Capability Model - Loop

Act as an expert in building capability models using the CMMI framework. You are tasked with creating a 3-level hierarchical capability maturity model. This will require you to iterate through the following instructions like a For Each loop. Given what you know about {{topic}} in the {{vertical}} market, write a list of {{n}} top-level themes that specific capabilities will align to using the CMMI framework. Make them outcome-focused. Make them MECE. Think step by step. Explain step by step. Think of this as a set of column headers in a table. They do not need to be numbered.

Theme output example:

Data Governance: This theme ensures that all data within the organization are managed as valuable resources. It includes outcomes such as effective data policies, data quality standards, metadata management, data privacy, and security.

Data Infrastructure and Technology: This focuses on the technical capabilities required to manage data effectively. Outcomes in this category include the establishment and maintenance of robust, scalable data storage and processing systems, effective data integration solutions, and advanced analytics tools.

Etc.

For each theme you generate, generate a complete list of capabilities that comprehensively cover all aspects of {{theme}} when {{topic}} for {{vertical}} using the Capability Maturity Model Integration (CMMI) framework. The capabilities should be distinct with minimal overlap.

I also want you to create groupings by Strategy, Operations, and Reporting & Analytics - and they should be listed in that order. Do not consider the ‘n’ variable, output a complete set of capabilities. Think of these as row groups that are independent of the themes. Do not actually group the output. Indicate the grouping immediately after the capability name in parentheses. The capability name should be concise, and in bold formatting. Explain each capability step by step. The explanation (not the capability name) should be in the form of "Ability to quickly and accurately <explanation>"

Begin example capability output format for the Data Governance theme example above:

1. 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.
2. Data Governance Policy (Strategy): Ability to quickly and accurately develop, revise, and implement data governance policies. These policies govern the use, collection, storage, and disposal of data in compliance with legal and ethical standards.
3. Data Risk Management (Strategy): Ability to quickly and accurately identify and assess risks associated with data management and governance, and develop strategies to mitigate those risks.
4. Data Stewardship (Operations): Ability to quickly and accurately designate data stewards who are responsible for enforcing data governance policies and procedures. This involves ensuring that data is properly classified, stored, used, and protected.
5. Data Quality Management (Operations): Ability to quickly and accurately maintain high levels of data quality in all systems. This includes regularly reviewing and cleaning data, identifying and fixing issues, and implementing quality control processes.
6. Data Lifecycle Management (Operations): Ability to quickly and accurately manage the lifecycle of data from creation or collection to disposal. This includes processes for data storage, archiving, and deletion, in line with data governance policies.
7. Data Security and Compliance (Operations): Ability to quickly and accurately manage data security and compliance. This includes processes for access control, data protection, and adherence to data privacy laws and regulations.
8. Data Reporting (Reporting & Analytics): Ability to quickly and accurately generate reports based on the bank's data. This involves accessing and analyzing data to produce insights that support decision-making.
9. Data Analysis and Insight Generation (Reporting & Analytics): Ability to quickly and accurately analyze data to generate insights. This includes identifying patterns and trends in the data that can inform strategic and operational decisions.
10. Data Governance Performance Monitoring (Reporting & Analytics): Ability to quickly and accurately monitor and report on the performance of data governance processes. This involves regularly reviewing data quality, data security, and compliance metrics, and making improvements where necessary.

End Example Capabilities

Generate results in a nested ordered list beneath the respective theme. Always output in markdown.

For each capability you generate, generate 5 capability maturity levels for {{capability}} within the {{vertical}} market with regard to {{topic}} in the context of {{theme}}. Give each capability name meaning beyond words like "Initial", "Ad Hoc", or "Quantitatively Managed". Explain each capability with concise language that is common in this activity that focuses on what must be accomplished to successfully achieve each level. Place a dash between the maturity level name and the explanation. Make the maturity level name bold. Include an example as a closing sentence. Output as a nested ordered list beneath each capability. Use the Capability Maturity Model Integration (CMMI) framework for the levels.

Here is an example for the media and entertainment vertical, on the topic of creating a personalized streaming experience for the capability of data storage and management. This is exactly the output I expect from you. Note: the example is always in italics.

Begin maturity example:

1. Basic Data Storage - The first level of data storage and management capability involves setting up a centralized data repository, such as a relational database, to store and manage essential user data, including demographic information, content preferences, and browsing history. This provides media companies with a foundation for data-driven decision-making and platform improvements. Example: A media platform uses a relational database to store user profile data and content consumption history.
2. Scalable Storage Infrastructure - At this level, media companies enhance their data storage and management capabilities by implementing scalable storage infrastructure, such as cloud-based databases or distributed data storage systems. This ensures that the platform can handle growing data volumes and user demands. Example: The platform migrates to a cloud-based database to accommodate its expanding user base and increasing data storage needs.
3. Data Security and Access Control - The third level involves incorporating robust data security measures, such as encryption, access controls, and data backup strategies, to protect user data from unauthorized access, loss, or corruption. This ensures a secure and trustworthy platform for users and reduces the risk of data breaches. Example: The platform implements data encryption at rest and in transit, as well as role-based access controls, to protect sensitive user information.
4. Data Lifecycle Management - In this stage, media companies implement data lifecycle management processes, including data retention, archiving, and deletion policies, to ensure that user data is managed efficiently and in compliance with data protection regulations. This optimizes storage resources and reduces compliance risks. Example: The platform establishes data retention policies to automatically archive or delete user data after a specified period, in accordance with GDPR requirements.
5. Intelligent Data Storage Optimization - The final level of data storage and management maturity involves creating a fully adaptive and intelligent data storage system that continuously learns from user behavior, platform operations, and external data sources. This system refines its storage and management processes and algorithms to optimize data storage efficiency, security, and compliance while accommodating the evolving needs of the platform and its users. Example: The platform uses AI-driven techniques to identify and predict storage capacity needs, proactively adjusting storage infrastructure and data lifecycle policies to ensure optimal performance and resource utilization.

End maturity example

Always output in markdown.

Begin hierarchy format example. The output should be in the following nested list format:

Theme 2: Explanation

Theme 3: Explanation

…

Theme {{n}}: Explanation

Here is an actual output example of a single theme, capability, maturity combination:

Data Governance: This theme ensures that all data within the organization are managed as valuable resources. It includes outcomes such as effective data policies, data quality standards, metadata management, data privacy, and security.

1. 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.
1. 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.
2. 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.
3. 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.
4. 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.
5. 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.

End example

Make sure maturity levels are nested beneath each capability. Make sure all the 3 levels our outputted completely. Produce (fill out) the entire model, not just an example. Provide full maturity levels for each capability, not just an example.

Vertical: Topic: Theme: [insert generated output] Capability: [insert generated output] n: