Imagine trying to grow your eCommerce business, but every time you need a report, you’re stuck pulling numbers from three different systems: Shopify for sales, Google Analytics for marketing, TradeGecko for inventory (insert other ERP/Marketing/Trading systems here). Which system do you trust? What if the numbers differ? How do you align that data from each?  

Do these pain points sound familiar? You’re not alone, these are frustrations we hear almost daily.

Data is supposed to be your business’s greatest asset, but when it’s locked away in silos, it can feel more like an obstacle. Many businesses resort to manually exporting data into spreadsheets, hoping to piece together a complete picture. It’s time-consuming, error-prone and frustrating. Let’s explore why Shopify and Magento users sometimes struggle with reporting and how a platform like The Data Refinery can change the game for them.

A quick overview of Shopify and Magento

The challenge of siloed data isn’t just about having too many systems; it’s also influenced by the tools at the heart of most eCommerce businesses. For many, that means Shopify or Magento. These platforms are crucial for online sales and data generation, but their different approaches to handling information create unique reporting challenges.

• Shopify makes it easy for businesses to set up their online store quickly, offering streamlined workflows for product management, payment processing and customer engagement. Its simplicity is a major draw for entrepreneurs.

• Magento is built for businesses that need more customisation. It’s a powerful platform often used by large enterprises that allows for tailored e-commerce experiences but requires more technical expertise to manage effectively. Both platforms offer strong e-commerce solutions, but their approaches to data management create challenges when it comes to reporting. Let’s break that down.

Shopify vs Magento: What sets them apart?

While Shopify and Magento both power online stores, their differences significantly impact how businesses manage and analyse data.

1. Ease of Use - Shopify is beginner-friendly, requiring little technical knowledge. Magento, on the other hand, has a steeper learning curve and is best suited for businesses with development resources.

2. Customisation - Magento allows deep customisation, making it ideal for businesses with unique needs. Shopify is more structured, limiting flexibility but ensuring a smoother setup.

3. Scalability - Magento handles complex operations and large catalogues well, making it great for scaling businesses. Shopify is also scalable but designed more for straightforward growth.

4. Cost and Resources - Shopify has transparent pricing with built-in hosting. Magento, while open-source, often involves higher costs due to hosting, development and maintenance.

5. Data & Reporting  - Shopify simplifies integrations but offers limited built-in reporting. Magento provides greater data control but requires more effort and technical expertise to configure analytics.  

How do these differences affect how businesses consolidate and analyse their data? Let’s take a closer look.

How Shopify and Magento store data (and why that makes reporting harder)

Even though Shopify and Magento serve the same purpose, the platforms and underlying data structures and deployment models are fundamentally different.

Shopify: Fully managed platform

Shopify has a well organised and simplistic data model (compared to Magento) that is stored within the Shopify platform. However, as a fully managed platform, users are not granted access to the underlying data. Users typically only have access to reports and exports permitted via either Shopify’s reporting tools, or 3^rd party reporting app store offerings.

Data is also available via the Shopify API but requires technical expertise to correctly use this API as a source for reporting.

To summarise, joined up reporting between Shopify, inventory tools and web analytics tools is hindered by data access constraints and the reporting capabilities of Shopify.

Magento: Open source / Hosted platform

As previously noted, Magento offers a different deployment model and a much more complicated and configurable set of controls to extend the platform. A self-hosted, or managed installation of Magento, typically means that it’s easier to interrogate the underlying Magento data stores.

The trade-off for greater configurability offered by Magento is that the underlying data store is much more complex to work with from a data analysis perspective. Knowing which data tables hold vital information such as orders, customers and products, is confusing and unintuitive. Finding that data requires a user to understand the Magento Entity-Attribute-Value (EAV) model to then be able to query the correct combination of tables.

Like Shopify, Magento offers a level of reporting via the administration portal or via 3^rd party apps, but again as with Shopify, users are restricted to the data served up by these reporting tools.

This means, users looking to leverage all their Magento data alongside other tools in their eCommerce stack, will need to work against the highly complex Magento data model.

How do you get value from the data within these platforms without the headache? That’s exactly what The Data Refinery solves.

How The Data Refinery standardises data for seamless reporting

Even with access to the underlying data held within Shopify or Magento, the operational structure and layout of the data means it is not in a state to allow for simple reporting metrics to be generated.

The Data Refinery eliminates data inconsistencies between Shopify, Magento and other eCommerce tools by using a Common Data Model, a standardised structure that ensures all integrated data speaks the same language. This makes reporting repeatable, scalable and automated.

