some books.
00:30:20.780 --> 00:30:20.840
I.
00:30:21.371 --> 00:30:32.391
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?
00:30:32.391 --> 00:30:34.671
You have to invest in your education.
00:30:35.361 --> 00:30:37.431
It's gonna be like, must have for the future.
00:30:37.560 --> 00:30:44.016
This is the way for you to to actually, stand up around the crowd and get your leadership spot.
00:30:44.615 --> 00:30:55.385
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?
00:30:55.986 --> 00:30:58.306
So the first step, what I would recommend is.
00:30:59.070 --> 00:31:01.961
People is asking I want to, be in like a product.
00:31:01.990 --> 00:31:04.090
It's like you, you have some management skill.
00:31:04.330 --> 00:31:04.961
Skill, right?
00:31:05.260 --> 00:31:07.901
So people are always saying yes, I want to be a manager, okay?
00:31:07.901 --> 00:31:11.050
Before you want to be manager, be the leader first, right?
00:31:11.050 --> 00:31:11.740
So what does it mean?
00:31:11.740 --> 00:31:12.520
Be the leader first.
00:31:12.520 --> 00:31:15.401
Nobody's pre preventing you to be the leader, right?
00:31:15.851 --> 00:31:17.351
Do the first exercise.
00:31:17.381 --> 00:31:18.671
This is my recommendation.
00:31:19.090 --> 00:31:19.865
Go to your customer.
00:31:20.465 --> 00:31:26.766
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.
00:31:26.816 --> 00:31:28.661
But go into discovery process.
00:31:29.391 --> 00:31:33.681
For example, ask your customer to take you to the sales call, right?
00:31:33.681 --> 00:31:36.621
Or to the sales trip, or go to the factory.
00:31:37.070 --> 00:31:40.671
So start understanding better the customer and the business, right?
00:31:40.671 --> 00:31:41.601
So this is doable.
00:31:42.050 --> 00:31:43.520
You can do it, right?
00:31:43.851 --> 00:31:48.980
So if customer's gonna say no, I'm not interested to take you, you can still do it, right?
00:31:48.980 --> 00:31:52.441
So go to the store, see how the business is working right?
00:31:52.441 --> 00:31:55.651
Find a way to learn more about the need of your business.
00:31:56.250 --> 00:31:57.086
Yeah, no, definitely.
00:31:57.086 --> 00:31:58.016
I think it's.
00:31:58.256 --> 00:31:58.901
First step.
00:31:58.901 --> 00:31:59.351
Yes.
00:31:59.965 --> 00:32:17.990
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?
00:32:18.590 --> 00:32:20.605
Absolutely no ab, absolutely.
00:32:20.605 --> 00:32:23.665
So definitely be more curious, right?
00:32:23.665 --> 00:32:24.655
But do not.
00:32:25.135 --> 00:32:29.066
So what I see is technical people, they, they all including myself, right?
00:32:29.306 --> 00:32:31.855
So we all excited about solutions.
00:32:31.915 --> 00:32:33.836
We are solutions people, right?
00:32:34.181 --> 00:32:38.980
So we see the first answer and we immediately like, oh, I know how to solve it.
00:32:39.030 --> 00:32:39.330
No.
00:32:39.361 --> 00:32:40.111
Stop here.
00:32:40.621 --> 00:32:43.651
So you have to ask at least a hundred questions.
00:32:44.111 --> 00:32:48.490
Hold yourself on any solution, hold yourself, forget about solution, right?
00:32:48.971 --> 00:32:52.211
Hand have more deeper conversation, right?
00:32:52.211 --> 00:32:58.931
So so you can actually understand what is the real problem, because the first sentence you might hear.
00:32:59.306 --> 00:33:00.776
It's not gonna be the real problem.
00:33:01.326 --> 00:33:04.625
And maybe the fifth sentence you're gonna hear, it's not gonna be real problem.
00:33:04.665 --> 00:33:11.066
We have to have deeper conversations and maybe more, informal conversations, right?
00:33:11.066 --> 00:33:16.911
So I'll give you in my examples when I was, going on a sales trip, like in in the pharma for example, right?
00:33:17.131 --> 00:33:18.931
We visit some doctors, right?
00:33:18.931 --> 00:33:22.381
And I can absorb how the sales conversation is happening, right?
00:33:22.780 --> 00:33:30.050
So then we, we live in the office and salesperson is actually supposed to enter information about the visa to the system, right?
00:33:30.451 --> 00:33:36.000
And I'm seeing how this person is struggling to do this because there is no internet connection, right?
00:33:36.000 --> 00:33:38.625
So if I don't, don't see it, right?
00:33:38.625 --> 00:33:43.165
So I might not, I don't understand why we have missing fields.
00:33:43.766 --> 00:33:47.786
We have missing fields because people simply didn't have internet connection in the location.
00:33:47.836 --> 00:33:54.346
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?
00:33:54.526 --> 00:33:54.945
So you.
00:33:55.546 --> 00:33:59.746
So obviously you've mentioned a couple of companies like Meta and about obviously getting it right.
00:34:00.135 --> 00:34:06.951
So when companies are getting product thinking right in data ai, what kind of business value shows up first?
00:34:07.550 --> 00:34:14.490
So I think first what the business, first of all, you can definitely see much better agility and and efficiency, right?
00:34:14.791 --> 00:34:19.320
So you are gonna be able to de, to deliver the value much quicker, right?
00:34:19.590 --> 00:34:26.161
Because the product mindset means that you develop, you deve you you develop the value in incremental steps.
00:34:26.791 --> 00:34:30.201
So it's like you almost because it's a like Iranian cycle, right?
00:34:30.201 --> 00:34:34.251
So this is not the project, oh wait, several months and this is your answer, right?
00:34:34.550 --> 00:34:39.621
You are constantly gonna be able to work with your customer and create incremental value, right?
00:34:39.621 --> 00:34:41.510
So this is the first benefit, right?
00:34:41.510 --> 00:34:43.851
So the value is gonna start showing right away.
00:34:44.675 --> 00:34:52.596
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.
00:34:52.626 --> 00:34:54.215
It's not gonna work in my environment.
00:34:54.405 --> 00:34:56.206
So this is the first benefit you're gonna see.
00:34:56.806 --> 00:35:01.201
Okay, so do you feel, obviously these, with businesses, building teams, do you think.
00:35:01.800 --> 00:35:03.030
Obviously I've called you the glue.
00:35:03.090 --> 00:35:07.021
Should they be hiring more people that can act as a glue within a business?
00:35:07.021 --> 00:35:11.951
So then they bring the business and tech closer together.
00:35:12.550 --> 00:35:13.076
Y Yes.
00:35:13.376 --> 00:35:13.976
Abso absolutely.
00:35:13.976 --> 00:35:21.545
We are already seeing right now that like having the business partnership is a requirement for all leadership type of roles, right?
00:35:21.545 --> 00:35:24.315
So do you want to be the technology director?
00:35:24.405 --> 00:35:26.266
Do you want to be the chief data officer?
00:35:26.266 --> 00:35:27.166
The first skill is.
00:35:27.920 --> 00:35:30.561
That you actually have to be able to build the business case.
00:35:30.561 --> 00:35:33.530
You have to be the partner, you have to understand the business, right?
00:35:33.891 --> 00:35:38.431
So these are the skills which are always, gonna be the skills for the senior leaders.
00:35:38.431 --> 00:35:40.721
Also I tell like a middle.
00:35:41.045 --> 00:35:43.425
Me, middle level man managers.
00:35:43.425 --> 00:35:46.695
This is a big cohort of people who come to me for career advice.
00:35:47.065 --> 00:35:51.916
I'm saying for you to grow up you're not gonna grow by IT and more tools, right?
00:35:51.916 --> 00:35:56.865
So you're gonna grow by it and leadership skills, better understanding of the business, right?
00:35:56.865 --> 00:35:59.266
So this is absolute requirement for you to grow.
00:35:59.865 --> 00:36:00.706
Yeah, no, definitely.
00:36:00.706 --> 00:36:06.195
And I think it's gonna be, I think as AI matures, it's gonna get deeper and deeper, isn't it?
00:36:06.195 --> 00:36:08.235
Into every area of the business.
00:36:08.266 --> 00:36:10.065
It's, and that's just the way it's gonna get.
00:36:10.065 --> 00:36:12.081
So if you've got that, the knack.
00:36:12.681 --> 00:36:14.771
like you said to keep asking questions why?
00:36:14.771 --> 00:36:20.351
Getting deeper into the problem, you are gonna be in a better position to add value to the company.
00:36:20.951 --> 00:36:24.851
Yeah, no, a abs absolutely, you go, you're gonna be like in a better position.
00:36:24.851 --> 00:36:28.731
And plus again, understanding how the business is working, right?
00:36:29.001 --> 00:36:32.971
So I think I'm, maybe I, I feel I'm lucky in my career, right?
