Given your knowledge of {{topic}} in the {{vertical}} market, please perform the following tasks:
- Generate Themes: Develop {{n}} top-level, outcome-focused themes in the context of the CMMI framework, ensuring they align with specific capabilities related to the topic. Make these themes MECE and provide a brief explanation for each one. They do not need to be numbered.
- Generate Capabilities for each Theme: For each theme, list out a comprehensive set of no less than 8 capabilities that cover all aspects of {{theme}} within {{topic}} for {{vertical}}, using the CMMI framework. Each capability should have a name, in bold. Do not precede the name with any descriptor. Remember to ensure minimal overlap among the capabilities. Classify each capability under one of the following groups (in order): Strategy, Operations, and Reporting & Analytics. Each of these groups should be considered for each theme. Provide a brief explanation for each capability, starting with "Ability to quickly and accurately...". Output as an ordered list in markdown.
- Generate Maturity Levels for each Capability: For each capability, develop 5 capability maturity levels within the context of the theme, making sure each level provides a clear progression in terms of sophistication or ability. Avoid generic level names like “Ad Hoc” or “Quantitatively Managed” and instead provide descriptive titles. Accompany each maturity level with a brief explanation. Also include an illustrative example as a closing sentence. Output as an nested list in markdown.
Please present your output in the following hierarchy and format using this example data:
Data Governance: This theme ensures that all data within the commercial bank are managed as valuable resources. It includes outcomes such as effective data policies, data quality standards, metadata management, data privacy, and security.
- Data Governance Strategy (Strategy): Ability to quickly and accurately define and implement the bank's data governance strategy. This involves setting the rules and procedures for data management, data quality, and data security, as well as clearly defining roles and responsibilities around data governance.
- Foundational Governance - At this initial level, a commercial bank formulates its data governance strategy, laying down the initial framework that includes the identification of key data stakeholders, an understanding of the current data landscape, and setting the foundation for a data governance council. This level serves as the blueprint for the bank's data governance journey. Example: A commercial bank establishes a data governance council consisting of key stakeholders from different business units and IT.
- Policy Establishment - This level sees the bank develop and implement data governance policies that outline the standards for data quality, data protection, data privacy, and data usage. These policies are crucial in standardizing how data is collected, stored, managed, and used across the organization. Example: The bank enacts a data quality policy that defines the metrics for measuring data quality and outlines the processes for data cleansing.
- Operational Integration - The third level involves integrating data governance strategy into the bank's daily operations. This means that data governance isn't just a set of guidelines, but an ingrained part of how the bank operates. Compliance with data governance policies is monitored and deviations are promptly addressed. Example: The bank integrates its data quality policy into the data entry process, ensuring that all newly input data meets the standards set in the policy.
- Governance Evolution - At this level, the bank has matured its data governance strategy to the point that it is able to adapt to changes in the business environment, regulatory landscape, or organizational goals. The data governance strategy is regularly reviewed and updated, and continuous improvement is a key focus. Example: As privacy regulations change, the bank promptly updates its data governance strategy to comply with new requirements.
- Advanced Governance Adaptability - The final level sees the bank mastering the ability to leverage its mature data governance strategy for competitive advantage. Governance becomes predictive, with data-driven insights informing strategic decisions. The organization exhibits a high level of adaptability to changes in technology, business environment, or regulations. Example: The bank uses insights from data governance metrics to enhance customer experience, streamline operations, and optimize regulatory compliance processes.
- Capability 2 (grouping)
- ...
Theme 2: Explanation
...
Theme {{n}}: Explanation
Please output in markdown format. Please output the complete model
Vertical: Topic: Theme: [insert generated output] Capability: [insert generated output] n: