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DOUGLAS' Transformation: Centralizing 200+ Data Sources with Fivetran
Technology Category
- Analytics & Modeling - Big Data Analytics
- Analytics & Modeling - Data-as-a-Service
Applicable Industries
- Buildings
- Cement
Applicable Functions
- Sales & Marketing
Use Cases
- Building Automation & Control
- Time Sensitive Networking
Services
- Data Science Services
- System Integration
The Challenge
DOUGLAS, a leading premium beauty platform in Europe, was facing a significant challenge in its journey to become a 'Digital First' business. The company's existing infrastructure and processes, particularly around Business Intelligence (BI) and data analytics, were not up to the mark. The systems for collecting data were scattered, and there was an overreliance on spreadsheets and manual input, which were not scalable. This lack of a centralized, automated data collection and analysis system was hindering the company's growth and its ability to gain valuable insights from its data.
About The Customer
DOUGLAS is Europe's leading premium beauty platform, inspiring customers to live their own kind of beauty. The company offers more than 130,000 beauty and lifestyle products through its online shops, beauty marketplace, and over 2,000 stores. In the fiscal year 2019-2020, DOUGLAS generated sales of 3.2 billion euros in areas such as perfumery, decorative cosmetics, skin and hair care, nutritional supplements, and accessories. The company is on a mission to become a 'Digital First' business, and to achieve this, it is investing in state-of-the-art technology and data modelling.
The Solution
To overcome these challenges, DOUGLAS decided to invest in a future-oriented, best-practice combination of Fivetran for automated data integration, Snowflake for the data warehouse, and Tableau for data analytics. This solution involved using around 200 active connectors to pull data from multiple sources with Fivetran, providing the business with market intelligence across a range of areas. The automated data integration by Fivetran allowed BI analysts, BI engineers, data scientists, and marketing and eCommerce experts to turn data into actionable insights more quickly. Additionally, the company now had a robust, resilient, and GDPR-compliant architecture that met all the necessary security requirements for a large, internationally operating online enterprise dealing with sensitive customer data.
Operational Impact
Quantitative Benefit
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