A One-Stop Data Shop: The Lego Group’s Anders Butzbach Christensen

**Sam Ransbotham** (0:02)
Our guests often use 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.

**Anders Butzbach Christensen** (0:16)
I'm Anders Butzbach Christensen from the Lego Group, and you're listening to Me, Myself, and AI.

**Sam Ransbotham** (0:23)
Welcome to Me, Myself, and AI, a podcast on artificial intelligence in 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.

**Shervin Khodabandeh** (0:41)
And I'm Shervin Khodabandeh, 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.

**Sam Ransbotham** (1:08)
Today's Shervin 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.

**Anders Butzbach Christensen** (1:16)
Thanks for having me, Sam.

**Sam Ransbotham** (1:18)
First, tell us a little bit about what you do at Lego Group.

**Anders Butzbach Christensen** (1:22)
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.

**Sam Ransbotham** (1:41)
Let's start with the first one. What does that mean to be self-service?

**Anders Butzbach Christensen** (1:44)
So a little less than two years ago, we started out our exploration of digital transformation within the Lego Group. And for us, that basically meant that we needed to do a lot of upskilling and we needed 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 as a 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.

**Sam Ransbotham** (2:28)
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?

**Anders Butzbach Christensen** (2:37)
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 would also be for data science purposes, et cetera.
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 sets 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.

**Shervin Khodabandeh** (3:30)
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?

**Anders Butzbach Christensen** (3:41)
The way that we are organized within the organization is called the data office. We do have a data science theory. 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.