Understanding IT: Technology Fundamentals for Product Owners and Business Analysts

Why take this course

During meetings, terms like API, cache, queue, deployment, and rollback come up all the time, and you know that each of them can affect scope, cost, or delivery timelines. After two days, you will be able to trace the journey of a feature from a user click to production deployment and learn how to ask the right questions to your technical team.

You will also explore where AI appears in modern applications and what questions to ask about data, response quality, and human oversight. No coding, no pretending to be an architect, and no need to keep a technical glossary open in another browser tab.

Why take this course

After completing the training, you will be able to:

• Draw a simple application map covering the frontend, backend, APIs, database, and external integrations.
• Trace the flow of requests and data from a user action to the system response.
• Read basic architecture and integration diagrams and identify dependencies between components.
• Distinguish between APIs, synchronous integration, asynchronous communication, batch processing, queues, and events.
• