Digitalizing the hospitality industry under Metro AG
DISH Digital Solutions (DISH) provides a suite of digital products to the hospitality industry: from streamlining operations to improving guest service. Based in Germany, the company belongs to the METRO AG conglomerate which employs over 90,000 people across Europe and Asia.
The Data Insights team at DISH is responsible for delivering value directly to the customer, by leveraging Machine Learning models to help customers optimize menus and manage inventory. Their goal is to enable restaurants to thrive in an increasingly competitive landscape. The team also works closely with other departments, such as Sales and Finance to deliver accurate and timely data for decision making.
Siloed analysis and wavering data trust
The Data Insights team was facing challenges, such as data quality issues, which impacted the reliability and accuracy of their datasets:
- Duplicated logic and discrepancies: There was no central place for data transformation, which led to the duplicate, differing metrics.
- Friction on the BI team: The BI team sits the closest to operations, embedded directly into business units. As such, stakeholders blamed data quality issues on the BI team despite the root cause lying further upstream.
- Siloed analysis: The quality issues were causing teams to mistrust the insights delivered by the Data Insights team. Instead, teams were performing their analysis.
DISH's existing data stack—using Google Cloud Platform ETL tools like Pub/Sub and DataProc—no longer served their requirements to improve data quality.
A transformation layer to improve data quality
Upon joining, Senior Head of Data & ML Operations Ramon Marrero identified the need for a tool that’d sit between the raw data and the reporting layer. This tool would enable the Data Insights department to perform data transformations and quality tests in a staging environment before delivering models to reporting or machine learning.
“We had no quality assurance, tests, documentation, or lineage tracking. We started searching for a tool that’d help us fill the gaps,” explained Ramon. “We needed to have tests embedded in our processes to substantially improve our data quality.”
Evaluating and advocating for dbt Cloud
The Data Insights department, led by Chief Data Officer Dr. Olaf Maecker, embarked on an initiative to modernize its integrations and data processes. Ramon had successfully used dbt Core before—the open-source version of dbt. He had seen the tool “bring more transparency, improving data quality and collaboration.” However, using dbt Core was resource-intensive. Its open-source nature required teams to set up and maintain the infrastructure themselves:
“If you want a scalable solution that easily integrates with your version control tools, then dbt Cloud is the best option,” said Ramon.
Set on the fit of dbt Cloud, the Data Insights department presented the business case to stakeholders. They explained that resolving data quality issues required the appropriate tooling—the costs vs. benefits resonated, and the team received the go-ahead from leadership.
Improved data quality, security, and velocity
A transparent and collaborative data development workflow
DISH Digital Solutions integrated dbt with their version control tool, Gitlab. This enabled the Data Insights department to move towards an increasingly transparent workflow, based on branches:
“Before, people just shared SQL statements through Jira tickets. It was chaos. Now everything's centralized. There's more transparency and much more visibility,” said Ramon. “dbt has improved our data quality, as well as enabled safe collaboration.”
The new setup has enabled all data engineers to work independently on features, perform reviews, and push their code to the different branches. Without the bottleneck of centralized approvals and re-working code errors, the Data Insights department can deliver data products and fulfill data requests faster.
Improved data quality, fewer bugs, and increased trust
The transparent engineering workflow decreased the occurrence of data issues by 30%. Not only are there fewer incidents, but with dbt features like Data Lineage, the team can fix remaining bugs faster. With fewer data quality issues, business stakeholders trust the data more, leaning on the Data Insights department to help with business-critical analytics.
“Analysts and business users can investigate transformations themselves on dbt Cloud,” said Ramon. “They only need to involve the engineering team if there’s something abnormal with the data, which now happens less and less. That means engineers can dedicate much of their time to providing value, instead of just fixing bugs.”
A secure and compliant data environment
After migrating to dbt Cloud, all models are now stored in the same geolocation as their data warehouse. This reduced the need for cross-border data transfers, simplifying compliance efforts. The company also leveraged dbt Cloud’s multi-tenant environment, ensuring that logs and metadata around their data remain within the EU.
Scaling data products within dbt Cloud
The Data Insights department is continuing to explore dbt features, including incorporating Python and model contracts. With contracts, the team will be able to enforce model logic to further increase both collaboration and governance.
In certain use cases, like machine learning, Python is a better fit than SQL. The team can bring their new engineering workflow—with data lineage, version control, documentation, and modularity—to ML models by consolidating their code from Jupyter notebooks with dbt.
