Enterprise Data Foundations for Generative and Agentic AI Training Course

Course Introduction

The Enterprise Data Foundations for Generative and Agentic AI Training Course provides an advanced framework for preparing enterprise data environments to support generative AI, agentic AI, intelligent automation, retrieval-augmented generation, and AI-driven decision systems. The programme focuses on the data architecture, governance, quality, integration, metadata, security, and operational capabilities required to make enterprise information reliable and usable by modern AI systems.

Generative and agentic AI applications depend heavily on the quality, accessibility, context, and governance of the data connected to them. Organisations may possess large volumes of information across databases, documents, applications, data warehouses, cloud platforms, knowledge repositories, and operational systems, yet still struggle to make that information AI-ready. This course addresses the architectural and management practices required to transform fragmented enterprise information into trusted foundations for intelligent applications.

Participants will explore modern data architectures supporting large language models, retrieval-augmented generation, AI agents, enterprise search, semantic retrieval, knowledge graphs, vector databases, structured data access, and multimodal AI. The course examines how these technologies interact with conventional enterprise data platforms and how organisations can build coherent information ecosystems rather than isolated AI experiments.

A major focus is placed on enterprise data quality, metadata, lineage, semantic consistency, master data, data products, access controls, privacy, security, and governance. Participants will learn how these capabilities influence AI reliability, retrieval accuracy, contextual relevance, explainability, compliance, and operational performance. Practical approaches will be used to identify and address weaknesses before data is exposed to generative or autonomous AI systems.

The programme also addresses the emerging requirements of agentic AI, where AI systems can plan tasks, retrieve information, invoke tools, interact with enterprise systems, and execute actions. Participants will examine the data foundations needed to support these workflows safely, including structured interfaces, permissions, real-time information, event streams, knowledge stores, audit trails, and human oversight mechanisms.

By the end of the course, participants will be able to assess enterprise data readiness for generative and agentic AI, design appropriate target-state architectures, establish governance and quality controls, and develop practical implementation roadmaps. They will gain the capability to connect enterprise data strategy with AI initiatives while creating scalable, secure, and sustainable foundations for intelligent enterprise transformation.

Duration

10 days

Who Should Attend

• Chief Data Officers and senior data executives responsible for enterprise data and AI transformation.

• Enterprise architects designing data platforms and technology environments for generative and agentic AI.

• Data architects responsible for developing scalable, secure, and AI-ready enterprise information architectures.

• Data engineers building ingestion, transformation, integration, streaming, and analytical data pipelines.

• AI architects designing enterprise generative AI, agentic AI, and intelligent application ecosystems.

• Machine learning and AI engineers working with enterprise data, models, retrieval systems, and AI applications.

• Data governance leaders responsible for enterprise data policies, standards, ownership, stewardship, and accountability.

• Data management professionals responsible for quality, metadata, cataloguing, lineage, master data, and lifecycle management.

• Cloud architects designing cloud, hybrid, and multi-cloud data foundations for AI workloads.

• Business intelligence and analytics professionals transitioning toward generative AI and intelligent analytics.

• Knowledge management professionals responsible for enterprise information discovery and contextual knowledge.

• Information architects developing business, semantic, conceptual, logical, and physical data models.

• Master data management specialists managing enterprise entities, reference data, and information consistency.

• Cybersecurity and privacy professionals responsible for protecting enterprise data used by AI systems.

• Digital transformation leaders implementing AI-enabled operating models and enterprise intelligence initiatives.

• Data product managers developing reusable, governed, business-oriented data products for AI consumers.

• IT managers overseeing enterprise data platforms, application integration, and AI infrastructure.

• Consultants and technology advisors supporting enterprise data modernisation and generative or agentic AI programmes.

Course Objectives

• Explain the data architecture principles and enterprise capabilities required to create reliable foundations for generative AI and agentic AI applications.

• Assess enterprise data environments for AI readiness by evaluating data quality, accessibility, governance, metadata, security, integration, lineage, and semantic consistency.

• Design modern data architectures that connect enterprise databases, documents, applications, cloud platforms, analytical environments, knowledge stores, and AI systems.

• Apply data preparation, transformation, enrichment, and quality management techniques that improve the reliability and contextual relevance of AI-generated outputs.

• Develop enterprise metadata and data cataloguing capabilities that enable AI systems and human users to discover, understand, govern, and reuse trusted information.

• Design retrieval architectures integrating structured data, unstructured content, vector stores, knowledge graphs, semantic layers, and enterprise search capabilities.

• Establish governance frameworks for enterprise data consumed by generative AI and agentic systems, including ownership, stewardship, access, accountability, and lifecycle controls.

• Apply master data, reference data, entity resolution, and semantic management practices to provide consistent enterprise context for AI applications.

• Design secure data access architectures that enforce identity, permissions, privacy, encryption, classification, monitoring, and policy controls across AI workloads.

