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Impact Study

Unlocking scalability, reusability, and security while reducing costs

Leading high-tech manufacturing company. An innovation leader in the semiconductor industry, providing chipmakers with hardware, software, and services.

Challenge

Following a period of organic growth, and anticipating that growth to continue, this company found itself in a situation where data had become scattered and siloed throughout the business. This was making it difficult to access and share high quality data, slowing the development of new applications, degrading service levels and eroding trust among customers around data security and IP protection. The challenge was to consolidate and streamline disjointed infrastructure and facilitate a seamless data exchange for data producers and consumers.

Solution

We collaborated with a high-end cloud-native service integrator to consolidate 5+ different platforms into a single secure data and technology solution. This brought down costs while enabling scalability, reusability, and security. We introduced standardized data management principles and a new operating model that enabled seamless exchange between producers and consumers of data. And we transformed a tightly coupled architecture of data and apps to their underlying platforms, which made portability almost impossible, into a new decoupled architecture. This leverages platform capabilities while maintaining interoperability between platform instances.

Outcome

This transformation has enabled a €multi-billion business case to increase value from data by enabling easy access to high quality data, trusted and secure data exchange, greater re-usability and faster time to market.

  • Accelerate: new solutions released in days instead of months.
  • Enable: new use cases made possible (e.g. projects that require data streaming).
  • Re-use: data products and features easily repurposed to extend value and enhance innovation
  • Reduce: build-once-use-many principle eliminates duplicate work, massively reducing platform DevOps-related spending.
Data scientists were spending 90-95% of their time just collecting, organizing and finding data, and only 5-10% on actual analysis and solution development. This transformation reverses those metrics, opening up a new world of opportunity.
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