00:36:32.971 --> 00:36:38.880
Because one of the major role when, when I actually, came to United States back in like in 2000.
00:36:39.210 --> 00:36:45.911
So I was hired by the Wall Street Journal, Dow Jones to establish basically data and analytics practice, right?
00:36:45.911 --> 00:36:47.471
Which didn't exist for digital.
00:36:47.840 --> 00:36:51.231
And I had the chance to work in a digital publishing.
00:36:51.856 --> 00:36:55.396
Before even digital transformation started, right?
00:36:55.815 --> 00:37:01.155
So this gave me opportunity to better understand how to work in this product model, right?
00:37:01.155 --> 00:37:03.496
So we did not call it the product model, right?
00:37:03.916 --> 00:37:06.885
But this almost an Amazon type of requirement, right?
00:37:07.195 --> 00:37:11.646
I remember one of the program I was driving is personalization, right?
00:37:11.646 --> 00:37:12.666
So like everybody.
00:37:13.115 --> 00:37:22.976
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?
00:37:22.976 --> 00:37:24.945
So you're going to get longer time value, right?
00:37:24.945 --> 00:37:26.686
So this is like a hypothesis, right?
00:37:27.346 --> 00:37:29.496
There are two ways to look into this, right?
00:37:29.646 --> 00:37:33.246
So one way is like how we technically prove it, right?
00:37:33.246 --> 00:37:37.056
Can we actually prove that personalization is driving more usage, right?
00:37:37.356 --> 00:37:41.561
But then the business side comes is this gonna convert to p and l?
00:37:42.085 --> 00:37:51.865
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.
00:37:52.195 --> 00:37:57.206
So this type of realization is gonna convert to p and l and this one is not gonna convert.
00:37:57.545 --> 00:38:01.146
And you are gonna see the room of people like really senior chief product officer.
00:38:02.061 --> 00:38:05.121
She was like, oh my God, Juliana, you changed my mind.
00:38:05.121 --> 00:38:06.530
I did not even think this way.
00:38:06.530 --> 00:38:12.590
That this presentation is not gonna convert to p and l, which means that we don't want to invest, right?
00:38:12.590 --> 00:38:17.090
So technically you can think, oh yeah, let's do everything gonna be personalized, right?
00:38:17.391 --> 00:38:25.550
But if it's not gonna convert to your pro profit, basically, or your additional customer growth, then it does make sense to do.
00:38:25.550 --> 00:38:26.331
And I would tell you.
00:38:27.215 --> 00:38:28.195
It's rarely.
00:38:28.286 --> 00:38:35.295
You can see the people especially like a mid-level, who are crunching all these numbers are thinking this way.
00:38:35.775 --> 00:38:38.896
So they need to start more about how the business is made.
00:38:39.315 --> 00:38:44.246
What is, what is our profit, what is our loss, what is operational efficiency?
00:38:44.545 --> 00:38:48.081
To better connect all the science projects to real business.
00:38:48.681 --> 00:38:53.885
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.
00:38:54.880 --> 00:38:58.960
Got to point in their career and they've done like a sideways move to more into the business.
00:38:59.170 --> 00:39:02.411
And then further on in their career, it's really benefited them.
00:39:02.721 --> 00:39:07.840
So I think it's, there's a lot of different ways you can go in the future, especially for leaders.
00:39:07.840 --> 00:39:07.985
You could see.
00:39:08.826 --> 00:39:18.996
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.
00:39:18.996 --> 00:39:28.856
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.
00:39:29.811 --> 00:39:30.851
A hundred percent.
00:39:30.851 --> 00:39:33.911
And actually some companies are doing this type of rotation.
00:39:33.911 --> 00:39:37.280
So for example, Johnson, actually we did quite of rotations, right?
00:39:37.280 --> 00:39:40.010
So like somebody from my team, like a business analyst.
00:39:40.601 --> 00:39:43.931
We sent her to the marketing organization for six months, right?
00:39:44.260 --> 00:39:45.860
And and she didn't like it.
00:39:45.860 --> 00:39:47.751
She was like, I don't want to be in marketing.
00:39:47.811 --> 00:39:50.541
No, go and learn what marketing is doing, right?
00:39:50.541 --> 00:39:52.190
Because, we working a lot with brands.
00:39:52.190 --> 00:39:54.081
You need to understand the brand structure, right?
00:39:54.570 --> 00:39:59.186
So this, these are like you, you basically need to give this type of skills to rat, rotate people.
00:39:59.186 --> 00:39:59.815
And the same.
00:40:00.260 --> 00:40:04.070
Take somebody from the business team and put them into technology organization.
00:40:04.070 --> 00:40:10.101
So for me is actually just maybe, how I came to to my role as a chief data ai.
00:40:10.490 --> 00:40:12.110
I started from the business, right?
00:40:12.110 --> 00:40:17.141
So when I was working in a publishing, I was in a product organization working directly with the business.
00:40:17.541 --> 00:40:21.376
And my, my fewest technical role, it's actually happened to be.
00:40:21.936 --> 00:40:23.405
When I joined Johnson, right?
00:40:23.405 --> 00:40:26.376
And this is where I started more being on the technical side, right?
00:40:26.715 --> 00:40:36.315
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?
00:40:36.596 --> 00:40:37.976
And we need both ways.
00:40:38.155 --> 00:40:42.880
We need people actually both ways to take this type of steps because there are a lot of really.
00:40:43.481 --> 00:40:48.911
Super smart and the people who are really understand the future of technology on the business side.
00:40:49.371 --> 00:40:54.431
But they feel that going back work for technology, it's almost like a step back.
00:40:54.911 --> 00:40:56.170
No, it's not the step back.
00:40:56.201 --> 00:40:59.081
You can be one of the best technical leaders, right?
00:40:59.081 --> 00:41:01.240
So if you come from the business to the technology.
00:41:01.840 --> 00:41:02.050
Yeah.
00:41:02.050 --> 00:41:02.590
No, definitely.
00:41:02.590 --> 00:41:02.860
Brilliant.
00:41:03.101 --> 00:41:03.670
Okay, brilliant.
00:41:03.670 --> 00:41:07.715
I really appreciate your insights Anna, I guess any I love your passion, by the way.
00:41:07.795 --> 00:41:10.585
Any, any final words you'd like to leave with the listeners?
00:41:11.186 --> 00:41:16.175
So I think my final word is is definitely I think I already said enough, is, wake up people.
00:41:16.565 --> 00:41:21.635
So unfortunately that AI is is gonna be, yeah, it's gonna put pressure on us.
00:41:21.635 --> 00:41:24.175
It's putting pressure on people who build ai.
00:41:24.505 --> 00:41:26.456
Gonna feel it first, right?
00:41:26.456 --> 00:41:28.445
Wake up invest in yourself.
00:41:28.876 --> 00:41:32.085
Nobody's gonna help you unless you help yourself, right?
00:41:32.085 --> 00:41:34.246
So there are so many resources available.
00:41:34.246 --> 00:41:36.376
As I mentioned, there are wonderful communities.
00:41:36.376 --> 00:41:39.315
There is a YouTube, there is a substack, right?
00:41:39.346 --> 00:41:40.576
But take care of yourself.
00:41:40.990 --> 00:41:44.291
So this is gonna be my last message because I love you so much.
00:41:44.661 --> 00:41:48.911
I want everybody in data analytics side to be successful, but please wake up.
00:41:49.510 --> 00:41:49.840
Brilliant.
00:41:49.900 --> 00:41:51.161
Thank you for your conversation.
00:41:51.760 --> 00:41:52.751
Thank you so much, Ben.
00:41:53.351 --> 00:41:56.050
And I guess for thank you for listeners for listening.
00:41:56.110 --> 00:42:01.990
If you want more conversations with data experts, please make sure you follow or subscribe to Data Analytics Chat.
00:42:02.351 --> 00:42:05.621
And so don't miss future episodes and we'll see you next time.
00:00:00.000 --> 00:00:06.929
So you need to see the process at the factory, how all machines are connected where the break potentially could be happening.
00:00:07.150 --> 00:00:14.900
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.
00:00:15.179 --> 00:00:22.800
And I would honestly say only maybe 10% of the people are able to clearly formulate the customer problem, right?
00:00:22.800 --> 00:00:25.859
Because they can say, oh, I did customer segmentation.
00:00:25.890 --> 00:00:27.359
I'm sorry, this is not the problem.
00:00:27.960 --> 00:00:31.289
So we still give out ourself an excuse saying that.
00:00:31.890 --> 00:00:34.020
Technology is enabler, right?
00:00:34.020 --> 00:00:36.719
So technology is not an enabler anymore.
00:00:36.719 --> 00:00:39.030
Technology is a new way of doing the business
00:00:51.676 --> 00:00:55.665
Data and AI careers are no longer built just around projects.
00:00:55.665 --> 00:00:59.716
They're built around products, outcomes, and long-term own ownership.
