A One-Stop Data Shop: The Lego Group’s Anders Butzbach Christensen
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Today, we're airing an episode produced by our friends at the Modern CTO Podcast, who were kind enough to have me on recently as a guest. We talked about the rise of generative AI, what it means to be successful with technology, and some considerations for leaders to think about as they shepherd technology implementation efforts. Find the Modern CTO Podcast on Apple Podcast, Spotify, or wherever you get your podcast.

Our guest often uses Lego as an analogy for how organizations can build up solutions with data. But today, find out how Lego itself builds data components that connect as easily as its bricks. I'm Anders Gutbart-Christensen from the Lego Group, and you're listening to Me, Myself, and AI. Welcome to Me, Myself, and AI, a podcast on artificial intelligence and business. Each episode, we introduce you to someone innovating with AI. I'm Sam Ransbotham, professor of analytics at Boston College.

I'm also the AI and Business Strategy Guest Editor at MIT Sloan Management Review.

And I'm Shervan Kodubande, senior partner with BCG and one of the leaders of our AI business. Together, MIT SMR and BCG have been researching and publishing on AI since 2017, interviewing hundreds of practitioners and surveying thousands of companies on what it takes to build and to deploy and scale AI capabilities and really transform the way organizations operate. Today, Shervan and I are excited to be joined by Anders Christensen.

He's the head of data engineering at LEGO Group. Anders, thanks for taking the time to join us. Welcome. Thanks for having me, Sam. First, tell us a little bit about what you do at LEGO Group. I'm heading up the data engineering department within the LEGO Group. We currently consist of three large global product teams within my area.

Two of the teams focus on self-service, enabling the organization to make data-driven decisions. And the last one is building a customer-free 60 view that allows us to build personalized experience. Let's start with the first one. What does that mean to be self-service?

So a little less than two years ago, we started out our exploration of digital transformation within the legal group. And for us, that basically meant that we needed to do a lot of upskilling and we need to focus on having the right competencies and teams and ways of working within the organization. So basically building the right digital foundation.

And in order for us to enable the four customer groups that we have, the consumers, the shoppers, the partners, and our colleagues, we needed to make sure that they had all the right tooling to do so. And a huge part of doing that is self-service, enabling them to make data-driven decisions. So what we did was that we took this centralized data platform that almost all large companies have today, and then we made that available for everyone to use, basically. And that's self-service.

So what does that look like? If I sit down tomorrow with Lego Group and they won't let me play with the bricks, how do I play with the data? What it basically means is that it's super easy for the product teams around the organization to come with their data, bring it into the platform, and then to play around with the data, transform it in whatever way they want to, and then expose it for whatever use they have. That might be for analytical purposes, but it could also be for data science purposes, etc.,

Making that journey as easy as possible and available to all types of skill sets within the organization is what it looks like. Right now, it's used for basically everything. That's all types of data coming from our websites flowing into the platform. And then we look at how the customers behave on the website and then provide the best possible recommendation experience to them.

That's one thing, but we also use it for forecasting. For example, we have a lot of different data that's coming in from our demand planners across the globe that gets all built into a beautiful data product that's used for creating this forecasting model. Anders, what I'm hearing is data platforms and data engineering, but I'm also hearing data science in there and recommendations and demand planning. Does your group do both?

The way that we are organized within the organization is called the Data Office. We do have a data science COE. They focus on a lot of the data science work, but we also do use data science within my area.

But the way that we utilize it is for enablement. So this could be how do we build data that allows people to innovate faster. In our use case, that is enabling synthetic data on the platform. So whenever someone comes along and wants to utilize a data set that potentially contains a personal identifiable information, they need a legal approval right.

And that is because we need to take care of our customers' data. So what we do is that we give them a synthetic dataset that's generated based on the schema, and then they can get started right away. It has zero similarities towards the original dataset, but it gives them the same output. That's a way for us to make that data science team innovate way faster than they originally would, because it can take months before you get that legal approval. Yes, yes. Synthetic data is really...

Really quite brilliant, Sam, if you remember when we had the conversation with Humana, yes, as well as I think with Moderna, synthetic data for purpose of experimentation. Just quite fascinating. Anders, you mentioned data product a few times. It's been a pretty big buzzword for about a decade, at least that I've heard. A lot of organizations we speak to

are building data products, how would you define data product? What would constitute a data product? What are components of a data product? If we first at all look at the definition of a data product, then for us, a data product is a way of thinking it's a mindset, right? So we used to think of data as a byproduct, something that is a part of a product out in the business or something like that. But to us, it's really important to put focus on data as a product. And that's why we call it a data product.

If you're very technical, it could be a database that consists of a lot of data sets with a lot of different data attributes within it. In theory, it's a lot of different inputs that goes into one product, right? But for us, it's really the mindset. It is the fact that that data is no longer just something we use as a part of our daily work. It is how we make decisions. It's how we create value.

When I think about a product, a product would have a product roadmap and it would evolve over time and you would innovate on a product. Is that also what's going on with your data product? Definitely. We view it as any other product. It's no different than another software product we build. And a good example is if we look at the 360 view that we're building within my area, there's a lot of different data sets that goes into that data product. I think we have

four or five different IDs across the legal group that we need to stitch together. In order to build this 360 view, we need to bring in those IDs

And in order for us to provide value to, let's say, that we want to build a personalized email campaign or something like that, the way that we evolve this product is by looking at what is the minimum viable product that we need to build? What data sets do we need to bring in, attributes, et cetera? And then we can basically evolve from there on. You don't need all five IDs in order to deliver that value. You could actually bring in maybe one or two and start building those personalized campaigns. The beauty of what you're saying is, in addition to building those

intelligence products on top of your data products, you're not just building it one time because the

rest of the organization can also use a lot of those assets in the future. And this is quite important because a lot of times you see in an organization, maybe they build a personalization engine specifically for what they want. And all of that data pipeline and data engineering then goes to waste. And somebody in the next room would do the same thing. And they'd go all the way back to scratch from the raw data again. And I think what you're saying is,

You've created the commonality or the common layers of data, and then folks just use what they need when they need, but it's not duplicative. You're spot on. And that actually talks a lot into reusability of data products across the organization. And then when you have onboarded all this data into the platform and build all these data products, it's also really important that you make it discoverable to the organization so that others can utilize it for other purposes and to create other types of value, right?

What we do is that we have a discoverability tool where you can go in and look at the different data sets. Of course, there is private data sets because if it contains, let's say, personal identifiable data, then it has to be private and you need to request access. But there could also be data products that doesn't need to be private and can be publicly available for people to tap into.

And it's really important for us to make it discoverable on the platform so that you can start stitching the data together and create new types of value. And you don't have to reuse or re-ingest things like that, as we have seen across the different organizations for years. Instead of being very restrictive about how you build these types of platforms, you need to think of it as empowering the product teams to get as much freedom as humanly possible.

It's sort of like the actual physical Lego blocks, right? Quick digression. We probably have 300 Lego sets that my kids have built and they serve their original purpose following the instruction and then they end up creating other monstrosities or cool creations. So I could totally see that analogy. Just thinking about that, we have so many people who come on our podcast and mention, oh, it's like a building block and they always make an analogy about

to Lego, of course. And Lego itself is using these building blocks and their own analogy. Yes. Couldn't agree more. But I think it's also really important to say that what we do is that Lego is a physical toy brand, right? We build physical toys and

And what we do in digital space is that we enable better experiences for those physical products. It's just to enhance that experience for our users and to make the experience of buying products and interacting with products way more fun for our consumers. And I also think that that talks very much into how you can use things like machine learning or AI to then make sure that we do it in a responsible way, right?

So what we're also looking at is how can we utilize machine learning to audit the platform and make sure that we don't have data lying around forever, but it actually gets cleaned up. And at the same time, also looking at PII detection, how do we make sure that our product teams actually knows if they have PII data within the platform or not?

People can't see this, but in the background, Ender's office is filled with these toys. I'm quite jealous because my background is pretty plain here. What kinds of things are people building with your data bricks? We actually do have a lot of cool experiences that got published not too long ago. We did the launch of LEGO Super Mario, I think, last year, and that was a huge success. That is a toy that's not only physical, but it's actually also interactable through these digital experiences. And that's something that was a huge success to us.

