Solution: A learning series emphasizes a data-informed workforce.

Business impact highlight: The program resulted in a 20 percent confidence boost and 79 percent improvement in participants’ ability to tackle data challenges.

Shopify is on a mission to improve commerce for everyone, and the e-commerce company believes doing so requires a team of innovators who are willing to experiment, observe, and continuously improve.

When Shopify made the transition from one data platform to another, the company urgently needed to provide training (a point-in-time solution) to level up individuals to the platform itself. The goal was to develop a program that would efficiently bring everyone up to speed on the new platform, regardless of their initial skill level. We sought to teach all employees to ask the right questions, know when to troubleshoot or consult a data expert, and get hands-on experience with relevant tools.

Recognizing the inevitable and continuous fluctuation of technology and tools, the L&D team needed a solution to ensure all employees had the skills and resources to complete their jobs well.

The challenge wasn’t just about learning new tools; it was to help team members approach data from a first-principles mindset, meaning L&D wanted to empower individuals to question assumptions and create their own solutions.

With the half-life of skills quickly shrinking, the L&D team chose not to focus on the specifics of each technology tool but, instead, to build a shared company language and develop a data-informed workforce—a much more involved task.

To promote critical-thinking, problem-solving, and decision-making skills, we created a series known as "Zero to," which includes programs such as Zero to Data, Zero to AI, and Zero to Code.

A practical, flexible approach

We developed and ran the Zero to Data program twice over a six-month period. The first version of the program launched in just six weeks via an iterative approach. Rather than waiting for the entire program to be complete, we released modules as they were ready, enabling us to gather real-time feedback and make immediate adjustments. That strategy not only accelerated the launch but also ensured the program remained responsive to learners’ evolving needs throughout its development.

Involving subject matter experts

To hasten the concept and content development without taking too much time from internal data subject matter experts, we created a prototype sandbox—an essential phase for testing and shaping ideas before full development. Instead of involving SMEs immediately, we used an artificial intelligence persona from our internal ChatGPT model to progress swiftly from concept to content.

The AI tool helped us create a solid draft, which SMEs could then review and refine, saving time. That tactic ensured we used SME time effectively, enabling them to focus on providing targeted, high-impact feedback that made the training program relevant and deeply connected to employees’ day-to-day work.

For the second version, we aimed to create more practical activities to help the program resonate with a broader range of employees. Building on the foundation established in version one, we piloted a new approach to maximize SME involvement. We organized a design flash burst, bringing together five data experts for two half-day sessions. We modeled the flash burst after a design sprint, which seeks to build and test a prototype in five days. We collaborated on the learning flow, refined the content focus for accuracy, and developed practical case study scenarios for participants.

Both approaches were highly efficient. In the past, securing time from data experts was challenging, often leading to delays. By convening multiple SMEs simultaneously, we expedited the process while benefitting from their collective ability to deliberate and refine the content from a technical perspective.

The collaborative discussions added a layer of depth and precision that our L&D team alone couldn’t achieve. In fact, from the first version to the second, the design sprint enabled us to create more scenarios (four versus one in version one of the program) faster, providing relevant options for all participants.

Hands-on learning

To make the learning experience as practical as possible, we structured the hands-on component around a central, ongoing project that mirrored real-world tasks participants might encounter in their roles. The approach enabled participants to apply their learning to authentic problems using the course’s tools and techniques. We designed the practice activities to be both meaningfully deep and accessible, ensuring all staff could benefit. While learning theory informed course development, our primary goal was to teach learners how to apply these skills to tackle real challenges.

For the first version, we developed a project that would resonate across the company. However, feedback indicated that participants wanted to practice activities that were more closely related to their specific jobs. In response, for the second version, we leveraged the design flash burst to brainstorm different case studies. We asked data experts, “What types of questions do different teams—such as operations, engineering, and product—typically ask you?”

The SMEs then paired up to develop specific practice questions for each module, tailored to the scenarios. The collaborative approach ensured that the exercises were directly relevant and practical, providing participants with experience that closely mirrored their day-to-day work challenges.

Launch and delivery

To set up participants for success, we introduced a Zero Week to help them navigate the necessary tools and establish permissions—particularly in the developer platform, which can be tricky for people new to technology.

We released content weekly, combining written materials with video tutorials. We also held live solution walkthroughs with a data SME who reviewed the week’s content and discussed any challenges.

Metrics and results

The first version of the program attracted more than 800 participants, 2.5 times higher than our initial target, and the second version attracted more than 300 participants. Team members had a 20 percent boost in confidence when using Shopify’s data platform and 79 percent of participants increased their ability to tackle data challenges.

Postprogram survey results highlighted three key factors.

Live solution walkthroughs. These sessions were a significant draw. While staff appreciated seeing the week’s problem solved, they found more value in hearing a data SME’s thought process—as in, how the SME approached data, the questions they asked themselves, how they navigated tools, and what they did when they encountered challenges.

Comprehensive and flexible content. The initiative’s breadth and flexibility ensured that it catered to everyone, so participants from all disciplines—including engineering, product, operations, and legal—could discover relevant and useful information.

Completion challenges. About 40 percent of participants didn’t complete significant portions of the curriculum, highlighting the difficulty of balancing pace and depth in a fast-paced environment. That feedback prompted an interesting question: Are completion metrics an indicator of program content or structure?

Next steps

The content is continuously available to employees so that they can engage with topics at their moment of need.

Looking ahead, our team is considering extending the live sprint cohort, giving participants two weeks between each module to enable more time for absorption and application. Of importance is flexibility. We want to empower employees to learn at their own pace and focus on the material most useful to them.

By focusing on first principles and practical application, we’ve equipped Shopify employees with the tools they need to confidently navigate data challenges. As we continue to refine and adapt the program, we seek to make data literacy an integral part of company culture, enabling staff to approach problems with a data-driven mindset, no matter how the tools or challenges evolve.

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