00:01:00.165 --> 00:01:18.075
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.
00:01:18.456 --> 00:01:25.355
PIN careers for data analytics leaders in the age of AI and why product skills are becoming essential.
00:01:25.956 --> 00:01:27.486
Ena, welcome to the show.
00:01:28.085 --> 00:01:29.135
Thank you so much, Ben.
00:01:29.135 --> 00:01:30.365
I'm so happy to be here.
00:01:30.965 --> 00:01:31.296
Me too.
00:01:31.296 --> 00:01:33.245
It is gonna be obviously a fascinating question.
00:01:33.786 --> 00:01:40.415
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?
00:01:41.016 --> 00:01:41.706
Absolutely.
00:01:41.706 --> 00:01:45.876
So I'm in data analytics and AI for over 25 years.
00:01:46.256 --> 00:01:49.346
I had an international career, which was quite of lucky.
00:01:49.346 --> 00:01:49.885
I have been.
00:01:50.256 --> 00:01:59.146
Crossing the borders, working for companies, the global companies across the globe started as as many of us from engineering education.
00:01:59.445 --> 00:02:03.736
But I did get my additional education, which have been more like on the business side.
00:02:04.561 --> 00:02:10.300
Definitely have been fortunate enough to work for, as you mentioned, for four fortune 500 companies.
00:02:10.610 --> 00:02:14.061
Started in publishing for Dow Jones Youth Corporation.
00:02:14.091 --> 00:02:19.531
Moved into healthcare companies like Johnson, one of the most admired companies in the globe.
00:02:19.871 --> 00:02:21.941
So then Nestle Danon.
00:02:22.040 --> 00:02:24.131
And most recently its connectivity.
00:02:24.730 --> 00:02:26.381
So you obviously work with some of the Yeah.
00:02:26.501 --> 00:02:28.721
Large, complex organizations there.
00:02:29.320 --> 00:02:37.360
Yes, always have been working with large complex metrics organizations where you have multiple businesses, multiple franchises.
00:02:37.610 --> 00:02:39.471
We constantly have been growing by.
00:02:39.855 --> 00:02:43.665
Acquiring new companies or diversifying companies, right?
00:02:43.665 --> 00:02:44.996
So you can think about that.
00:02:45.086 --> 00:02:47.485
I'm not just traditional like chief data and ai.
00:02:47.756 --> 00:02:54.890
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.
00:02:55.491 --> 00:03:02.001
Yeah, so you obviously you've got a good blend then of business and data skills, which is obviously, I think, key today.
00:03:02.031 --> 00:03:07.700
'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.
00:03:07.700 --> 00:03:10.540
So you've got the bl, the nice, the glue in the middle.
00:03:11.140 --> 00:03:12.971
Yes, def definitely in the middle.
00:03:12.971 --> 00:03:27.480
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.
00:03:27.961 --> 00:03:30.991
And this was marketing how to sell the technology.
00:03:31.431 --> 00:03:33.050
And I got so much excited.
00:03:33.050 --> 00:03:35.540
The professor was absolutely amazing, professor.
00:03:35.811 --> 00:03:42.330
So that she basically convinced me after I got my engineering degree, join joining the PhD program.
00:03:42.420 --> 00:03:47.881
So I got my PhD in marketing and economics, which is really unusual combination, right?
00:03:47.881 --> 00:03:49.920
So engineering and marketing economics.
00:03:50.350 --> 00:03:52.855
But I think this type of skill is actually helping me because.
00:03:53.570 --> 00:03:57.950
You need to sell the technology to people, you need to explain technology to people, right?
00:03:57.950 --> 00:04:01.080
So you have to be a marketer and communicator.
00:04:01.300 --> 00:04:04.480
So this was one of the most defining moments in my career, I would say.
00:04:05.080 --> 00:04:05.591
Fascinating.
00:04:05.711 --> 00:04:06.431
Yeah, no, definitely.
00:04:06.431 --> 00:04:14.080
I think storytelling easier, obviously, especially in today, like you need to be able to communicate across all levels, business, tech, data.
00:04:14.681 --> 00:04:19.771
So then do you, is there been an experience that sort of most shaped how you approach leading AI initiatives?
00:04:20.370 --> 00:04:27.540
So I always think about what is starting the first step is what is really, we need to do for the business, right?
00:04:27.880 --> 00:04:30.641
You're gonna hear a lot like define the business problem.
00:04:30.641 --> 00:04:33.461
I think it's not good enough defining the business problem.
00:04:33.461 --> 00:04:36.821
You need to understand this, how the business is working, right?
00:04:36.821 --> 00:04:40.271
So where are the biggest pools are happening in the business, right?
00:04:40.571 --> 00:04:44.800
So if you and manufacturing company actually that how the product gets designed, right?
00:04:44.800 --> 00:04:46.571
Because if there is no product designed.
00:04:47.065 --> 00:04:48.415
There is no company, right?
00:04:48.415 --> 00:04:52.615
So then you think about how the product gets actually manufactured, right?
00:04:52.615 --> 00:04:54.925
So how the product is moving to the customer, right?
00:04:54.925 --> 00:04:59.255
So you need to to really experience all this all these moments, right?
00:04:59.646 --> 00:05:05.026
And as a leader, what I usually start my journey always I go directly to customers.
00:05:05.086 --> 00:05:07.005
I go directly to the factory.
00:05:07.350 --> 00:05:14.730
I talk to people who are doing the business every single day, and I'm not asking them is what technology you need, right?
00:05:14.730 --> 00:05:17.790
So I'm observing how the process is happening.
00:05:18.130 --> 00:05:19.841
What is possible to do, right?
00:05:19.841 --> 00:05:22.060
So the conversation is about them.
00:05:22.060 --> 00:05:23.201
It's not about me.
00:05:23.201 --> 00:05:25.031
What technology I can implement, right?
00:05:25.391 --> 00:05:31.326
So I'll give you one example is like in manufacturing, one of the biggest use case, it's predictive maintenance, right?
00:05:31.646 --> 00:05:35.326
It's maintenance like a, the factory, it's super expensive, right?
00:05:35.326 --> 00:05:41.315
So if you stop the factory line you're gonna lose millions of dollars or maybe even hundreds of millions of dollars, right?
00:05:41.716 --> 00:05:44.896
You need to have a kind of prediction when you should stop it, right?
00:05:45.266 --> 00:05:47.115
And when you look at this task.
00:05:47.836 --> 00:05:51.735
Purely as a data exercise it's not gonna work, right?
00:05:51.735 --> 00:05:58.665
So you need to see the process at the factory, how all machines are connected where the break potentially could be happening.
00:05:58.886 --> 00:06:06.636
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.
00:06:06.906 --> 00:06:13.815
So we do need people to experience the real work before they actually build in any technology and models.
00:06:13.846 --> 00:06:15.615
So this always have been my approach.
00:06:15.915 --> 00:06:21.975
Or if I'm in retailer environment like for example, working for Johnson Consumer Business.
00:06:22.225 --> 00:06:25.086
I have been traveling all, all countries all around the globe.
00:06:25.326 --> 00:06:30.706
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.
00:06:31.091 --> 00:06:32.591
How people buy my product.
00:06:32.711 --> 00:06:34.420
Where is my product in the store?
00:06:34.600 --> 00:06:36.310
How they can find it, right?
00:06:36.310 --> 00:06:38.591
What is the next product sent into my product?
00:06:38.591 --> 00:06:41.975
So seeing all of this is helping me to better understand how I can help the business.
00:06:42.576 --> 00:06:42.865
Yeah.
00:06:42.920 --> 00:06:43.430
And I like that.
00:06:43.461 --> 00:06:46.071
'cause you are, it's more you're thinking outside the box.
00:06:46.071 --> 00:06:46.930
You're not in your role.
00:06:47.230 --> 00:06:48.365
You're thinking as a, a.
00:06:48.966 --> 00:06:50.286
overall picture, aren't you?
00:06:50.766 --> 00:06:56.795
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?
00:06:57.396 --> 00:07:01.846
You need to take a lot but also you need to ask deeper questions, right?
00:07:01.846 --> 00:07:06.536
So what I'm observing is that we stopped early on questions, right?
00:07:06.536 --> 00:07:07.076
So even.
00:07:07.425 --> 00:07:17.185
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?
00:07:17.536 --> 00:07:24.795
But they don't go deeper to understand where the break is happening, what expectations from the factory, what challenges factory having right.
00:07:25.130 --> 00:07:29.161
So listening to customer should be significantly increased, right?
00:07:29.350 --> 00:07:37.625
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.
00:07:38.036 --> 00:07:45.656
And I would honestly say only maybe 10% of the people are able to clearly formulate the customer problem, right?
00:07:45.656 --> 00:07:48.716
Because they can say, oh, I did customer segmentation.
00:07:48.745 --> 00:07:50.216
I'm sorry, this is not the problem.
