Customer Stories
Cloud-Native Data Platform Maximizes Data Potential for Global Business
Multinational logistics services company optimizes business by embedding software engineering and DevOps principles in data science
A multinational logistics services company with a pioneering spirit faced challenging issues in a post-pandemic reality. The logistics business encompasses a huge network of companies, contacts, and technology, and vast amounts of data, so relies heavily on data-driven operations to move goods around the world efficiently and successfully. This company wanted to harness the power of its own data to achieve its digital marketing potential.
Use multiple data sources to optimize data-driven business
Why
Build a cloud-native data platform based on Google Cloud Practice
What
Embed principles from software engineering a DevOps in data science activities
How
New Data Platform
Over 2.5 quintillion bytes of data are created in the world every day. While this wealth of information can drive innovation, it can also hamper it. A pioneering global player in logistics was already collecting behavioral, attitudinal, and transactional metrics 24/7, both from its own applications and third-party tools such as Google Analytics and Adobe Analytics. But the wide variety of sources—intelligent marketing automation tools, websites, customer surveys, social media, online communities, and loyalty programs,(not to mention its legacy systems)—made maximizing its data potential a challenge. In short, the company needed an entirely new data platform.
Multidisciplinary Team
A project team consisting of Xebia consultants and client employees, including platform- and data engineers, a Scrum Master, and a product owner, was assembled. This allowed alignment with the client's requirements while maintaining a sustainable knowledge transfer. The team had the creative freedom to define the roadmap together, apply different ways of working and change members. After trying out various solutions, they decided on Agile Scrum combined with DevOps principles. Within a month, a fully compliant data platform was built on Amazon Web Service (AWS). The team designed the architecture, combining AWS-native services and open source tools. Fully reproducible within minutes, the new data platform supported continuous delivery concepts and self-service data services. Later, the platform was moved to Google Cloud Provider.
“With the new self-service data platform, we are able to instantly provide our marketing organization worldwide with actual and consistent data to fuel their customer-focused campaigns.”
Vice President Digital
Multinational logistics services company
Advantages of a Cloud
The new data and analytics platform designed by the multidisciplinary team quickly and easily integrated multiple data sources while retaining all the advantages of the cloud—think scalability, reliability, real-time availability, and cost-effectiveness. It can be used to search and identify customer segments to target with campaigns and can also run on-demand analyses without interfering with existing data in corporate systems. This means that data scientists can develop predictive models while retaining data isolation, -traceability and -scalability. They can also use version control for their code, embed code quality checks and use automated deployment pipelines. Performance and alerts can be monitored via a dashboard.
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