Course Introduction
The Agentic Data Management and Intelligent Data Operations Training Course provides a practical and strategic foundation for organizations seeking to transform conventional data management into intelligent, autonomous, and outcome-driven operations. The course explores how agentic artificial intelligence, intelligent automation, data engineering, governance, and operational intelligence can work together to improve the way organizations collect, manage, protect, analyze, and use data.
Modern organizations generate data at unprecedented speed and scale, yet many continue to struggle with fragmented platforms, inconsistent data quality, manual processes, governance gaps, and slow operational decision-making. This course examines how agentic approaches can address these challenges by enabling intelligent systems to monitor data environments, identify issues, recommend actions, coordinate workflows, and support continuous optimization while maintaining appropriate human oversight.
Participants will gain an in-depth understanding of the architecture and operating principles behind agentic data management. The program examines intelligent data agents, autonomous workflows, metadata intelligence, data observability, data quality management, master and reference data, data integration, and AI-enabled operational processes. Emphasis is placed on connecting these capabilities to measurable business outcomes rather than treating artificial intelligence as an isolated technology initiative.
The course also addresses the governance, security, ethical, and operational implications of deploying autonomous or semi-autonomous data systems. Participants will explore issues such as agent permissions, accountability, explainability, privacy, model risk, data lineage, hallucinations, unauthorized actions, bias, regulatory compliance, and human-in-the-loop controls. These considerations are essential for organizations that want to adopt intelligent data operations responsibly and at enterprise scale.
Through practical frameworks, real-world scenarios, strategic discussions, and implementation-oriented exercises, participants will learn how to assess organizational readiness and identify high-value opportunities for intelligent data operations. The course covers both foundational concepts and emerging developments, including multi-agent architectures, AI-powered data platforms, semantic layers, synthetic data, real-time intelligence, autonomous data quality remediation, and intelligent data products.
By the end of the program, participants will be better equipped to design, govern, implement, and continuously improve agentic data management capabilities. They will understand how to move from fragmented and reactive data operations toward proactive, intelligent, automated, and resilient environments that improve data reliability, accelerate decision-making, reduce operational costs, strengthen governance, and create sustainable competitive advantage.
Duration
5 days
Who Should Attend
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Chief Data Officers and senior data executives responsible for enterprise-wide data strategies and transformation.
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Data governance managers seeking to modernize governance through artificial intelligence and intelligent automation.
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Data managers and data stewards responsible for data quality, ownership, standards, and operational accountability.
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Data engineers and architects designing scalable, automated, and intelligent enterprise data environments.
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AI and machine learning professionals developing agentic systems that interact with organizational data platforms.
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Business intelligence and analytics professionals seeking more reliable, timely, and intelligent data operations.
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IT managers and technology leaders responsible for enterprise platforms, integration, automation, and digital transformation.
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Enterprise architects evaluating agentic architectures, intelligent platforms, and next-generation data operating models.
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Information security and privacy professionals addressing risks associated with autonomous data processing systems.
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Compliance and risk professionals responsible for responsible AI, regulatory compliance, and data management controls.
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Data quality specialists seeking advanced approaches to automated monitoring, detection, remediation, and prevention.
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Database administrators and platform specialists managing increasingly automated and AI-enabled data environments.
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Digital transformation leaders evaluating opportunities to use intelligent agents to improve operational efficiency.
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Project and program managers leading data modernization, AI adoption, governance, or intelligent automation initiatives.
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Consultants and advisors supporting organizations with data strategy, AI transformation, governance, and operating models.
Course Objectives
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Explain the fundamental principles of agentic artificial intelligence and demonstrate how autonomous agents can enhance enterprise data management and operational processes.
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Evaluate existing data management environments to identify operational bottlenecks, automation opportunities, governance weaknesses, quality issues, and high-value agentic use cases.
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Design intelligent data operations architectures that integrate data platforms, AI agents, automation frameworks, metadata, governance controls, and human oversight mechanisms.
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Apply advanced data quality practices using intelligent monitoring, anomaly detection, root-cause analysis, automated remediation, and continuous improvement techniques.
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Develop effective strategies for metadata management, data lineage, semantic understanding, and context engineering to improve the performance of intelligent data agents.
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Establish governance frameworks that control agent permissions, decision-making authority, accountability, transparency, auditability, privacy, security, and responsible autonomous operations.
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Assess emerging agentic data technologies and determine how multi-agent systems, intelligent data products, real-time analytics, and AI-enabled platforms can support organizational objectives.
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Develop practical approaches for automating repetitive data operations while maintaining appropriate human-in-the-loop controls for sensitive, high-risk, or business-critical decisions.
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Create implementation roadmaps that align intelligent data operations initiatives with business priorities, organizational capabilities, technology architecture, risk requirements, and measurable performance indicators.
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Measure the business impact of intelligent data operations through operational efficiency, data quality, decision speed, cost reduction, resilience, compliance improvement, and stakeholder value metrics.
Comprehensive Course Outline
Module 1: Foundations of Agentic Data Management and Intelligent Operations
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Understanding the evolution from traditional data management toward intelligent, autonomous, and outcome-oriented data operating models.
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Core concepts of agentic AI, intelligent agents, autonomy levels, reasoning, planning, tool use, memory, and goal-oriented data operations.
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Business drivers, strategic opportunities, limitations, and organizational challenges associated with adopting agentic data management capabilities.