00:07:50.815 --> 00:07:54.776
So you need to understand why you have been doing the customer segmentation.
00:07:55.165 --> 00:07:58.651
What exactly changed for the business by you doing the customer segmentation.
00:07:59.250 --> 00:08:02.586
Yeah, and I think nowadays data roles are going that way as well.
00:08:03.156 --> 00:08:06.591
And we've got the tools can do the heavy lifting, especially like the data scientists now.
00:08:06.591 --> 00:08:10.370
It's, if you can understand the business problems, that's where you are getting the value.
00:08:10.430 --> 00:08:15.141
'cause you can add so much more knowledge and expertise in that domain.
00:08:15.500 --> 00:08:20.391
And again, like obviously, like we used to hire data scientists say a couple years ago, and that's more so techie focused.
00:08:20.391 --> 00:08:28.661
But now like obviously the game's completely changed and now businesses are looking for that strategic thinking creative thinking business domain knowledge.
00:08:29.261 --> 00:08:30.565
Absolutely a hundred percent.
00:08:30.565 --> 00:08:38.625
So it's so this is the the danger, which I'm warning in my people through my newsletter like record, because I do.
00:08:38.806 --> 00:08:43.275
Believe that analytics people have to record the career they have right now.
00:08:43.676 --> 00:08:45.745
Is that you should not be sitting still.
00:08:46.400 --> 00:08:50.336
Because the, your technical skills, I'm not gonna, I'm not gonna serve you.
00:08:50.365 --> 00:08:51.985
They already not serving you, right?
00:08:51.985 --> 00:08:56.206
So you do need to get additional, maybe additional education, right?
00:08:56.206 --> 00:08:59.255
So you need to get additional understanding of the business.
00:08:59.255 --> 00:09:02.316
You need to invest in yourself to understand the business, right?
00:09:02.316 --> 00:09:09.176
Because it's interesting that AI is challenging the people who are front line of ai, right?
00:09:09.176 --> 00:09:11.696
So data analytics, AI people are forced.
00:09:12.296 --> 00:09:16.255
Challenge right now in in the career because they do need to have different skills.
00:09:16.855 --> 00:09:17.125
Yeah.
00:09:17.456 --> 00:09:22.405
So then obviously there's the shift from data projects to data products Now.
00:09:22.525 --> 00:09:29.096
So in your view, what does that shift really mean for data analytics teams on the ground?
00:09:29.696 --> 00:09:31.525
Yes, it's really interesting.
00:09:31.525 --> 00:09:40.355
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?
00:09:40.836 --> 00:09:48.125
What is interesting is 90% of these conversations are gonna be arguing about technicality of the term, right?
00:09:48.125 --> 00:09:57.676
So is this architectural decision is this a certain container or having the like an agreement with your consumer, whatever it is, right?
00:09:58.035 --> 00:10:03.826
So 90% of conversation is gonna be about the technical side, how architecturally you do it, right?
00:10:04.066 --> 00:10:10.306
And I think this is what our community is doing completely wrong because the product is coming from the customer.
00:10:10.306 --> 00:10:16.755
Understand the first step is spend time with the customer, understand how the data is changing the business.
00:10:17.216 --> 00:10:19.966
And designing around the customer, right?
00:10:20.265 --> 00:10:33.895
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?
00:10:33.895 --> 00:10:38.035
They all come with concept that you need to have a refrigerated architecture, right?
00:10:38.035 --> 00:10:40.495
So technically this is absolutely correct.
00:10:40.900 --> 00:10:46.701
But what nobody's saying that federated architecture 90% of success is not technical.
00:10:46.910 --> 00:10:49.250
It's actually about how we work, right?
00:10:49.250 --> 00:10:50.211
And how we work.
00:10:50.510 --> 00:10:52.910
We have to work around the customer need, right?
00:10:52.910 --> 00:10:55.431
So this could be internal customer, right?
00:10:55.431 --> 00:11:00.850
If we serving sales organization, marketing organization we serving for example, manufacturing, right?
00:11:01.201 --> 00:11:03.671
Or this could be around external customer.
00:11:03.671 --> 00:11:07.880
If we build in the product, which we actually sell to to the market, to external, right?
00:11:08.390 --> 00:11:15.130
This is really unfortunate side when I'm seeing the data product I actually mean the product mindset, right?
00:11:15.461 --> 00:11:28.421
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?
00:11:28.541 --> 00:11:32.081
We invested massively in training people.
00:11:32.475 --> 00:11:33.855
In a product mindset.
00:11:34.066 --> 00:11:37.096
So we did not train them in a technical architecture, right?
00:11:37.365 --> 00:11:48.995
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?
00:11:49.426 --> 00:11:51.436
So this is the thing I'm seeing right?
00:11:51.941 --> 00:11:58.990
So why I believe that the product mindset is absolutely must have for ai, right?
00:11:59.530 --> 00:12:03.360
So this is how I explain to to business executives, right?
00:12:03.691 --> 00:12:05.640
So what AI is doing for the business.
00:12:05.941 --> 00:12:10.191
Without ai we always have been growing like like a linear business, right?
00:12:10.191 --> 00:12:15.081
So you design your product, manufacture product, shipped to customer, serve your customer, right?
00:12:15.081 --> 00:12:16.071
So this is the line.
00:12:16.461 --> 00:12:22.750
Every single time we want to get more product shipped, we want to get more customers.
00:12:23.000 --> 00:12:24.650
We add more resources, right?
00:12:24.650 --> 00:12:27.441
So we create, we like buy another factory.
00:12:27.441 --> 00:12:30.081
We create another call center for customers, right?
00:12:30.500 --> 00:12:33.321
So what AI is doing, AI is helping to create.
00:12:33.860 --> 00:12:37.821
I call it like acceleration loop in each cycle, right?
00:12:37.821 --> 00:12:46.221
So basically instead of eating additional resources, building the product, AI can actually help me to build the product faster, right?
00:12:46.221 --> 00:12:52.280
So then AI can help me, like with predictive man maintenance, quality control to do better manufacturing.
00:12:52.280 --> 00:12:57.581
So I can actually do more, by creating this kind of, AI loop, right?
00:12:57.910 --> 00:13:07.150
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?
00:13:07.181 --> 00:13:14.150
What is the most important closing this loop is actually including the people knowledge closing the loop, right?
00:13:14.510 --> 00:13:20.155
Because what is happening in many AI applications right now is that people get an answer.
00:13:21.020 --> 00:13:24.140
They just store this answer on, in Excel file.
00:13:24.191 --> 00:13:25.301
This is the reality, right?
00:13:25.301 --> 00:13:29.110
So like what you do when you get an answer from GPT, oh, I just copied to Excel, right?
00:13:29.591 --> 00:13:33.221
So that the knowledge is not going back to the system, right?
00:13:33.610 --> 00:13:37.030
The product skill is exactly this loop.
00:13:37.360 --> 00:13:43.821
How you build the knowledge, how you build the knowledge around the customer, how you build the knowledge around around the process, right?
00:13:44.221 --> 00:13:50.610
We massively need this type of skill with all technical data, AI type of organizations, right?
00:13:50.610 --> 00:13:57.441
So AI cannot be project, it's not the project because, because you cannot create the loop if you're in the project, right?
00:13:57.441 --> 00:13:59.910
So you need to constantly learn constantly.
00:14:00.510 --> 00:14:19.530
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.
00:14:20.130 --> 00:14:22.441
That's another component to the skillset that you need.
00:14:22.441 --> 00:14:27.750
Don't you need the data science skills, the actuary knowledge, and then the techie stuff.
00:14:27.841 --> 00:14:32.431
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?
00:14:32.831 --> 00:14:34.961
So it's gonna be interesting to see how.
00:14:35.561 --> 00:14:38.020
Because obviously you've got people that jump different industries, haven't you?
00:14:38.921 --> 00:14:47.711
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.
00:14:47.921 --> 00:14:51.910
It's gonna be fascinating to see how that plays out because the tech skills.
00:14:52.660 --> 00:14:52.900
Yeah.
00:14:52.900 --> 00:14:56.530
Let's say you've got all these tools now that can help you do the coding.
00:14:56.530 --> 00:15:00.850
It's now gonna become, how can you add value to your strategic creative knowledge?
00:15:01.451 --> 00:15:01.691
Abs.
00:15:01.780 --> 00:15:02.380
Absolutely.
00:15:02.530 --> 00:15:05.880
So te tech skill is not differentiator anymore, right?
00:15:06.221 --> 00:15:10.880
But what is also interesting, because my experience is with traditional enterprises, right?
00:15:10.880 --> 00:15:15.041
So traditional enterprises is like healthcare, banking, manufacturing, right?
00:15:15.041 --> 00:15:20.311
So these are traditional enterprises and there is a completely different world, which is actually.
00:15:20.660 --> 00:15:22.640
Google Meta, Amazon, right?
