Data & AI Operating Model
Translating Data & AI Strategy into Skills, Organization, Processes, Technology, and Governance
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This course is part of the certified "AI & Data Strategist" Master Class. When you enroll in the entire Master Class, you’ll save 21 percent compared to enrolling in the individual modules.
- What Defines a Data & AI Operating Model
- "Structure Follows Strategy"
- how you assess your own level of maturity (self-assessment)
- An Overview of the Dimensions: Business Model, Capabilities, Organization & Roles, Processes, Technology & Architecture, Governance (including GRC), Policies
- Key design considerations: Work split between business units and IT; organizational structure—horizontal (centralized/decentralized) and vertical (levels, e.g., staff functions)
- Capability Mapping Methodology; Capability Categories for Data & AI (Data, Technology/Platform, Analytics/AI, Governance, People/Skills, Processes); Strategic Data Acquisition as an Example Capability
- Practical Exercise: Create your own capability map; compare target and actual levels for each capability; identify capability gaps
- Preparing for the webinar: Bring your own capability map and list of prioritized gaps to Webinar 2 as a working document.
- Organizational Structure: Work Split Between Business Units and IT; horizontal organizational structure (centralized/decentralized, hub-and-spoke) and vertical organizational structure (staff unit/subdepartment/business unit)
- Roles and Responsibilities: AKV (Tasks, Competencies, Responsibilities) and RACI; Interfaces with Technology and IT Architecture
- Business Process Organization: Process Modeling at Levels 1, 2, and 3
- Data & AI Governance and GRC (Governance, Risk & Compliance): Committees, Decision-Making Processes, and Guidelines for Data and AI
- Practical Assignment: Design Your Own Governance, Risk, and Compliance (GRC) Structure and Outline Initial Data and AI Policies
- Preparing for the Webinar: Your draft governance and policy documents will serve as the basis for Webinar 3
- Integrate organization, processes, technology, and governance, and use enterprise architecture to assess the technological fit with data and AI management and processes
- Promoting Operationalization, Process Acceptance, and Adoption — Getting People and the Organization On Board
- Conclusion: Refine your target operating model and prepare it for decision-making
1. Understanding the Data & AI Operating Model – Framework & Dimensions
- Elements of a Data & AI Operating Model
- Why “Structure Follows Strategy” Should Apply
- Assess your own level of maturity (self-assessment)
- Key design considerations: Work split between business units and IT; horizontal and vertical organizational structure
2. Capability Mapping – Identifying the Right Skills
- Capability Mapping Methodology
- Practical Exercise: Create your own capability map; compare target and actual levels for each capability; identify capability gaps
3. Design the organizational structure and operational processes
- Organizational Structure: Work Split Between Business Unit and IT; Horizontal and Vertical Organizational Alignment
- Roles and Responsibilities: Tasks, Competencies, Responsibility & RACI; Interfaces with Technology & IT Architecture
- Workflow Organization: Process Modeling at Various Levels
4. Governance – Committees and Guidelines
- Data & AI Governance and GRC: Committees, Decision-Making Processes, and Policies for Data and AI
- Practical Assignment: Design Your Own Governance, Risk, and Compliance (GRC) Structure and Outline Initial Data and AI Policies
5. Consolidate and operationalize the Data & AI Operating Model
- Integrate organization, processes, technology, and governance, and use enterprise architecture to assess the technological fit with data and AI management and processes
- Promoting Operationalization, Process Acceptance, and Adoption—Getting People and the Organization On Board
- Conclusion: Refine your target operating model and prepare it for decision-making
This course offers you a digital blended concept that has been developed for part-time learning. Thanks to a flexible mix of online seminars and self-study phases, you are sure to reach your goal. This is how you learn in this course:
Learning environment: In your online learning environment, you will find useful information, downloads and extra services for this training course after you have registered.
Self-study phases: Learn independently, at your own pace and whenever you want. Our courses offer you didactically high-quality learning material.
Live webinars: In regular online seminars, you will meet your trainers in person. You will receive answers to your questions, specific assistance and instructions on how to deepen your knowledge and apply the skills you have acquired in practical exercises.
Learning Community: A digital learning community will be available to you throughout the course. trainers with other participants and the trainers , and ask questions.
Certificate of Completion and Open Badge: As a graduate of the class, you’ll receive a certificate of completion and an open badge, which you can easily share on professional networks (such as LinkedIn), among other places.
- You understand the framework and scope of a Data & AI Operating Model
- In capability mapping, you identify the right capabilities for implementing the AI and data strategy
- You know how to design an appropriate organizational structure
- You have a clear understanding of roles and responsibilities, as well as the interfaces with technologies and the IT architecture
- You design a process organization in a goal-oriented manner
- Ingovernance , yousee an element that provides a framework and security
- You will consolidate the Data & AI Operating Modeland put it into operation
- Using enterprise architecture, you'll assess the technology's fit with data and AI management as well as with processes
- Anyone who wants to tackle data- and AI-driven transformation at the decision-making level and create real business value through it
- Anyone with a basic understanding of data and business, as well as an in-depth understanding of at least one of these two areas
- Anyone with (professional) experience working on data projects in companies
- Customized training courses
- Direct application in practice
- Efficient use of time and resources
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Develop a data and AI strategy and embed it throughout your organization—with the "AI & Data Strategist" Master Class.
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