i-spark makes data extra valuable to WWL holding with expertise in various areas such as data architecture, data engineering, machine learning, dashboarding and data analytics.
- i-spark makes data extra valuable
View caseWWL holding is a specialist in lighting, both conventional lamps and future ready Led lights and fixtures. With 24 webshops in 15 countries and a wide range of eco-friendly LED solutions, WWL is making an important contribution to the greening of Europe. The company has its own data department and i-spark provides hands-on support there with expertise in various areas such as data architecture, data engineering, machine learning, dashboarding and data analytics.
Data
Extra Valuable
Making data extra valuable
Various teams within WWL Holding use insights from data to evaluate the effectiveness of their webshops, marketing efforts, and internal processes. Insight drives growth! Some of this information is even utilized for providing shipping data to international customs authorities. Thanks to i-spark's expertise, WWL's data team gains additional knowledge, making their data exceptionally valuable. Together, we're enhancing their data significantly.
About usInsights from Combined Data Sources
For WWL’s webshops, in addition to sales figures, the number of returns is a crucial KPI. Based on clickstream data, transactional data, and data from marketing channels, WWL continuously optimizes its webshops.
The data from the 24 webshops is spread across a wide range of different systems and platforms. These include sources like e-commerce platforms, warehousing software, customer support solutions, ERP packages, and various marketing automation solutions.
i-spark supports WWL in unlocking and centrally storing this data, enabling the combination of data sets.
Smooth Engineering, Clear Reporting
We developed a data platform based on Amazon Web Services (AWS). Every hour, we collect all data through various ELT pipelines, both custom-made (in AWS Lambda and AWS Glue) and through our partner Dataddo. We store this raw data in a Data Lake in AWS S3. Subsequently, we write this raw data to the Amazon Redshift data warehouse.
Given the large amount of data, we use DBT to transform the data into a data model. DBT helps reduce the load on the dashboarding tool, in this case, Looker. Our analysts use this data model to create desired insights in Looker, available through clear reports.
Enriching Source Data with Customer Information
In addition to Looker reports, the system we developed sends back ‘Customer Cards’ via APIs to the source systems, enriching the source data with information from our client’s customers. In collaboration with WWL Holding, we continuously work to add more value to the vast amount of existing data.
Are you looking to increase the value of your data or improve your data team’s capabilities? Feel free to contact us with no obligations. Take the next step in maximizing the full potential of your data by reaching out to us today!