Project Description

As a leader within a very competitive industry, our customer saw very early the potential of using data to improve production efficiency and create the best possible customer experience. One important step in that journey was the decision to create a pan-European Data Analytics landscape.

The goal was to provide a unified methodology and implementation path to deliver data-driven projects across Europe to serve the data-driven strategic ambitions.

The project had to deliver data efficiently to feed data science and business intelligence use cases. Some were very marketing-driven (e.g. customer lifetime prediction), some firmly linked to the manufacturing processes(e.g. predictive maintenance of robots) and some related to the usage of connected vehicles (e.g. smart warranty dashboard).

A big part of the complexity was the important number of siloed data sources that had to be combined to provide the necessary results across this large range of diverse business use cases.

If you add to that the vast usage of personal data that had to be managed under GDPR constraints, the technical aspects were consuming a lot of bandwidth across the teams.

Getting the Technical Complexity out of the Way

As an out-of-the-box implementation of the target data architecture, Digazu immediately provided automation and simplification of many of the technical aspects:

  • User-friendly end-to-end real-time data engineering: data collection, streaming, data transformation and distribution
  • Automation of the GDPR processes: data anonymization, right to be forgotten, data retention, consent management
  • One-click development lifecycle integration: move from test to user acceptance and production
  • Control framework interfacing with the overall data governance and enterprise access management systems
  • Operations and monitoring

In a few months, the team was able to roll out the first business use cases and build credibility towards the business to address an ever-growing number of value-creating data projects.

A Business-driven Data Program

The shift from technology to value creation allows the program owner to communicate clear value metrics to the business: recurring cost savings, increased quality, new business generation and improved customer satisfaction.

The standardisation of processes and roles provided by the program provides a much more readable and predictable engagement model for the rest of the company to create value through data.

Now, data engineering, once arguably the biggest bottleneck, is down to minimum effort across over 200 data sources and all the focus is on managing the effectiveness of the program as a whole and delivering business value.

With a clear path to value creation, there is now a pipeline of over 25 business use-cases and this is just the beginning.