Computational Sciences Center of Excellence (CS-CoE) Architecture Playbook

The CS-CoE Architecture Playbook is a collection of best practices, guidelines, and standards for designing and implementing software architectures within the Computational Sciences Center of Excellence at Roche. It serves as a reference for architects, developers, and other stakeholders involved in software development projects.

Architecture Reference Slide Deck

Architecture Master Deck brings together our shared direction and the blueprint for delivery. It outlines our Vision and Mission, the Business Architecture that anchors key capabilities and value streams, our Key Focus Areas, the Operating Model that clarifies how we work, and the Roadmap that sequences what we deliver and when.

Use this deck as the single reference point for strategic alignment, decision-making, and communicating priorities across stakeholders.

Benefits of Enterprise Architecture

Enterprise Architecture brings many benefits across a complex organisation. The key ones are summarised below together with the features the Cs-CoE Enterprise Architecture provides

Strategic Alignment

Benefit Features

Effective Prioritization and Resourcing: Supports the alignment of investments in data, applications, and technology to business goals, which provides effective prioritization and ensures resources are allocated to the most critical areas.

Business Capability Taxonomy

Strategic Views for Decision Support

E2E Business Process

Digital Landscape of system maturity

Staying Current and Reducing Risk: Aligning with strategic partners in the complex IT industry helps the organization stay up-to-date with the latest technologies and applications.

Technical Blue Prints

Digital Landscape of system maturity

Cost Reduction and Efficiency

Benefit Features

Reduced costs: A solid architecture significantly contributes to reduced operational costs and cycle-time. By minimizing duplication and promoting data reuse, it directly lowers operational expenses. Furthermore, it accelerates cycle-time by reducing the effort consumers spend on data cleaning, allowing them to focus on analysis and speed up decisions.

Strategic Views for Decision Support

Use case driven data architecture simplification

Digital Landscape of system maturity

Technical Blue Prints

Accelerated Initiatives: Provides a unified starting point for initiatives, speeding up the time from experiment to production by ensuring teams use common technologies and applications.

Use case driven data architecture simplification

Technical Blue Prints

Reduce complexity

Benefit Features

Improved Agility and Strategic Clarity: Business architecture, achieved by viewing the business through value streams fosters agility, reduces risk, and provides a clear roadmap for achieving strategic objectives, while ensuring organizational friction is reduced and data flows seamlessly.

Business Capability Taxonomy

Strategic Views for Decision Support

Data Terminology and Preclinical Research Data Model

E2E Business Process

The xRED Data Stack

Digital Landscape of system maturity

Enhanced Data Quality and Speed: Standardization of data ensures it remains consistent and retains integrity, making it faster to use in both primary and secondary use cases.

Data Terminology and Preclinical Research Data Model

Use case driven data architecture simplification

Transparent and Seamless Data Flow: Well-documented data models deliver a transparent understanding of data structure and flow, which reduces friction between organizations and enables a seamless flow of data with clear lineage.

Data Terminology and Preclinical Research Data Model

Data Architecture Stewardship

Use case driven data architecture simplification

Agility

Benefit Features

Ease of Change: Applications built to be modular by design overcome the rigidity of monolithic systems, making it clearer where and when a change needs to be made, and easier to understand and quantify its impact.

Technical Blue Prints

Digital Landscape of system maturity

The xRED Data Stack

Accelerated Innovation: The introduction of dual cycles allows for exploration to run in parallel to delivery, giving researchers the freedom to innovate while maintaining the governance needed for consistent, reproducible, compliant and efficient AI solutions.

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