It started as an Experiment, now Data Literacy is a Foundational Piece in Digital Transformation

In the Fall of 2020, a small group of colleagues got together in a 4-day long design sprint workshop and the result was to initiate a couple of experiments to uncover if the organization had any interest in Data Literacy. Read on to see how we started out, what we learned and where we are today.

What is Data Literacy?

Before we dive into how it all started, let’s look at how we define Data Literacy as a term or practice. When looking up Data Literacy online, one can get many definitions which are almost similar. We have chosen to define Data Literacy as:

The ability to read, understand, create and communicate data as information

It is very important to understand that being or striving to become a data driven organization is a lot more than mastering certain technical tools. We all need to understand that data is the fuel of a digitalization and if we do not treat data as a first-class citizen, we cannot expect to get the most value out of running data through glamorous and advanced tools.

Therefore, to enable an organization to get most value out of data, it is important to focus on the level of data literacy across the entire workforce. We do not aim for everyone to become super star data scientists and develop advanced algorithms, but we do aim for everyone to have a basic foundational understanding of how they can apply a data mindset and skills to their everyday job. This applies to all of us at the LEGO Group, whether we produce or enable data products or make decisions based on data presented to us.

Therefore, we all need to have the right:

Skills, Knowledge, Attitudes & Behaviours

Imagine that a colleague has spent numerous hours to collect, cleanse and display data in a report, but the receivers of the report are then unable to make any sense of what is being presented to them. This is not to state that the collected data is wrong or that the report developer did a bad job. But it could be that either the receivers of the report are not comfortable reading data as information or that the visualization of the data is done in a non-user-friendly presentation with too much noise and unnecessary information. It could also be that data is not put into the right context and therefore not well linked to the business processes it represents.

There are many more examples of data not being set into a proper context, and thus it remains only data and does not become information which can be used for decision making, recommendations or inspiration.

With the Data Literacy experiment, we set out to further strengthen how we in the LEGO Group work with data and how we can collectively become even better at reading, understanding, visualizing and communicating data as information.

In the next section, you will hear about how we kicked the experiments off to unlock the great potential in all our fantastic colleagues working with data, and where it has taken us so far.

Design Sprint Workshop and Experiments

With great inspiration and facilitation from one of our Agile Coaches, a small group of 4 employees joined a 4-day long design sprint workshop which had the goal of defining one or more experiments to kick off and address the following business challenge:

How might we create a learning environment for colleagues to upskill their data literacy?

Most of us probably went into the 4 days with a solution or idea of training tools and topics already in mind. However, we experienced during the workshop that we ended in a very different place than first expected. We learned to address the problem to be solved from a different perspective, and we were challenged whenever we accidently jumped into solution-mode. In this article we will not go into details of what a design sprint workshop is, however, it is worth mentioning that the Cynefin Framework, Agile principles and Product Led orientation were foundational in the exercise.

The result of the design sprint workshop was to initiate 2 experiments. These had to be short lived and executed in an agile manner in order to fail fast, learn, iterate and learn again.

The 2 experiments chosen were:

• Build a Data Literacy Community
• Setup a Data Literacy Site

The Data Literacy Community

The purpose of the community is to connect people around data (including mentoring), to inspire and help each other with data challenges, encourage Thought Leadership, recruitment and promotion of relevant topics to discuss in the community and to develop content about on the associated Data Literacy site.

The Data Literacy Site

The purpose of the Data Literacy site is to showcase and role model real LEGO examples of good practices on data visualization, analytics, fact-based storytelling, data preparation, data cleansing and modelling and much more. The site also has to offer a combination of internally sourced learning material as well as links to pre-purchased learning material such as LinkedIn Learning and Pluralsight content. Ideally, the site should also be a place to crowdsource input and content from colleagues, so they can share their best practices and great examples of how they have overcome different challenges working with data.

Running the Experiments

So how did we start up these two experiments to get some early learnings? A colleague and I dedicated time to the initiatives and then we simply created a Kanban board in Microsoft Teams as we needed great flexibility. We did not want to set specific sprint goals for a sprint of 2–3 weeks duration, even though this was our usual setup for other work maintained in a JIRA based backlog. Instead, we had the overall goal for the remainder of the current year of 2020 to initiate and engage a community and to support this with a site containing relevant content. Of course, we did run daily stand-up meetings, and we worked alongside each other in a very flexible, Kanban-based setup.

We defined epics and listed at a high-level, what was needed in terms of platforms to base the community and site on respectively. As Yammer is already in great use at the LEGO Group, we decided to launch our community in this platform. Likewise, to avoid hard dependencies on web developer resources the dream of a fancy looking website was replaced by a simple internal SharePoint communication site. Both technologies were already widely used in the organization, so we dived into the research about how to mature and engage a network/community and how to best design a SharePoint site with focus on the most relevant messages to the audience and easy to find relevant content.

The Data Literacy initiative was very well received by colleagues, and we gained 3 times as many community members during the first quarter of the experiment than we had aimed for. Also, the willingness to provide input and ideas for showcases of real LEGO practices and solutions to overcome data challenges was greater than expected. The word about the site started spreading and more and more expert forums or dedicated teams started approaching us to get a subpage created within our site so that they could upload and share all their knowledge, guidelines and best practices with the rest of the organization.

What’s Next?

With organizational changes and enhanced focus on data, we are now part of a newly established Data Office at the LEGO Group. In short this means that, what started as a small experiment, initiated by a couple of colleagues who strongly believe in unlocking all the great in-house data knowledge and inspiring each other to constantly learn and improve the way we work with data, is now founded in a dedicated team within the Data Office.

Stay tuned to learn more about how we further evolve the Data Literacy in practice in the LEGO Group, as the Data Office matures and the focus on importance of data in a Digital Transformation grows. A follow up article will be published later this year.