• Evaluate the data requirements of agentic AI systems, including tool access, real-time data, event streams, structured interfaces, audit trails, and action permissions.

• Develop data readiness roadmaps that prioritise foundational capabilities, technology investments, governance improvements, and implementation activities for enterprise AI adoption.

• Integrate data architecture, governance, quality, security, retrieval, knowledge management, and operational capabilities into sustainable enterprise generative and agentic AI ecosystems.

Comprehensive Course Outline

Module 1: Foundations of Enterprise Data for Generative and Agentic AI

• Examine how generative AI and agentic AI change the requirements for enterprise data architecture, accessibility, quality, context, governance, and operational integration.

• Explore the relationship between enterprise data foundations, large language models, retrieval systems, AI agents, intelligent applications, and automated business processes.

• Assess the characteristics of AI-ready data, including accuracy, completeness, freshness, contextual richness, discoverability, interoperability, lineage, and controlled accessibility.

• Develop an enterprise AI data maturity perspective for identifying foundational weaknesses, capability gaps, architectural dependencies, and transformation priorities.

Module 2: Enterprise Data Architecture for AI Workloads

• Examine modern enterprise data architecture patterns supporting generative AI, agentic systems, analytics, machine learning, operational applications, and decision intelligence.

• Compare data warehouse, data lake, lakehouse, data fabric, data mesh, hybrid, and cloud-native architectures for AI-oriented enterprise environments.

• Design logical and physical architecture layers connecting source systems, ingestion, storage, processing, governance, knowledge, retrieval, analytics, and AI consumption.

• Apply architecture decision frameworks to select appropriate technologies and patterns according to enterprise scale, workload requirements, governance, and business objectives.

Module 3: AI-Ready Data Ingestion and Integration

• Design scalable ingestion architectures connecting databases, enterprise applications, APIs, SaaS platforms, documents, files, events, sensors, and external data sources.

• Examine ETL, ELT, change data capture, event streaming, APIs, data virtualisation, federation, and integration patterns for AI data ecosystems.

• Develop strategies for combining batch and real-time data to provide generative and agentic AI systems with current and historical enterprise context.

• Address integration challenges involving inconsistent schemas, fragmented systems, duplicated information, incompatible formats, legacy platforms, and changing data structures.

Module 4: Data Quality and AI Readiness

• Establish data quality frameworks covering accuracy, completeness, consistency, validity, uniqueness, timeliness, integrity, and fitness for AI applications.

• Examine how poor data quality can affect retrieval, model grounding, summarisation, recommendations, classification, prediction, and autonomous agent behaviour.

• Apply profiling, validation, anomaly detection, standardisation, cleansing, enrichment, and continuous monitoring techniques to improve enterprise data readiness.

• Develop AI-specific data quality controls that combine automated detection, human validation, business rules, statistical monitoring, and remediation workflows.

Module 5: Enterprise Metadata, Cataloguing and Data Discovery

• Design metadata architectures capturing technical, business, operational, semantic, lineage, quality, security, and governance information about enterprise data.

• Develop intelligent data catalogues that help users and AI applications discover relevant datasets, documents, knowledge assets, and trusted information sources.

• Explore active metadata approaches that support automated classification, recommendations, impact analysis, policy enforcement, quality monitoring, and AI-assisted data management.

• Establish enterprise metadata standards that improve contextual understanding and enable AI systems to interpret data assets consistently and responsibly.

Module 6: Semantic Data, Knowledge and Enterprise Context

• Examine semantic models, business glossaries, ontologies, taxonomies, knowledge graphs, and semantic layers as foundations for contextual enterprise AI.

• Design enterprise knowledge representations that connect entities, concepts, relationships, events, processes, documents, transactions, and business rules.

• Explore knowledge graph applications for enterprise search, contextual retrieval, relationship discovery, recommendation, reasoning, and intelligent decision support.

• Integrate structured enterprise semantics with generative AI systems to improve contextual grounding, consistency, traceability, and explainability.

Module 7: Vector Databases and Retrieval-Augmented Data Foundations

• Understand embeddings, vector representations, similarity search, indexing, ranking, and retrieval architectures used in enterprise generative AI applications.

• Design retrieval-augmented generation architectures connecting enterprise documents, structured data, metadata, knowledge graphs, vector stores, and language models.

• Examine document preparation, chunking, enrichment, embedding, indexing, retrieval, reranking, context construction, and source attribution processes.

• Establish governance, security, freshness, lineage, access control, and evaluation mechanisms for enterprise vector and retrieval environments.

Module 8: Data Foundations for Agentic AI

• Examine how agentic AI systems consume enterprise data, invoke tools, access services, coordinate tasks, and interact with operational business environments.

