I work with merchants who use the Shopify platform to sell online, among other tools for analytics and testing. Working with merchants who sell for millions of dollars each year over thousands of SKUs to consumers and businesses, gets complicated for everyone in the chain, especially if you don’t have a single source of information.
Setting up machine learning models and API integrations is a lot of work for one platform, let alone working across multiple third-party platforms that vary with each merchant.
Self-service analytics for Shopify
To get around this, we built microservices to export data using parameters like date ranges to MongoDB.
Leveraging Microservices to power self service analytics in Metabase
Another microservice would clean up this data and have it ready for a Metabase import allowing us to standardize datasets across platforms, so merchants and managers can ask more meaningful questions in Metabase.
The microservices that export and clean the data run locally and on-premise, allowing higher levels of trust for the merchants. Because all of the data is stored locally, it safeguards us from third-party breaches that we have no control over.
Key takeaways of using Metabase for Shopify
- It’s not just about beautiful visualizations. There’s a need to have relevant information sets done beautifully, to allow better decision making.
- Many companies aren’t data-driven and people who run those companies need to be data-informed.
- One block at a time approach. It’s easy to get carried away with the powerful features that are easy to use, but keeping the focus to build relevant blocks systematically is a better approach.