When I hear the data product and

various usages of it. I think within the enterprise, you mentioned planning and optimization. And I could imagine as a customer interacting with Lego websites or instructions or purchasing recommendation engines and things like that. But you also mentioned something around the actual experience of building, like doing play. Is there also products that enable players

better play or different play experiences? Around three or four years ago, we released a mobile application where you could use AI to do different play experiences. But that required that you needed to have a phone or an iPad in order to interact with the experience. I think for us, it is combining it like we did in Super Mario, where it was within the toy that made a big difference.

Andres, this is quite fascinating what you've shared with us. And I'm sure many of our listeners and folks that wear your hat in other organizations are wondering if there is a secret sauce or if there's a recipe. So do you want to share with them and us, what does it take to actually build what you guys are building?

I think one of the really important things for us had been to empower our product teams to actually build new products and take ownership of them. And the way that we did that was by establishing three different pillars within the company. We wanted to be product-led, architecture-led, and then engineering-led. So what it means is that being product-led is that we actually recognize that the different teams are products themselves. We no longer have these projects that has a start and end date, but it's products that are evolving.

Then the second one coming up is actually looking at the architecture itself, making sure that the products we build like data platforms, it's not only something that solves a short-term need, but also a long-term need. And that we make sure that we don't need to redo things over time. I think that way of thinking has been really important for us and it also sets the direction for our teams.

Then the engineering lead for us is a lot around the way that we deliver our technologies and make them available across the company, but also outside the company. We actually strongly believe that the engineers are the specialists. So when a product manager or management comes along, they of course set the scene by talking about the why and what we do things. But the team is solely responsible for the how.

And they need to figure out, you know, when we talk about the synthetic data, okay, if we need to work with synthetic data, then how do we do that in the best way? What does a POC look like? What does a minimum viable product look like? And how do we evolve that over time? They need to figure that out and tell us. And then we, of course, look at things like how can we deliver value as soon as possible? And that's getting those POCs out there tested and make sure that they deliver value to the organization.

For us, the engineering is really important and one of the key reasons why we have had the success we have today. I think we sometimes also need to remember that we hire in these skilled people because they are the specialists and they are the best of the best, right? So empowering the teams is just a key thing in order to achieve great success. How did you end up in this role? I mean, our show is me, myself and AI. I'm curious how you actually ended up involved in all these things.

Yeah, I think that's a longer story. I have an educational background within computer science and in web development, and I actually started out as a consultant seven years ago. I did mobile applications and websites and moved into project management of the clients that we built those products for.

And then I think as so many other people in Denmark, we dream about working for a Lego ride. We've all played with the bricks and we dream about working for them. It's not just Denmark. You're absolutely right. And we also see that. But I totally by coincidence saw this job ad that said something about senior product owner of Big Data. And I probably didn't know what Big Data was, but the senior product owner I knew because we were also working with agile within the consultancy agency. So I applied for the position without knowing too much about what the domain was.

And then after four or five rounds of interviews, I, by some coincidence, managed to get the role. I started out in the Lego group, had our recommendation team for Lego.com and Lego Life, which is our social app. Then I did image sharing, moderation, and tagging to ensure that we have safe content within our platforms.

Then after half a year, I got the job to digitalize our supply chain, building domain forecasts, et cetera, which was a huge project. And I didn't know anything about supply chain, forecasting or anything. And then we decided to accelerate our digital transformation. And then I became the head of data engineering. When we started out, there was not a lot of companies that had tried that before. So we needed to do a lot of discovery and research to see how does other companies do that and then try to stitch everything together

And that basically leads us to where we are today, to where I am. And I think the curiosity around data is what kept me here. That's great. Okay, so you've set me up for this. Many of the things you're talking about with your digital transformation seem like you're fairly mature in them at this point. You mentioned, for example, the synthetic data that you're working on and the PII identification. What's next? What kinds of things are you and the LEGO Group headed towards?

There is a huge journey ahead of us. There is, of course, a lot of product teams that use the platform today. I think we have 600 plus data products on our platform, but there are still a lot of digital product teams that are not using it, especially through self-service. There's a long journey ahead of us to build up the ambassadors around the organization and make it as easy as humanly possible to build those data products that create a lot of value. You've been on this journey for quite some time and have a lot of

lessons learned and experiences. If you were to maybe fast forward, what would be ideal? If you sit down and say, wouldn't it be cool if 10 years from now when organizations are talking about data products, they could have XYZ? What is XYZ 10 years from now?