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Distinguishing agentic data operations from conventional automation, analytics, machine learning, robotic process automation, and generative AI solutions.
Module 2: Agentic Data Architecture and Intelligent Operating Models
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Designing enterprise architectures that connect intelligent agents with data platforms, applications, APIs, metadata services, governance systems, and operational workflows.
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Understanding single-agent, multi-agent, hierarchical-agent, event-driven, and collaborative architectures for complex data management environments.
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Developing intelligent data operating models that define responsibilities across people, agents, platforms, processes, controls, and business stakeholders.
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Addressing interoperability, scalability, resilience, observability, orchestration, and lifecycle management requirements for enterprise agentic architectures.
Module 3: Intelligent Data Integration, Engineering, and Pipelines
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Applying intelligent agents to automate data ingestion, transformation, integration, pipeline monitoring, dependency management, and operational troubleshooting.
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Designing adaptive data pipelines capable of responding intelligently to changing schemas, data sources, workloads, business requirements, and operational conditions.
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Using AI-assisted data engineering to accelerate pipeline development, documentation, testing, optimization, incident diagnosis, and maintenance activities.
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Managing real-time, batch, streaming, event-driven, and hybrid integration patterns within intelligent and increasingly autonomous data environments.
Module 4: AI-Powered Data Quality and Data Observability
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Establishing intelligent data quality frameworks that continuously detect anomalies, inconsistencies, missing values, duplication, drift, and unusual operational patterns.
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Applying agentic techniques to investigate data quality incidents, identify probable root causes, recommend corrective actions, and automate approved remediation processes.
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Integrating data observability with lineage, metadata, monitoring, alerting, service-level objectives, and business impact analysis for proactive data operations.
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Exploring emerging approaches to autonomous data quality management, including predictive quality controls, adaptive rules, intelligent validation, and continuous learning.
Module 5: Metadata, Lineage, Semantics, and Context Intelligence
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Understanding metadata as a critical source of context that enables intelligent agents to interpret data structures, relationships, ownership, meaning, and business relevance.
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Building comprehensive data lineage capabilities that support impact analysis, troubleshooting, governance, compliance, and trustworthy agentic decision-making.
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Developing semantic layers, business glossaries, knowledge graphs, and contextual representations that improve data discovery and machine understanding.
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Examining context engineering techniques for providing agents with accurate, relevant, current, and appropriately governed information during data operations.
Module 6: Intelligent Data Governance, Security, and Responsible AI
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Designing governance frameworks for autonomous and semi-autonomous agents, including authorization, accountability, approval thresholds, audit trails, escalation, and human oversight.
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Managing privacy, confidentiality, data access, identity, security controls, prompt risks, unauthorized actions, data leakage, and adversarial threats affecting intelligent data operations.
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Addressing responsible AI issues involving transparency, explainability, fairness, hallucination management, model risk, accountability, and appropriate use of autonomous decision-making.
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Establishing continuous governance and compliance monitoring capable of adapting to changing regulations, organizational policies, technology risks, and emerging AI requirements.
Module 7: Agentic Automation and Intelligent Data Operations
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Identifying high-value data operations suitable for intelligent automation, including incident management, monitoring, reconciliation, reporting, classification, and routine administration.
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Designing agentic workflows that can observe operational conditions, reason about potential actions, execute approved tasks, evaluate outcomes, and escalate exceptions.
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Applying human-in-the-loop and human-on-the-loop models to balance automation efficiency with appropriate control over high-impact and sensitive data decisions.
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Measuring automation effectiveness through operational resilience, response times, error reduction, productivity improvements, cost efficiency, and service reliability.
Module 8: Advanced Analytics, Intelligent Data Products, and Decision Support
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Exploring how agentic systems can transform curated data into intelligent data products designed around specific business users, decisions, processes, and measurable outcomes.
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Integrating agents with business intelligence, predictive analytics, natural language interfaces, decision intelligence, and real-time operational intelligence capabilities.
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Designing trustworthy agent-assisted decision workflows that combine organizational data, business rules, analytical models, contextual knowledge, and appropriate human judgment.
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Examining emerging intelligent data product models that continuously adapt to changing user requirements, data conditions, business priorities, and operational environments.
Module 9: Emerging Technologies, Trends, and Industry Issues
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Exploring multi-agent systems, agent-to-agent collaboration, autonomous orchestration, tool-using agents, and emerging protocols for coordinating intelligent data workflows.
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Examining vector databases, retrieval-augmented generation, knowledge graphs, multimodal data, semantic architectures, and context-aware systems supporting advanced data intelligence.
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Assessing synthetic data, privacy-enhancing technologies, confidential computing, federated approaches, and other emerging techniques for secure and scalable data utilization.
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Evaluating emerging regulatory, workforce, ethical, cybersecurity, vendor, infrastructure, and organizational issues that may influence the future of agentic data operations.
Module 10: Implementation Strategy, Optimization, and Future Readiness
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Developing a practical roadmap for moving from experimentation and pilot projects toward governed, scalable, measurable, and enterprise-wide agentic data operations.
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Establishing readiness assessments, use-case prioritization frameworks, business cases, investment models, capability requirements, and implementation milestones for intelligent data programs.
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Defining performance indicators for measuring agent accuracy, reliability, autonomy, operational value, governance effectiveness, user adoption, and business impact.
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Building a future-ready intelligent data strategy that supports continuous innovation, workforce transformation, technology evolution, regulatory change, and sustainable organizational value.
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.