00:15:22.951 --> 00:15:28.291
In these type of companies, they adopted the product mindset from the day one, right?
00:15:28.291 --> 00:15:29.910
So this is why they're so successful.
00:15:29.910 --> 00:15:36.091
By building the product, building like personalization in Amazon or the way how meta is operates and right.
00:15:36.451 --> 00:15:38.160
So I have been hiring people actually.
00:15:38.975 --> 00:15:42.745
From this type of Es in my teams just to help to build the skill.
00:15:43.206 --> 00:15:48.905
But what I also is finding quite of really different, and some, sometimes it is upsetting, right?
00:15:49.265 --> 00:15:54.696
Because if you go to internet you're gonna see enormous amount of communities.
00:15:54.725 --> 00:15:59.775
There are meta community, there is Amazon community, there is a product sense community.
00:15:59.775 --> 00:16:01.846
There is a product we can community, right?
00:16:02.265 --> 00:16:02.770
All of this.
00:16:03.466 --> 00:16:06.946
Places where people are loaning from each other, right?
00:16:07.245 --> 00:16:13.806
What is interesting is if you look to traditional enterprises, people who are working, healthcare and other kind of industries, right?
00:16:14.285 --> 00:16:15.576
They're not talking to each other.
00:16:15.755 --> 00:16:16.956
They're really silent.
00:16:17.385 --> 00:16:20.561
And they're not taking the step to learn the new skill.
00:16:20.740 --> 00:16:24.610
What I'm seeing with Amazon type of people, they are so proactive.
00:16:24.701 --> 00:16:26.831
They go ahead and take a new skill.
00:16:26.831 --> 00:16:28.600
They take a product class, right?
00:16:29.030 --> 00:16:33.380
And in enterprise, we almost have to force people to take this step, right?
00:16:33.821 --> 00:16:35.331
And and unfortunately.
00:16:35.515 --> 00:16:38.966
They actually gonna sacrifice career if they're not gonna do it, right?
00:16:39.385 --> 00:16:45.916
So what I have been doing lately, like in my this record post I was trying to wake up people saying, wake up.
00:16:45.916 --> 00:16:46.995
You have to speak up.
00:16:46.995 --> 00:16:49.546
You have to go to, to talk to your customer.
00:16:49.546 --> 00:16:51.525
You have to present yourself.
00:16:51.525 --> 00:16:55.405
You cannot be silent, go outside of your comfort zone, right?
00:16:55.716 --> 00:16:59.586
So this is what like must have, and I'm trying to promote as much as possible.
00:17:00.186 --> 00:17:00.966
Yeah, no, definitely.
00:17:00.966 --> 00:17:19.026
'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.
00:17:19.701 --> 00:17:25.101
Not gonna be as value driven as like the techie would expect.
00:17:25.340 --> 00:17:38.846
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.
00:17:39.175 --> 00:17:43.996
But it's gonna be interesting to see how, obviously leaders adapt this way, new way of working.
00:17:44.596 --> 00:17:46.756
And it is needed at all levels.
00:17:46.786 --> 00:17:51.625
So because I have been talking to chief Information officers who actually.
00:17:52.006 --> 00:17:55.546
Argue with me in saying there is no difference between product and project.
00:17:55.996 --> 00:18:01.036
I was like, there is a big difference actually being a product organization and project organization, right?
00:18:01.395 --> 00:18:05.296
So there are education needed at the highest level of technology leadership.
00:18:05.665 --> 00:18:08.395
But also if we go to the colleges, right?
00:18:08.816 --> 00:18:15.036
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.
00:18:15.326 --> 00:18:20.276
And I do have a lot of friends whose kids like the same age and the going through the colleges, right?
00:18:20.635 --> 00:18:27.800
So what is happening right now is the last five years colleges graduated over a million.
00:18:28.401 --> 00:18:36.990
Students in computer science and analytics only analytics alone it's almost like 500,000 people across the US right?
00:18:37.580 --> 00:18:43.990
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?
00:18:44.246 --> 00:18:46.286
They come in with some technical skills.
00:18:46.286 --> 00:18:53.135
And even these technical skills are quite of outdated because most of the colleges are still teach in R Power bi.
00:18:53.135 --> 00:18:56.256
They're not even explaining how you can use the AI tools, right?
00:18:56.685 --> 00:19:00.955
But but they also, colleges do not teach the data strategy.
00:19:01.840 --> 00:19:05.125
Colleges do not teach the any product management skills.
00:19:05.425 --> 00:19:08.185
There are no classes about the soft skills.
00:19:08.675 --> 00:19:13.415
And it's interesting, so when I'm interviewing students coming, like for internships, right?
00:19:13.846 --> 00:19:19.496
You can see amazing cv with a lot of projects and you are asking how you did this project, right?
00:19:19.885 --> 00:19:21.685
So that usually the answer is.
00:19:22.645 --> 00:19:30.246
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?
00:19:30.576 --> 00:19:37.306
But it's actually, I stayed silent in my room and I have been doing, the stuff my own right.
00:19:37.306 --> 00:19:40.635
So what I usually send to students, it's including my son.
00:19:41.451 --> 00:19:54.340
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.
00:19:54.441 --> 00:20:00.161
So you cannot put in your CV enormous amount of technically correct.
00:20:00.490 --> 00:20:03.480
But not, nonsense customer type of projects, right?
00:20:03.730 --> 00:20:12.161
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?
00:20:12.580 --> 00:20:18.171
Teaching my own son and his response is, mom, this actually works.
00:20:18.171 --> 00:20:19.820
I was like, yes, I told you.
00:20:20.451 --> 00:20:22.101
Go outside of your car zone.
00:20:22.191 --> 00:20:23.181
Talk to people.
00:20:23.330 --> 00:20:24.590
And he was like, you know what?
00:20:24.590 --> 00:20:26.270
I'm amazed people are responding.
00:20:26.300 --> 00:20:29.030
I'm, I was like, of course they need help, right?
00:20:29.300 --> 00:20:33.405
So be more proactive and and this is the way how you can get your first job.
00:20:34.006 --> 00:20:41.986
Yeah, no, I definitely think it's also if you go to the business side, you understand their problems.
00:20:42.976 --> 00:20:46.965
You can then obviously you've got the technical background, you can then add so much more value.
00:20:47.326 --> 00:20:49.215
And that's, it's that collaboration bit.
00:20:49.215 --> 00:20:53.175
It's obviously data teams and business teams are getting closer and closer.
00:20:53.596 --> 00:20:57.911
Not quite close enough yet, but I think that's the way the future's gonna be.
00:20:57.911 --> 00:21:01.520
And that's where the businesses that do that is gonna have that success.
00:21:02.361 --> 00:21:08.060
So then we, I guess with the organizations that I guess are moving towards like this more product thinking.
00:21:08.661 --> 00:21:12.915
do you feel leaders struggle to adapt their ways of working?
00:21:13.516 --> 00:21:16.846
So we still give out ourself an excuse saying that.
00:21:17.445 --> 00:21:19.576
Technology is enabler, right?
00:21:19.576 --> 00:21:22.276
So technology is not an enabler anymore.
00:21:22.276 --> 00:21:25.066
Technology is a new way of doing the business, right?
00:21:25.066 --> 00:21:28.915
So technology is creating this business loops, right?
00:21:28.915 --> 00:21:31.826
So that business can actually run really differently, right?
00:21:32.256 --> 00:21:35.695
So this is definitely the lack of literacy, what I'm saying, right?
00:21:35.695 --> 00:21:39.915
So we do need to have a massive literacy on both and business and the technical side.
00:21:40.256 --> 00:21:41.965
Because technical teams, when they.
00:21:42.181 --> 00:21:44.901
Let's say build data products using Databricks.
00:21:45.151 --> 00:21:48.330
Most of the teams are given excuse to business saying oh, it's okay.
00:21:48.330 --> 00:21:51.240
We can play the data product role.
00:21:51.431 --> 00:21:54.401
You don't need to come, which is gonna technically build it up.
00:21:54.641 --> 00:21:55.480
Never works.
00:21:55.580 --> 00:21:58.161
We tried it multiple times celebrate it.
00:21:58.161 --> 00:21:59.451
Multiple successes.
00:21:59.730 --> 00:22:04.721
Year later everything goes back to, to the same kind of behavioral, right?
00:22:04.721 --> 00:22:08.411
So behavioral means that the literacy doesn't stick to organization, right?
00:22:08.861 --> 00:22:11.496
So we do need to change step by step, right?
00:22:11.855 --> 00:22:16.096
So for for leaders who really need to drive this change, right?
00:22:16.096 --> 00:22:22.635
So from the technology side and business side, my advice is go back to education.
00:22:22.726 --> 00:22:23.806
Go back to education.
00:22:23.806 --> 00:22:29.411
But don't take, if you are technical leader you don't need to take another AI class.
00:22:29.461 --> 00:22:34.090
It's good enough if you watch some YouTube, me videos, educate you on the technology.
00:22:34.431 --> 00:22:37.790
But what classes you need to get, you need to get into communication.
00:22:37.790 --> 00:22:42.441
You need to get into collaboration, you need to actually negotiation, right?
00:22:42.441 --> 00:22:45.441
So how negotiate benefits with the business, right?
00:22:45.951 --> 00:22:50.510
So we do need the massive reeducation of technology and data people.
00:22:50.631 --> 00:23:01.006
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.
00:23:01.760 --> 00:23:05.330
Are not even teaching data strategy, which is shocking, right?
00:23:05.330 --> 00:23:07.280
So there's no classes on data strategy.
00:23:07.520 --> 00:23:13.836
So how you can be the leader of the future if you don't even know what role data is playing in organization.
00:23:14.435 --> 00:23:14.615
Yeah.
00:23:14.621 --> 00:23:20.695
And also because also, businesses are struggling to hire like data scientists, data engineers.
00:23:20.726 --> 00:23:29.605
'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.
00:23:30.326 --> 00:23:31.451
So do you think businesses.
00:23:32.050 --> 00:23:37.070
Should start investing more in this I guess like executive training at an earlier age.
00:23:37.070 --> 00:23:45.740
Because I've had guests one guest on here before and she had got executive coaching at her like an early age or not?
00:23:45.740 --> 00:23:46.280
No, sorry.
00:23:46.340 --> 00:23:48.080
Like in early in her career.
00:23:48.421 --> 00:23:51.931
And then that's ramped her up and now she's Chief data another company.
00:23:52.320 --> 00:23:57.691
So I think is it more being, just trying to get into that mindset of being more proactive, learning these skills that.
00:23:58.290 --> 00:24:00.976
Are gonna be helpful five, 10 years ahead.
00:24:01.576 --> 00:24:02.701
A absolutely right.
00:24:02.701 --> 00:24:05.851
And I would love actually to provide like an analogy, right?
00:24:06.181 --> 00:24:10.590
Because there are a lot of leadership development frameworks, right?
00:24:10.641 --> 00:24:12.050
You might know some of them.
00:24:12.050 --> 00:24:13.641
There are like a strength finder.
00:24:14.191 --> 00:24:16.221
There are working ingenious.
00:24:16.270 --> 00:24:18.760
So there is a framework called disc, right?
00:24:19.121 --> 00:24:23.290
All of this framework is actually look at the people behaviorals, right?
00:24:23.290 --> 00:24:27.201
And looking for example, who is actually excelling what, right?
00:24:27.201 --> 00:24:30.320
So some, somebody, people are really great in making connections.
00:24:30.320 --> 00:24:37.101
Some people are really great in making deep dive research and kind of understanding all facts and everything, right?
00:24:37.530 --> 00:24:41.971
But all of these methodologies, old methodologies, they, they do not talk about.
00:24:42.661 --> 00:24:44.550
What is changing when technology is around?
00:24:44.921 --> 00:24:55.800
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?
00:24:56.201 --> 00:24:59.141
And my topic was about what is the leader of the future, right?
00:24:59.560 --> 00:25:06.820
So I did ask my audience, which majority of executive officers, chief and transformation officers, I said, what is.
00:25:07.330 --> 00:25:09.851
The one dysfunction of the team, right?
00:25:10.090 --> 00:25:15.826
And everybody know from the old old leadership development the number one dysfunction is trust.
00:25:16.506 --> 00:25:20.895
And, but in all methodology, the trust is between people, right?
00:25:20.895 --> 00:25:25.846
Between your manager, between leadership and the employees of the company, right?
00:25:25.846 --> 00:25:31.695
So this is the old version, but now data and technology is a part of this, right?
00:25:31.695 --> 00:25:33.155
So it's do we trust data?
00:25:33.756 --> 00:25:36.276
Do we trust technology to make decisions for us?
00:25:36.276 --> 00:25:40.506
And when we had this conversation in this big room of senior executives.
00:25:41.355 --> 00:25:44.776
The reaction was like, oh my God, this is absolutely right.
00:25:44.826 --> 00:25:59.986
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.
00:26:00.036 --> 00:26:04.425
So when I speak about data ai literacy programs.
00:26:05.070 --> 00:26:07.111
This is not about training tools.
00:26:07.211 --> 00:26:13.480
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.
00:26:13.901 --> 00:26:15.760
No, it's not about understanding data.
00:26:16.030 --> 00:26:24.040
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?
00:26:24.040 --> 00:26:28.480
And how you should be working together so you can actually trust the data, right?
00:26:28.871 --> 00:26:29.980
So really different center.
00:26:30.580 --> 00:26:37.270
So what then would you say like the for the listeners, what skills that are becoming essential?
00:26:37.601 --> 00:26:39.461
That people should maybe focus more.
00:26:39.490 --> 00:26:41.080
'cause obviously it's changed now.
00:26:41.080 --> 00:26:42.580
Obviously there's so much, there's so much to learn, isn't there?
00:26:42.580 --> 00:26:44.560
With generative ai, it's all constantly evolved.
00:26:44.560 --> 00:26:50.351
But is there any other key skills that people should pay attention to for this?
00:26:50.351 --> 00:26:52.901
Like I guess more of a product driven environment.
00:26:53.500 --> 00:26:56.201
This all comes into soft skills, right?
00:26:56.201 --> 00:26:57.911
So this is your cultural agility.
00:26:58.721 --> 00:26:59.980
This is your curiosity.
00:27:00.580 --> 00:27:02.201
This is the skill to partner.
00:27:02.800 --> 00:27:11.721
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?
00:27:12.111 --> 00:27:16.351
Collaboration, up and down, listening to customer negotiation.
00:27:16.711 --> 00:27:21.181
So these are the skills which absolutely needed for the future, right?
00:27:21.181 --> 00:27:22.766
So I'm currently recommending again.
00:27:23.421 --> 00:27:27.371
I'm speaking up for people on traditional enterprises, right?
00:27:27.671 --> 00:27:42.000
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?
00:27:42.000 --> 00:27:43.320
So being a agile.
00:27:43.320 --> 00:27:45.776
So these are type of skills are much needed for the future.
00:27:46.375 --> 00:27:47.726
And I guess this is gonna be.
00:27:48.596 --> 00:27:51.266
A massive step for a lot of people in the tech world.
00:27:51.296 --> 00:27:54.355
'cause obviously traditionally they're really technically gifted.
00:27:54.726 --> 00:27:58.086
And this is gonna be coming outta their shell, isn't it really to get these soft skills?
00:27:58.115 --> 00:28:09.905
'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.
00:28:10.746 --> 00:28:10.955
Yeah.
00:28:10.955 --> 00:28:14.705
Like focused on your soft skills and I guess understanding problems now.
00:28:15.306 --> 00:28:17.256
Yeah, no, ab absolutely right.
00:28:17.256 --> 00:28:19.740
I think we are just in the beginning of.
00:28:20.340 --> 00:28:23.431
Like a major changes in a career pass, right?
00:28:23.431 --> 00:28:27.290
So it's not gonna be like a linear career pass anymore, right?
00:28:27.290 --> 00:28:29.631
So it's gonna be more matrix career pass.
00:28:29.941 --> 00:28:33.030
It's not gonna be differentiation or technical people.
00:28:33.030 --> 00:28:34.441
And this are business people, right?
00:28:34.441 --> 00:28:36.780
So it's gonna be the new cohort of people.
00:28:37.070 --> 00:28:44.131
Which actually understand both sides, the business and the technical sides, and can actually run the business more technically.
00:28:44.230 --> 00:28:45.911
We already have great examples.
00:28:45.911 --> 00:28:48.211
I'm just going back to me, at the Amazon, right?
00:28:48.211 --> 00:28:51.556
So these are the new generation of leaders who grew up.
00:28:52.175 --> 00:28:54.695
Running the technology type of business, right?
00:28:54.935 --> 00:29:04.576
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?
00:29:04.796 --> 00:29:06.826
We are gonna see the massive, massive change.
00:29:07.135 --> 00:29:09.145
So this technical people, we said, right?
00:29:09.306 --> 00:29:11.855
But business people also have a gap, right?
00:29:11.855 --> 00:29:12.941
So this is an example.
00:29:13.240 --> 00:29:14.171
A guy now.
00:29:14.435 --> 00:29:15.846
It's going so crazy.
00:29:15.846 --> 00:29:21.550
I'm talking to development teams and they have so many requests from business leaders to develop all kind of agents, right?
00:29:22.000 --> 00:29:27.851
But when we talk to business people and I say, okay, how are you gonna evaluate your digital employee?
00:29:28.270 --> 00:29:31.901
So it's interesting response because it's like, what do you mean digital employee?
00:29:32.171 --> 00:29:36.431
I was like, this agent is actually gonna make your sales now.