What I dream about is that all data producers expose their data into this platform so that it's available in the discovery tool. And I dream about a one-stop shop. And then it's really easy, no matter what skillset you have, to stitch that together into a data product. And for me, that goes for technical teams building transformation scripts in Python or another programming language to business people who goes into a drag and drop tool, and then suddenly it's available in a reporting tool.

So what I dream about is this one-stop shop for everyone across the organization, enabling them to become data-driven. And if I look ahead, that's where we are in hopefully 14 years.

You know, Shervin, as we step back, we've heard from a lot of people who are expressing some similar types of things, but in different domains. Obviously, not everyone is making children's toys or toys for grownups, as Shervin just mentioned. He still plays with Lego. But we've seen people in healthcare. We've seen them, Zyad Obermeyer, they're building a nightingale. They're building an infrastructure to allow people to get access to the data and use things. I think we're seeing this

That seems like it's kind of exciting. Is that where we're headed? Is every person then going to become able to do all these things on their own? I truly hope so. And I think like we're seeing in a lot of places right now, AI is moving forward and Google is not Google anymore. Now you have chat GPT coming up and there's just a lot of technologies that we couldn't even imagine five years ago. They are now publicly available in the hands of normal people.

First of all, this is quite fascinating and thank you for sharing. We're going to move to another segment, which should be just ask you like five rapid fire questions. And you give us the first thing that comes to your mind. Go ahead. All right. Let me actually get the questions. Okay. What is your proudest AI moment? Or let me generalize data moment.

One of my proudest moments with data was when we actually moved from having a lot of different types of data coming in with different types of data qualities that we couldn't stitch together. It didn't create the value we needed because they just didn't do things in the right way. So what we did was that we built a tool that educated the data producers on how to actually create good, high quality data products. And that was a huge success. And I think one of the key reasons behind that data product becoming a success

What worries you about AI? The worry is not that it's going to take over our jobs. I think the worry is that we unfortunately never can stop learning, right? That's very good. That's very true. It's also the same thing you said about you're never done with the data. What's your favorite activity that involves no technology? For me, it's physical activity. When I get off work after many hours, I need to do something that takes my mind off data and tech.

What was the first career that you wanted? What did you want to be when you grew up? First off, I actually wanted to be a lawyer, which I found out wasn't for me. I have an aunt that is a lawyer and I borrowed one of her school books and I looked at it and I found out that there's way too many laws. So I gave up on that fairly quickly. And then I wanted to be a trader. And then I found out that that requires looking at stocks on a screen for a long time. And that wasn't for me either. And then I moved into tech.

and building different types of applications. And I think what I really found cool about IT is that you can build products that creates a lot of value, a lot of revenue without having to buy a lot of things. You don't need a physical product. And I think that was something that really got me into IT. What's your greatest wish for AI in the future? My greatest wish is that it will make the world a better place.

And I'll leave it there because that means that can be done in different ways. You're not going to tell us how. You're going to hold that for the next time we talk to you, I guess. Exactly. Well, I think what's particularly interesting about today's discussion is a lot of the people that Trevor and I talk to talk about Legos as building blocks. And they make an analogy of the things that they're doing in their organization are, oh, we're building these Legos so that people can build data.

And what they don't realize is they think they're talking about Lego bricks, but they're actually talking about the way that Lego approaches data. I think that's pretty fascinating. And I think that's the kind of thing that a lot of people can learn from. Thanks for taking the time to talk with us and appreciate you sharing this. Yeah, thanks, Andrew. This has been great. Thanks for listening. Next time, we'll talk with Rathi Murthy, CTO and President of Expedia Product and Technology. Please join us.

Thanks for listening to Me, Myself, and AI. We believe, like you, that the conversation about AI implementation doesn't start and stop with this podcast. That's why we've created a group on LinkedIn specifically for listeners like you. It's called AI for Leaders. And if you join us, you can chat with show creators and hosts, ask your own questions, share your insights, and learn more about AI.

and gain access to valuable resources about AI implementation from MIT SMR and BCG, you can access it by visiting mitsmr.com forward slash AI for Leaders. We'll put that link in the show notes and we hope to see you there.