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Valentina Perezalonso
Valentina Perezalonso
Content Marketing Manager
14 Dec, 2022 · 4 min read

How erlich textil streamlined their data modeling with Y42

From constantly exporting CSV files and importing them into spreadsheets to building data models once and streamlining the production of data assets — erlich textil’s success story with Y42.



Retail apparel and fashion


Cologne, Germany

  • erlich textil became a data-focused company by implementing Y42.

  • With Y42, erlich textil only had to build multi-purpose data models once.

  • erlich textil is able to build ad-hoc reports in <1 hour with Y42.

Becoming a business' first data hire isn’t for everyone. Imagine an ecommerce company that has no existing data infrastructure. Would you take up the challenge and set it up yourself, from scratch?

This exact challenge is what motivated Arne Ilg to join erlich textil as their first business analyst. 

erlich textil is a Cologne-based fair fashion company founded in 2016. They specialize in lingerie and home textiles with a fair, transparent, and environmentally responsible approach. The company has been part of the CALIDA group since 2022.

No data strategy in place

Before Arne joined erlich textil, the company needed to make sense of its data. But lacking a comprehensive data strategy, all they could do was export CSV files from the tools they were using and push them into spreadsheets.

Once on board, he set out to explore the data tooling market to find the right fit for erlich textil. For the first few months, he focused on understanding what their current setup was, what their needs were, and where they wanted to go in the next year.

Because Arne was undertaking the task of building the data infrastructure by himself, he had a few requirements for the data tool that erlich textil needed:

  • Compatibility with Google BigQuery data warehousing

  • Ease of use and maintenance

  • Accessibility and centralization

  • Data modeling capabilities

  • Availability of data integrations

  • Data orchestration capabilities

  • Data visualization capabilities

Choosing the right data solution

Having considered tools like Fivetran and Supermetrics, Arne landed on using Y42 as erlich textil’s data solution. These were the winning factors: 

  • Flexibility: Being able to build data models either by writing SQL or simply drag-and-dropping nodes onto the canvas. “It’s very handy,” claims Arne.

  • Accessibility: “I can easily explain to non-technical team members how to use Y42 or what I’m doing in it. They can quickly grasp it,” explains Arne.

  • Available integrations: erlich textil could connect their data tool to Slack, for example. This was a big plus for them.

  • Fair price: Arne believes that Y42 offered “a fair price for a good product that works and is continuously growing”.

But the most important winning criterion was the fact that Arne could build the entire data infrastructure that erlich textil needed without having to hire any data engineers.

Arne Ilg
Arne Ilg
Business analyst at erlich textil

“I’m building the whole data warehouse and data models, setting up the orchestrations, and then doing the visualizations as well as implementing the KPI strategy across the company. Y42 allows me to do all of this without having to do any engineering in the background.”

Implementing Y42

Getting started with Y42 was very straightforward for Arne: “I built an entire data pipeline, from integrating the raw data to modeling, publishing it on a BigQuery workspace, automating it with orchestrations, and exporting it to PowerBI within one day.”

What’s more, the support of Y42’s Customer Success Team enabled Arne to learn the basics of certain modeling use cases (like an RFM score model, for instance) even faster.

Arne Ilg
Arne Ilg
Business analyst at erlich textil

“I absolutely love the Customer Success Team. It supports me in every step and every case. The team is super nice and relaxed. Working with them is always a pleasure.”

As for their first big use case, erlich textil was initially focused on getting their revenue numbers right by modeling the data and then visualizing it. The data was coming from the ecommerce platform Magento. Cleaning and transforming this data was very time-consuming at the beginning, especially when getting the numbers right on an SKU level. But once the model was finished, Arne didn’t have to do it again. This initial model and the business logic implemented in it now serve as the universal basis for many other downstream data assets — like a customer data platform or a marketing ROAS dashboard — which prevents different stakeholders from duplicating business logic and using siloed data across the company.


After implementing Y42, erlich textil achieved three key results:

  • Becoming a data-focused company: Since implementing Y42, the entire company has become more data-focused. Everyone in the company knows what Y42 is and what it’s being used for — Arne has even organized Y42 demos and workshops so that teams understand how to work with their data better.

  • Only building multi-purpose models once: erlich textil requires a lot of financial reporting for weekly, monthly, and quarterly recaps. Without Y42, Arne would have had to build the report from scratch every single time it was required. But with Y42, the financial data only had to be modeled once and orchestrated — now, the reports build themselves automatically.

  • Saving time on ad-hoc reporting: With Y42, erlich textil have managed to build ad-hoc reports in less than an hour — for instance, understanding the average time it takes customers to fulfill a second order (requested by the marketing team).

On top of everything, Arne argues that erlich textil’s biggest win with Y42 has been getting all its numbers right and having access to data evaluations like never before in an efficient and streamlined way.

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About Y42

Y42 is an Integrated Data Development Environment (IDDE) purpose-built for analytics engineers. It helps companies easily design production-ready data pipelines (integrate, model, orchestrate) on top of their Google BigQuery or Snowflake cloud data warehouse. Next to interactive, end-to-end lineage and embedded, dynamic documentation, DataOps best practices such as Virtual Data Builds are baked in to ensure true pipeline scalability.

It's the perfect choice for experienced data professionals that want to reduce their tooling overhead, collaborate with junior data staff, or (re)think their data stack from scratch.

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