Stabilizing the Retail Analytics Platform for Takko Fashion
Client & Challenge
Takko Fashion is an international retail company with high standards for reliable data, consistent reporting, and fast analytics processes.
The existing data landscape was fragmented across various ERP, CRM, and internal systems. Unstable interfaces, changing schemas, and recurring pipeline errors led to delays in data delivery. Additionally, duplicates, inconsistent data logic, and incorrect currency conversions undermined confidence in dashboards and reports.
Project Goals:
- Stabilization of the central data platform using Azure Databricks
- Improvement of data quality for Finance, BI, and business units
- Reduction of pipeline errors and manual interventions
- Provision of reliable, analytics-ready datasets
- Scaling of data engineering capacity without building additional internal teams
- Skalierung der Data-Engineering-Kapazität ohne Aufbau zusätzlicher interner Teams
Strategy
ServiceFactum assumed operational responsibility for stabilizing and further developing the Databricks platform. To this end, a nearshore team of four data engineers was established and managed by ServiceFactum under clear onshore governance.
The focus was on stable data pipelines, improved data quality, and a reliable operational foundation for continuous analytics delivery.
“We don’t just provide a nearshore team, we manage the delivery process to transform unstable data processes into reliable data operations with clear KPIs, transparency, and increasing operational responsibility.
Our goal is to lay the groundwork, step by step, for stable, scalable operations.”
Bernd Wandt, CEO and Onshore Delivery Manager at ServiceFactum
Solution
ServiceFactum stabilized the data platform through proactive monitoring, systematic error analysis, and the optimization of unstable connectors.
In Azure Databricks, data from SAP, Salesforce, and internal systems was standardized, cleaned, and harmonized. Using Python, Spark, and SQL, duplicates were removed, business-critical logic was corrected, and consistent datasets were made available for Power BI.
Key measures:
- Resolving recurring pipeline issues
- Adapting to schema changes and unstable interfaces
- Cleaning and harmonizing retail data
- Correcting critical logic, e.g., in currency conversions
- Integration into backlog management and BI prioritization
Technologies
The solution was based on Azure and Azure Databricks. Python, Spark, and SQL were used for data processing, quality assurance, and transformation.
Data from SAP, Salesforce, and internal systems was prepared for reporting and analytics and made available via Power BI. Azure DevOps supported the management and continuous delivery of the solution.
Results & Benefits
Takko Fashion was able to significantly improve the stability of its central data platform. Pipeline failures and manual interventions were reduced, while data became more reliable and consistent for reporting and analytics.
The improved data quality bolstered confidence in Power BI dashboards and business reports. New analytics use cases could be implemented more quickly.
Through the onshore-managed nearshore model, Takko Fashion also achieved cost efficiencies of 30 to 50 percent compared to purely onshore setups, without adding additional internal headcount.