00:29:37.111 --> 00:29:38.461
You are accountable, right?
00:29:38.621 --> 00:29:45.641
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?
00:29:45.641 --> 00:29:46.330
I was like no.
00:29:46.330 --> 00:29:50.141
This is your job because this is your digital employee, right?
00:29:50.441 --> 00:29:58.560
So we also need different type of skills on a, on the business side to understand how you manage technology as your new employees.
00:29:59.161 --> 00:30:06.510
So would you say, obviously the rise of ai, is it accelerating or complicating the move towards more product centric roles?
00:30:07.320 --> 00:30:08.250
It is accelerate.
00:30:08.340 --> 00:30:10.111
It's accelerate a hundred percent.
00:30:10.161 --> 00:30:12.181
Ben as I'm saying, is people wake up.
00:30:12.181 --> 00:30:16.740
It's like you, you have to invest in yourself and get this type of skills right?
00:30:16.740 --> 00:30:20.780
Start from, reading internet, watch YouTube videos, buy some books.
00:30:20.780 --> 00:30:20.840
I.
00:30:21.371 --> 00:30:32.391
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?
00:30:32.391 --> 00:30:34.671
You have to invest in your education.
00:30:35.361 --> 00:30:37.431
It's gonna be like, must have for the future.
00:30:37.560 --> 00:30:44.016
This is the way for you to to actually, stand up around the crowd and get your leadership spot.
00:30:44.615 --> 00:30:55.385
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?
00:30:55.986 --> 00:30:58.306
So the first step, what I would recommend is.
00:30:59.070 --> 00:31:01.961
People is asking I want to, be in like a product.
00:31:01.990 --> 00:31:04.090
It's like you, you have some management skill.
00:31:04.330 --> 00:31:04.961
Skill, right?
00:31:05.260 --> 00:31:07.901
So people are always saying yes, I want to be a manager, okay?
00:31:07.901 --> 00:31:11.050
Before you want to be manager, be the leader first, right?
00:31:11.050 --> 00:31:11.740
So what does it mean?
00:31:11.740 --> 00:31:12.520
Be the leader first.
00:31:12.520 --> 00:31:15.401
Nobody's pre preventing you to be the leader, right?
00:31:15.851 --> 00:31:17.351
Do the first exercise.
00:31:17.381 --> 00:31:18.671
This is my recommendation.
00:31:19.090 --> 00:31:19.865
Go to your customer.
00:31:20.465 --> 00:31:26.766
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.
00:31:26.816 --> 00:31:28.661
But go into discovery process.
00:31:29.391 --> 00:31:33.681
For example, ask your customer to take you to the sales call, right?
00:31:33.681 --> 00:31:36.621
Or to the sales trip, or go to the factory.
00:31:37.070 --> 00:31:40.671
So start understanding better the customer and the business, right?
00:31:40.671 --> 00:31:41.601
So this is doable.
00:31:42.050 --> 00:31:43.520
You can do it, right?
00:31:43.851 --> 00:31:48.980
So if customer's gonna say no, I'm not interested to take you, you can still do it, right?
00:31:48.980 --> 00:31:52.441
So go to the store, see how the business is working right?
00:31:52.441 --> 00:31:55.651
Find a way to learn more about the need of your business.
00:31:56.250 --> 00:31:57.086
Yeah, no, definitely.
00:31:57.086 --> 00:31:58.016
I think it's.
00:31:58.256 --> 00:31:58.901
First step.
00:31:58.901 --> 00:31:59.351
Yes.
00:31:59.965 --> 00:32:17.990
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?
00:32:18.590 --> 00:32:20.605
Absolutely no ab, absolutely.
00:32:20.605 --> 00:32:23.665
So definitely be more curious, right?
00:32:23.665 --> 00:32:24.655
But do not.
00:32:25.135 --> 00:32:29.066
So what I see is technical people, they, they all including myself, right?
00:32:29.306 --> 00:32:31.855
So we all excited about solutions.
00:32:31.915 --> 00:32:33.836
We are solutions people, right?
00:32:34.181 --> 00:32:38.980
So we see the first answer and we immediately like, oh, I know how to solve it.
00:32:39.030 --> 00:32:39.330
No.
00:32:39.361 --> 00:32:40.111
Stop here.
00:32:40.621 --> 00:32:43.651
So you have to ask at least a hundred questions.
00:32:44.111 --> 00:32:48.490
Hold yourself on any solution, hold yourself, forget about solution, right?
00:32:48.971 --> 00:32:52.211
Hand have more deeper conversation, right?
00:32:52.211 --> 00:32:58.931
So so you can actually understand what is the real problem, because the first sentence you might hear.
00:32:59.306 --> 00:33:00.776
It's not gonna be the real problem.
00:33:01.326 --> 00:33:04.625
And maybe the fifth sentence you're gonna hear, it's not gonna be real problem.
00:33:04.665 --> 00:33:11.066
We have to have deeper conversations and maybe more, informal conversations, right?
00:33:11.066 --> 00:33:16.911
So I'll give you in my examples when I was, going on a sales trip, like in in the pharma for example, right?
00:33:17.131 --> 00:33:18.931
We visit some doctors, right?
00:33:18.931 --> 00:33:22.381
And I can absorb how the sales conversation is happening, right?
00:33:22.780 --> 00:33:30.050
So then we, we live in the office and salesperson is actually supposed to enter information about the visa to the system, right?
00:33:30.451 --> 00:33:36.000
And I'm seeing how this person is struggling to do this because there is no internet connection, right?
00:33:36.000 --> 00:33:38.625
So if I don't, don't see it, right?
00:33:38.625 --> 00:33:43.165
So I might not, I don't understand why we have missing fields.
00:33:43.766 --> 00:33:47.786
We have missing fields because people simply didn't have internet connection in the location.
00:33:47.836 --> 00:33:54.346
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?
00:33:54.526 --> 00:33:54.945
So you.
00:33:55.546 --> 00:33:59.746
So obviously you've mentioned a couple of companies like Meta and about obviously getting it right.
00:34:00.135 --> 00:34:06.951
So when companies are getting product thinking right in data ai, what kind of business value shows up first?
00:34:07.550 --> 00:34:14.490
So I think first what the business, first of all, you can definitely see much better agility and and efficiency, right?
00:34:14.791 --> 00:34:19.320
So you are gonna be able to de, to deliver the value much quicker, right?
00:34:19.590 --> 00:34:26.161
Because the product mindset means that you develop, you deve you you develop the value in incremental steps.
00:34:26.791 --> 00:34:30.201
So it's like you almost because it's a like Iranian cycle, right?
00:34:30.201 --> 00:34:34.251
So this is not the project, oh wait, several months and this is your answer, right?
00:34:34.550 --> 00:34:39.621
You are constantly gonna be able to work with your customer and create incremental value, right?
00:34:39.621 --> 00:34:41.510
So this is the first benefit, right?
00:34:41.510 --> 00:34:43.851
So the value is gonna start showing right away.
00:34:44.675 --> 00:34:52.596
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.
00:34:52.626 --> 00:34:54.215
It's not gonna work in my environment.
00:34:54.405 --> 00:34:56.206
So this is the first benefit you're gonna see.
00:34:56.806 --> 00:35:01.201
Okay, so do you feel, obviously these, with businesses, building teams, do you think.
00:35:01.800 --> 00:35:03.030
Obviously I've called you the glue.
00:35:03.090 --> 00:35:07.021
Should they be hiring more people that can act as a glue within a business?
00:35:07.021 --> 00:35:11.951
So then they bring the business and tech closer together.
00:35:12.550 --> 00:35:13.076
Y Yes.
00:35:13.376 --> 00:35:13.976
Abso absolutely.
00:35:13.976 --> 00:35:21.545
We are already seeing right now that like having the business partnership is a requirement for all leadership type of roles, right?
00:35:21.545 --> 00:35:24.315
So do you want to be the technology director?
00:35:24.405 --> 00:35:26.266
Do you want to be the chief data officer?
00:35:26.266 --> 00:35:27.166
The first skill is.
00:35:27.920 --> 00:35:30.561
That you actually have to be able to build the business case.
00:35:30.561 --> 00:35:33.530
You have to be the partner, you have to understand the business, right?
00:35:33.891 --> 00:35:38.431
So these are the skills which are always, gonna be the skills for the senior leaders.
00:35:38.431 --> 00:35:40.721
Also I tell like a middle.
00:35:41.045 --> 00:35:43.425
Me, middle level man managers.
00:35:43.425 --> 00:35:46.695
This is a big cohort of people who come to me for career advice.
00:35:47.065 --> 00:35:51.916
I'm saying for you to grow up you're not gonna grow by IT and more tools, right?
00:35:51.916 --> 00:35:56.865
So you're gonna grow by it and leadership skills, better understanding of the business, right?
00:35:56.865 --> 00:35:59.266
So this is absolute requirement for you to grow.
