Albert Heijn Technology is the division focused on innovation in retail within the Dutch supermarket chain giant Albert Heijn. It also closely collaborates with retail chains Etos and Gall & Gall.
Albert Heijn’s central data organization is responsible for making operational data available for analytical purposes. Source systems send this data to a central data platform, which serves data scientists, machine learning engineers and data analysts in doing their work. The data platform handles terabytes of data driving a plethora of analytics use cases, ranging from store transactions, stock levels, product information, delivery trucks for online orders, to many others.
This data platform, however, needed to be replaced by a newer one, while it continues to supply analysts and data scientists with (new) data sources. To carry out this process, a team within AH Technology needed a product owner, engineering capacity and a technological lead to help them lead, design and implement their new version of their data platform.
Xomnia provided an analytics translator (as a product owner), data engineers and tech leads. They provided the client’s Head of Tech strategic consultations, and its data architects tactical consultations. Our team was also in charge of the continuation of daily engineering operations.
Some of the in-progress features the Xomnia team started working on include:
Additionally, the team collaborated with business stakeholders to develop standardized KPIs that can be used by multiple end users, eliminating the requirement for duplicate data sets across departments, and providing a consistent view of metrics.
The collaboration between Albert Heijn Technology and Xomnia resulted in significant improvements to their data platform, making it more efficient, effective, and user-friendly. It also helped streamline processes and make data more accessible to analysts and data scientists. Consequently, this enabled stakeholders to make more informed decisions based on accurate and reliable data.
Xomnia's team also provided help to address challenges related to integrating data from source applications to the analytical platform. Moreover, they facilitated the processes by which analytics teams got the data they need, solved data quality issues, migrated data and data pipelines to newer technologies, among others.