Data & Analytics Strategy: This artifact serves as the foundational plan for all data initiatives within an organization. It defines the long-term vision and mission for how data will be used to create business value. It sets strategic objectives that align data activities with broader organizational goals, such as increasing revenue, optimizing operations, or improving customer experience.

Roadmap: A roadmap provides a high-level, phased plan that translates the data strategy into actionable steps. It outlines key initiatives, their timelines, and the dependencies between them. This artifact helps manage expectations and resources by providing a clear, sequential view of how the organization will achieve its data-related objectives.

Data Governance Framework: A governance framework establishes the policies, roles, and processes for managing data as a valuable asset. It defines who is responsible for data (data stewards, owners), what rules govern its use (policies), and how decisions are made about it. This structure ensures data is handled consistently and responsibly across the organization.

Data Ethics & AI Principles: This document outlines the ethical guidelines for data collection, usage, and the development of AI systems. It addresses critical concerns such as privacy, fairness, and transparency, ensuring that data practices are not only legally compliant but also morally sound. These principles guide data professionals in making responsible decisions that prevent unintended harm.

Data & Report Glossary: A glossary is a centralized repository of business-friendly definitions for key terms, metrics, and reports. It acts as a single source of truth, eliminating confusion and ensuring that everyone in the organization is using the same language and understanding for critical business concepts like "customer lifetime value" or "monthly active users."

User Personas & Classifications: This artifact defines the different types of data users within an organization and their specific needs. By creating detailed personas, it guides the design of data solutions, access policies, and training programs, ensuring that data is accessible and useful to the right people while maintaining security.

Logical & Physical Data Models: Data models are visual blueprints that define the structure of data. A logical data model represents data concepts and their relationships in a business-friendly way, without specifying the underlying technology. A physical data model translates this into a technical design, showing how data is physically implemented in a database, including table structures, data types, and indexes.

Data Dictionary: A data dictionary provides detailed definitions for every field or column in a dataset. For each field, it specifies its data type (e.g., text, number), business meaning, and any validation rules. This artifact is essential for developers and analysts to understand the content and constraints of the data they are working with.

Data Lineage Maps: Data lineage maps trace the flow of data as it moves through systems. They show the data's journey from its source, through various transformations (e.g., calculations, aggregations), to its final destination (e.g., a dashboard or report). These maps are crucial for debugging data quality issues, understanding dependencies, and ensuring compliance.

Data Catalog & Metadata: A data catalog is a searchable inventory of all data assets within an organization. It provides metadata—data about the data—including descriptions, owners, refresh schedules, and tags. This makes it easier for users to discover, understand, and trust the data they need for analysis.

Source-to-Target Mappings: This artifact provides detailed, field-level instructions for data integration processes, such as Extract, Transform, Load (ETL) or Extract, Load, Transform (ELT). It specifies exactly how data from a source system should be transformed and mapped to a target system, ensuring consistency and accuracy in data pipelines.

Technical Architecture & Tool Portfolio: This document outlines the technology stack and components used for data and analytics solutions. It specifies the tools, platforms, and standards for everything from data storage and processing to business intelligence and machine learning. This ensures a cohesive and scalable technology environment.

Data Quality Standards & Rules: This artifact defines the criteria for high-quality data. It establishes metrics to assess data's accuracy, completeness, consistency, timeliness, and validity. These standards provide a benchmark against which data can be measured and improved, preventing errors and ensuring reliability.

Data Quality Dashboards: These dashboards provide a continuous view of data quality metrics over time. They highlight issues, show trends, and monitor compliance with data quality standards. By visualizing data quality, these dashboards enable teams to proactively identify and address problems before they impact business decisions.

Data Classification Matrix: A data classification matrix categorizes data based on its sensitivity. For example, it might classify data as public, internal, confidential, or restricted, with specific rules for personally identifiable information (PII) or protected health information (PHI). This matrix is fundamental for guiding security and access control policies.

Access Control Policies: These policies define the rules and roles that govern who can access specific data sets, dashboards, or models. They are based on the data classification matrix and ensure that only authorized users can view, modify, or use sensitive data, thereby protecting against data breaches and misuse.

Audit Trail Records: Audit trails are logs that record sensitive actions, such as data access, queries, or changes to a model. They provide a detailed history of "who did what, when," which is critical for ensuring compliance with regulations and for security investigations in case of a data breach.

KPI & Metric Definitions: This is a standardized document that provides the official formula, owner, and business context for each key performance indicator (KPI) and metric. It ensures that everyone in the organization is calculating and interpreting metrics consistently, preventing different teams from reporting conflicting numbers for the same metric.

BI Dashboard & Report Inventory: This artifact is a comprehensive list of all published dashboards and reports. It includes details such as their purpose, target audience, and ownership. This inventory helps prevent the creation of duplicate reports and allows users to easily discover existing resources.

SQL Query Library & Templates: This is a repository of modular, reusable code snippets for common data queries and analysis tasks. By providing standardized templates, this artifact ensures consistency in analysis, reduces redundant work, and helps maintain a high standard of code quality across the team.

Analytics Standards & Templates: These guidelines define best practices for data visualization, dashboard layouts, and analytical modeling. They ensure that all analytical outputs, from reports to presentations, follow a consistent style and quality. This makes information easier to understand and promotes a professional brand for the analytics team.

Model Cards: A model card is a detailed documentation for a machine learning model. It provides essential information such as the model's purpose, details about the training data used, and its performance metrics. This artifact promotes transparency and helps users understand the model's capabilities and limitations.

Feature Store Catalog: This catalog is an inventory of reusable features for machine learning models. It provides definitions, sources, and usage statistics for each feature, allowing data scientists to discover and share pre-processed data efficiently. This prevents redundant feature engineering and ensures consistency across models.

Bias & Explainability Reports: These reports document the analysis of a model's fairness and interpretability. They use visualizations (like SHAP plots) and statistical tests to show how the model makes decisions and whether it exhibits bias toward certain demographic groups. These reports are crucial for building trust in AI systems and ensuring ethical deployment.

Prompt Templates (for LLMs): For large language models (LLMs), prompt templates are standardized, reusable formats for prompts. They are designed to ensure consistent and effective interactions with generative AI systems. By using templates, teams can achieve predictable outputs and streamline workflows.