This means that data we consume from Shopify or Magento is transformed into the same reporting structure, this enables our product to provide the exact same reporting capabilities regardless of the source platform.

The key outputs delivered by our common data model are:

• Consistent Data Model - Maps disparate sources into structured, linked tables.  

• Cross-System Analytics - Ensures 'like-for-like' reporting across platforms.  

• Automated Customer Matching - Consolidates multiple records into unified profiles.  

• Pre-Built Dashboards & KPIs - Saves teams from manual reporting tasks.

Learn more about how The Data Refinery standardises data here.

How the Data Refinery Integrates with Shopify

The data held within Shopify is broad and is used to power your entire eCommerce store. From a report perspective only a small portion of data can (and should) be used for reporting purposes, but the tougher question is, what data is that? And how should the data be pieced together for coherent reporting purposes.

Shopify in fact holds data in 97 different tables, attempting to report across this data structure is difficult, as certain entities must be associated with one another to produce valid reporting outcomes.

This inflexible data structure means reporting become cumbersome and prone to mistakes, where a missing association, or data taken from the wrong tables can lead to drastically different outputs.

To cut through this noise, The Data Refinery gathers data from 17 of those 97 tables, to produce a logical and meta rich view of your Shopify data, ideal for report building and more crucially data is re-housed into our common data model, alongside all data from your sibling systems.

Our Shopify integration is constantly evolving, as Shopify introduces changes to their platform, leading to changes in the data models, our integration changes also. We ensure our integrations mitigate any risk of broken reports or misconfiguration which in typical integrations might break downstream dependencies.

How the Data Refinery integrates with Magento

Brands using Magento for their eCommerce store, will typically be looking for a more configurable and extendable commerce platform. Most brands using Magento will be large enterprises that have complex or unique commerce requirement and the budget to extend the Magento instance via dedicated engineering resources.

Given the above, Magento as a platform is much more complicated to use both from an implementation and administration perspective, and the data that Magento produces is vast and highly complex. To further complicate integration requirements, Magento is open source and can be hosted using different database editions such as MySQL, MariaDb, MySQL. This means there are multiple hurdles to overcome to reliably access Magento data before then facing the daunting task of making the underlying data reportable.

In comparison Shopify (that is underpinned by 97 tables), Magento can produce up to 450 tables. Data is also stored in multiple places, with core Magento tables that are then referenced by extended entities, administration entities and store front entities. This makes finding the correct tables to use for reporting purposes a challenge.

On first inspection the administration tables (also known as grid tables) offer up a schema layout that is somewhat like that seen with Shopify. However, the issue with reporting from these tables is that they are not updated in a predicable way (some auto update, others on schedule, and some manually). This results in stale reporting outputs and no way to maintain a real-time view of performance.

The reason for this much larger footprint of tables, is the configurable nature of the platform, that means for each typical commerce entity (e.g. orders, products, categories) there is a requirement to include multiple ancillary tables that hold settings, configuration and mappings that means the platform (products, customers, orders) can be customised without making code changes.

This extensible model is known as an EAV(Entity, Attribute, Value) Model and is an established pattern that data or application architect might choose to adopt when building a system, where they want to give users the flexibility to create their own entities and fields. Source.

The only reliable way to extract accurate data from Magento is to bypass Grid Tables altogether and work directly with the EAV tables. These raw data tables span 29 core tables. Unlike Grid Tables, EAV tables are updated in real time, making them the most dependable source of truth within Magento. However, integrating this data is far more complex (compared to Shopify), as it requires mapping and modelling data across 29 tables instead of 17.

We have created a suite of connections to support the differing database editions and versions of Magento (V1, V2), to ensure we cover all potential configuration combinations. Our integration ensures you get access to the correct data needed for reporting, where such data is hard to unearth from the hundreds of tables within the Magento eco-system. Brands using Magneto can create truly unique commerce experiences, we provide the insights that ensure complete oversight, so brands can validate and enhance all aspects of eCommerce operations.

Summary

So, in summary, if you’re an eCommerce business struggling to get useful and detailed insight from your data sources, there are LOTS of very good reasons why. We’ve spent years building bespoke data warehouses and it took us quite some time to build a standardised Data Model and pipeline that meant we could help businesses like yours get to a single customer view and whole of organisation reporting capability. The best bit is, now we’ve done it, it only takes a few minutes to get you set-up and you’ll be fully data enabled in no time.  

If you’d like to see it in action get in touch.