New Way Now: Gymshark reduces data pipeline development time from weeks to days with BigQuery
23Featured in this video: Evie Dineva, Group Head of Data Engineering and Data Science at Gymshark
Executive summary: Gymshark, a leading UK fitness community and gymwear brand, was looking for a way to get its data into shape for AI innovation. With Google Cloud, Gymshark leverages BigQuery, Looker, Dataflow, and Vertex AI to supercharge its data and AI capabilities while improving its ability to provide unique, personalized experiences to millions of customers all over the world.
Challenge: Recognizing that data is key for driving value with AI, Gymshark knew it would need to focus on building stronger data foundations to prepare for the future. Gymshark wanted to transform its existing data infrastructure to enhance the quality of its customer data and make it easier to gain insights about its customers throughout their personal fitness journeys.
Solution: Gymshark built a new unified data platform with BigQuery to bring together all of its data from across its website, mobile and training apps, and brick-and-mortar stores. Consolidating its data in BigQuery enabled Gymshark to use the full power of the Google Cloud ecosystem, including services like Dataflow, Looker, and Vertex AI, to create an end-to-end platform that accelerates real-time data processing and data pipeline creation while also empowering business stakeholders with self-serve analytics and reporting. In addition, Gymshark’s data is now primed to take advantage of cutting-edge technologies, such as generative AI, to drive innovative use cases like gen AI-powered assistants to personalized in-app training recommendations.
Results: With its new data platform doing the heavy lifting, Gymshark is now poised to harness its data to help achieve its goals — both today and tomorrow. By building a unified data platform on BigQuery and utilizing services like Dataflow, Looker, and Vertex AI, Gymshark has reduced data pipeline development time from weeks to days.
Key takeaways and highlights from our interview with Evie Dineva, group head of data engineering and data science at Gymshark:
→ “For us to be able to do artificial intelligence and drive value from it, we need to focus on the data foundations. That’s where partnering with Google Cloud comes into the forefront to help us support that journey. BigQuery is positioned as the unified data platform, where data scientists, data analysts, [and] data engineers can go in and create a seamless data journey — end to end.”
→ “We unite BigQuery with Looker and Vertex AI to drive insight in the hands of business stakeholders and provide them with real-time analytics and self-serving analytics. Some results we’re seeing [are] with time efficiencies to produce and reproduce data engineering architecture processes and also rebuild our insights. Previously, it would take a data engineer a number of weeks to build out a new pipeline from scratch — now, it’s taking that time to value to a couple of days.”
→ “You cannot have a brilliant, solid house with a great interior if your foundations are unstable. For us, utilizing tools, such as Dataflow, and full control over our data engineering architecture processes with Google Cloud services has been instrumental in giving us the confidence that we're building solid foundations, so that we can have the big house that we all want and really drive value from our data assets.”
Google Cloud products used: BigQuery, Looker, Vertex AI, Dataflow
Learn more:
→ Gymshark Plans Accelerated Global Growth with Google Cloud AI: https://goo.gle/4iXklT4
→ Transforming Gymshark as a global brand and retailer through digital and data: https://goo.gle/42dUSxg