A global manufacturing and technology leader partnered with AIM Consulting to modernize its data ecosystem for its Research and Development (R&D) function. AIM implemented a cloud-based solution that fostered data sharing, empowered non-technical users, and integrated advanced data management and governance practices. This initiative led to faster innovation, enhanced collaboration, and enabled a cultural shift towards openness in data sharing, significantly boosting the company’s R&D capabilities.
Case Study Contents:
- Situation: Modernizing R&D Data Infrastructure for Global Innovation
- Solution: Building a Cloud-Powered Data Sharing Platform
- Results: Accelerating Scientific Discovery Through Digital Transformation
What AIM did:
- Implemented cloud-based data ecosystem on AWS, integrating services like S3, EC2, Lambda, EKS, Kafka, and OpenSearch with Databricks platform
- Designed architecture based on data mesh principles, emphasizing data as a product and federated governance
- Created central data catalog to enable data discovery and sharing across R&D teams
- Developed user-friendly interface for both technical and non-technical users to access data and computing resources
- Established automated data governance checks during data ingestion
- Provided coaching and knowledge transfer to enable client’s teams to independently maintain the solution
- Created product maturity roadmap and release plan for ongoing development
- Conducted regular SCRUM ceremonies to maintain project alignment and progress
Situation: Modernizing R&D Data Infrastructure for Global Innovation
A global leader in manufacturing and technology sought to modernize its data ecosystem to support data storage, search, discovery, and sharing use cases across its Research and Development (R&D) function. The company aimed to empower its scientists and non-technical users with cloud services for data storage and computing capacity. Additionally, they wanted to foster a culture of data sharing across teams to accelerate innovation and drive better collaboration.
However, the company faced several challenges in meeting these objectives:
- Lack of Expertise: The organization had an initial concept but lacked the expertise to move from idea to product, particularly in the data and analytics space.
- Project Management Challenges: The existing project management and prioritization model was not well-suited to this type of transformational project.
- Cultural Resistance: Data sharing required a significant cultural shift, as many data scientists were accustomed to keeping their work private.
- Architectural Complexity: There was no established, scalable architecture pattern within the organization for this type of project, making it a first of its kind.
Recognizing the complexity and their own limitations—such as unclear vision, resource constraints, and lack of specific engineering skill sets—the company decided they needed external help after failed attempts to advance the project internally.
Solution: Building a Cloud-Powered Data Sharing Platform
The company chose AIM as their partner based on our proven experience with similar transformational projects, a broadly skilled team, and a strong vision for project execution.
We proposed a comprehensive solution that included:
- Logical and Solution Architecture: Drawing on data mesh principles, including concepts of data as a product, federated computational governance, self-service, and domain-driven ownership of data.
- Cloud-Based Data Management: Delivering a cloud-based solution for data management and governance, designed to be scalable and user-friendly.
- Data Catalog Implementation: Establishing a central data catalog to promote data discovery and sharing across the R&D function.
- Databricks Integration: Enabling the management and analysis of large datasets using the Databricks platform, with automated provisioning for storage space and compute capacity.
- User Interface Development: Creating a user-friendly interface to drive adoption and ease of use among both technical and non-technical users.
- Product Maturity Map and Release Plan: Developing a roadmap for the product’s evolution and a clear release plan.
- Data Governance Best Practices: Identifying and recommending best practices for data governance, particularly in handling restricted data.
The project scope included the delivery of a cloud-based solution built on AWS, utilizing services like S3, EC2, Lambda, EKS, Kafka, OpenSearch and others. The technical stack also incorporated Databricks, SyncThing, Temporal.io, and a variety of programming languages and tools.
We kicked off the project with a comprehensive alignment session, establishing the project scope, deliverables, roles, and a detailed project plan. Regular SCRUM ceremonies and feedback loops between the product owner and engineering team ensured continuous alignment and progress.
We also provided extensive coaching and knowledge transfer sessions, empowering the client’s teams to continue development and support of the solution independently.
Results: Accelerating Scientific Discovery Through Digital Transformation
The project, which is ongoing, has already yielded significant successes:
- Adoption and Efficiency Gains: The platform has seen rapid adoption within the R&D teams, enabling faster execution of experiments and increased collaboration across scientists.
- Cultural Shift: The introduction of a self-serve user interface empowered non-technical users to leverage the system, contributing to a broader cultural shift towards data sharing.
- Data Governance Improvements: Automated data governance checks were implemented as part of data ingestion, ensuring compliance and integrity.
- Strategic Wins: The platform allowed the client to achieve initiatives that were originally part of a three-year plan, much sooner than anticipated.
The success of this project means that the company’s R&D teams will continue to see increased efficiency and faster innovation cycles. As collaboration and data sharing become the norm, scientists will be able to leverage powerful cloud infrastructure and capabilities to expedite the processing of their experiments and analysis, driving the company’s competitive edge in the industry.
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