00:35:59.865 --> 00:36:00.706
Yeah, no, definitely.
00:36:00.706 --> 00:36:06.195
And I think it's gonna be, I think as AI matures, it's gonna get deeper and deeper, isn't it?
00:36:06.195 --> 00:36:08.235
Into every area of the business.
00:36:08.266 --> 00:36:10.065
It's, and that's just the way it's gonna get.
00:36:10.065 --> 00:36:12.081
So if you've got that, the knack.
00:36:12.681 --> 00:36:14.771
like you said to keep asking questions why?
00:36:14.771 --> 00:36:20.351
Getting deeper into the problem, you are gonna be in a better position to add value to the company.
00:36:20.951 --> 00:36:24.851
Yeah, no, a abs absolutely, you go, you're gonna be like in a better position.
00:36:24.851 --> 00:36:28.731
And plus again, understanding how the business is working, right?
00:36:29.001 --> 00:36:32.971
So I think I'm, maybe I, I feel I'm lucky in my career, right?
00:36:32.971 --> 00:36:38.880
Because one of the major role when, when I actually, came to United States back in like in 2000.
00:36:39.210 --> 00:36:45.911
So I was hired by the Wall Street Journal, Dow Jones to establish basically data and analytics practice, right?
00:36:45.911 --> 00:36:47.471
Which didn't exist for digital.
00:36:47.840 --> 00:36:51.231
And I had the chance to work in a digital publishing.
00:36:51.856 --> 00:36:55.396
Before even digital transformation started, right?
00:36:55.815 --> 00:37:01.155
So this gave me opportunity to better understand how to work in this product model, right?
00:37:01.155 --> 00:37:03.496
So we did not call it the product model, right?
00:37:03.916 --> 00:37:06.885
But this almost an Amazon type of requirement, right?
00:37:07.195 --> 00:37:11.646
I remember one of the program I was driving is personalization, right?
00:37:11.646 --> 00:37:12.666
So like everybody.
00:37:13.115 --> 00:37:22.976
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?
00:37:22.976 --> 00:37:24.945
So you're going to get longer time value, right?
00:37:24.945 --> 00:37:26.686
So this is like a hypothesis, right?
00:37:27.346 --> 00:37:29.496
There are two ways to look into this, right?
00:37:29.646 --> 00:37:33.246
So one way is like how we technically prove it, right?
00:37:33.246 --> 00:37:37.056
Can we actually prove that personalization is driving more usage, right?
00:37:37.356 --> 00:37:41.561
But then the business side comes is this gonna convert to p and l?
00:37:42.085 --> 00:37:51.865
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.
00:37:52.195 --> 00:37:57.206
So this type of realization is gonna convert to p and l and this one is not gonna convert.
00:37:57.545 --> 00:38:01.146
And you are gonna see the room of people like really senior chief product officer.
00:38:02.061 --> 00:38:05.121
She was like, oh my God, Juliana, you changed my mind.
00:38:05.121 --> 00:38:06.530
I did not even think this way.
00:38:06.530 --> 00:38:12.590
That this presentation is not gonna convert to p and l, which means that we don't want to invest, right?
00:38:12.590 --> 00:38:17.090
So technically you can think, oh yeah, let's do everything gonna be personalized, right?
00:38:17.391 --> 00:38:25.550
But if it's not gonna convert to your pro profit, basically, or your additional customer growth, then it does make sense to do.
00:38:25.550 --> 00:38:26.331
And I would tell you.
00:38:27.215 --> 00:38:28.195
It's rarely.
00:38:28.286 --> 00:38:35.295
You can see the people especially like a mid-level, who are crunching all these numbers are thinking this way.
00:38:35.775 --> 00:38:38.896
So they need to start more about how the business is made.
00:38:39.315 --> 00:38:44.246
What is, what is our profit, what is our loss, what is operational efficiency?
00:38:44.545 --> 00:38:48.081
To better connect all the science projects to real business.
00:38:48.681 --> 00:38:53.885
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.
00:38:54.880 --> 00:38:58.960
Got to point in their career and they've done like a sideways move to more into the business.
00:38:59.170 --> 00:39:02.411
And then further on in their career, it's really benefited them.
00:39:02.721 --> 00:39:07.840
So I think it's, there's a lot of different ways you can go in the future, especially for leaders.
00:39:07.840 --> 00:39:07.985
You could see.
00:39:08.826 --> 00:39:18.996
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.
00:39:18.996 --> 00:39:28.856
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.
00:39:29.811 --> 00:39:30.851
A hundred percent.
00:39:30.851 --> 00:39:33.911
And actually some companies are doing this type of rotation.
00:39:33.911 --> 00:39:37.280
So for example, Johnson, actually we did quite of rotations, right?
00:39:37.280 --> 00:39:40.010
So like somebody from my team, like a business analyst.
00:39:40.601 --> 00:39:43.931
We sent her to the marketing organization for six months, right?
00:39:44.260 --> 00:39:45.860
And and she didn't like it.
00:39:45.860 --> 00:39:47.751
She was like, I don't want to be in marketing.
00:39:47.811 --> 00:39:50.541
No, go and learn what marketing is doing, right?
00:39:50.541 --> 00:39:52.190
Because, we working a lot with brands.
00:39:52.190 --> 00:39:54.081
You need to understand the brand structure, right?
00:39:54.570 --> 00:39:59.186
So this, these are like you, you basically need to give this type of skills to rat, rotate people.
00:39:59.186 --> 00:39:59.815
And the same.
00:40:00.260 --> 00:40:04.070
Take somebody from the business team and put them into technology organization.
00:40:04.070 --> 00:40:10.101
So for me is actually just maybe, how I came to to my role as a chief data ai.
00:40:10.490 --> 00:40:12.110
I started from the business, right?
00:40:12.110 --> 00:40:17.141
So when I was working in a publishing, I was in a product organization working directly with the business.
00:40:17.541 --> 00:40:21.376
And my, my fewest technical role, it's actually happened to be.
00:40:21.936 --> 00:40:23.405
When I joined Johnson, right?
00:40:23.405 --> 00:40:26.376
And this is where I started more being on the technical side, right?
00:40:26.715 --> 00:40:36.315
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?
00:40:36.596 --> 00:40:37.976
And we need both ways.
00:40:38.155 --> 00:40:42.880
We need people actually both ways to take this type of steps because there are a lot of really.
00:40:43.481 --> 00:40:48.911
Super smart and the people who are really understand the future of technology on the business side.
00:40:49.371 --> 00:40:54.431
But they feel that going back work for technology, it's almost like a step back.
00:40:54.911 --> 00:40:56.170
No, it's not the step back.
00:40:56.201 --> 00:40:59.081
You can be one of the best technical leaders, right?
00:40:59.081 --> 00:41:01.240
So if you come from the business to the technology.
00:41:01.840 --> 00:41:02.050
Yeah.
00:41:02.050 --> 00:41:02.590
No, definitely.
00:41:02.590 --> 00:41:02.860
Brilliant.
00:41:03.101 --> 00:41:03.670
Okay, brilliant.
00:41:03.670 --> 00:41:07.715
I really appreciate your insights Anna, I guess any I love your passion, by the way.
00:41:07.795 --> 00:41:10.585
Any, any final words you'd like to leave with the listeners?
00:41:11.186 --> 00:41:16.175
So I think my final word is is definitely I think I already said enough, is, wake up people.
00:41:16.565 --> 00:41:21.635
So unfortunately that AI is is gonna be, yeah, it's gonna put pressure on us.
00:41:21.635 --> 00:41:24.175
It's putting pressure on people who build ai.
00:41:24.505 --> 00:41:26.456
Gonna feel it first, right?
00:41:26.456 --> 00:41:28.445
Wake up invest in yourself.
00:41:28.876 --> 00:41:32.085
Nobody's gonna help you unless you help yourself, right?
00:41:32.085 --> 00:41:34.246
So there are so many resources available.
00:41:34.246 --> 00:41:36.376
As I mentioned, there are wonderful communities.
00:41:36.376 --> 00:41:39.315
There is a YouTube, there is a substack, right?
00:41:39.346 --> 00:41:40.576
But take care of yourself.
00:41:40.990 --> 00:41:44.291
So this is gonna be my last message because I love you so much.
00:41:44.661 --> 00:41:48.911
I want everybody in data analytics side to be successful, but please wake up.
00:41:49.510 --> 00:41:49.840
Brilliant.
00:41:49.900 --> 00:41:51.161
Thank you for your conversation.
00:41:51.760 --> 00:41:52.751
Thank you so much, Ben.
00:41:53.351 --> 00:41:56.050
And I guess for thank you for listeners for listening.
00:41:56.110 --> 00:42:01.990
If you want more conversations with data experts, please make sure you follow or subscribe to Data Analytics Chat.
00:42:02.351 --> 00:42:05.621
And so don't miss future episodes and we'll see you next time.