• Design data interfaces, APIs, event streams, structured tools, knowledge services, and controlled data access mechanisms for AI agents.

• Establish permission architectures that limit agent access according to identity, role, business context, task requirements, and authorised actions.

• Develop auditability and observability mechanisms for tracking agent data access, tool usage, decisions, actions, exceptions, and downstream consequences.

Module 9: Master Data, Entity Resolution and Reference Data

• Establish master data strategies for customers, employees, products, suppliers, assets, locations, accounts, and other critical enterprise entities.

• Apply entity resolution techniques to identify duplicate, fragmented, inconsistent, or conflicting representations of the same real-world entities.

• Develop reference data standards that provide consistent classifications, codes, hierarchies, categories, and controlled vocabularies across enterprise systems.

• Examine how trusted master and reference data improve AI grounding, personalisation, recommendations, analytics, automation, and enterprise decision-making.

Module 10: Data Governance for Generative and Agentic AI

• Develop governance frameworks defining data ownership, stewardship, accountability, policies, standards, access rights, usage controls, and escalation procedures.

• Establish governance mechanisms covering datasets, documents, prompts, retrieval sources, embeddings, knowledge stores, AI applications, and agent-accessible enterprise data.

• Apply data lifecycle, retention, classification, consent, privacy, provenance, lineage, and responsible-use principles to AI-enabled data environments.

• Design governance operating models that balance central enterprise standards with domain-level ownership, agility, innovation, and business accountability.

Module 11: Security, Privacy and Controlled AI Data Access

• Design security architectures covering identity, authentication, authorisation, encryption, network controls, privileged access, monitoring, and secure data exchange.

• Apply classification, masking, tokenisation, anonymisation, access policies, retention controls, and privacy safeguards to data consumed by AI systems.

• Address risks associated with sensitive information exposure through prompts, retrieval systems, vector stores, knowledge graphs, APIs, tools, and autonomous agents.

• Develop policy-based access controls that ensure AI systems receive only the data and permissions necessary for authorised tasks and business purposes.

Module 12: Data Products and AI Consumption Layers

• Design governed data products that package trusted enterprise information for analytics, AI applications, business processes, and decision-support use cases.

• Examine data product principles including ownership, documentation, discoverability, quality expectations, service levels, interfaces, security, and lifecycle management.

• Develop reusable data services and consumption layers that allow AI teams to access enterprise information without creating uncontrolled data duplication.

• Establish performance, usage, quality, lineage, and business-value measures for enterprise data products supporting generative and agentic AI.

Module 13: Real-Time Data, Events and Operational Intelligence

• Design event-driven data architectures that provide AI applications with timely information about transactions, activities, system states, and business events.

• Examine streaming data, event buses, message systems, change data capture, real-time processing, and event-driven integration for intelligent enterprise applications.

• Integrate real-time information with historical data and knowledge repositories to support context-aware AI decisions and agent workflows.

• Develop monitoring and resilience strategies for high-volume, low-latency data environments supporting continuously operating intelligent systems.

Module 14: Data Observability, Lineage and AI Reliability

• Establish end-to-end data lineage connecting source systems, transformations, datasets, retrieval stores, AI applications, analytical outputs, and business decisions.

• Implement data observability practices for monitoring freshness, volume, schema changes, distribution, quality, pipeline health, and data availability.

• Examine how data lineage and provenance contribute to trustworthy AI outputs, investigation, compliance, debugging, impact analysis, and responsible decision-making.

• Develop operational controls for detecting data drift, stale knowledge, broken pipelines, retrieval failures, unexpected changes, and degraded AI data quality.

Module 15: Enterprise AI Data Transformation Strategy

• Assess current enterprise data capabilities and develop structured roadmaps for improving AI readiness across architecture, governance, quality, security, integration, and knowledge management.

• Establish transformation priorities based on business requirements, AI use cases, technical dependencies, regulatory obligations, data maturity, and organisational capability.

• Develop target-state architectures, transition architectures, implementation phases, investment priorities, operating models, and measurable transformation milestones.

• Design sustainable collaboration models connecting data teams, AI teams, enterprise architecture, cybersecurity, governance, business domains, and executive leadership.

Module 16: Generative and Agentic AI Data Foundations Capstone

• Develop a complete enterprise data foundation architecture supporting generative AI, retrieval-augmented generation, agentic workflows, analytics, and intelligent business applications.

• Design integrated data flows covering ingestion, quality, governance, metadata, semantic context, retrieval, vector storage, knowledge services, security, and AI consumption.

• Create an implementation roadmap covering technology architecture, governance, operating models, data products, security controls, observability, and organisational capability development.

• Present an enterprise AI data foundation blueprint demonstrating scalability, trustworthiness, security, interoperability, operational resilience, and readiness for future AI capabilities.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808

Training Venue 

The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.

Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Upskill certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808

Terms of Payment:

Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.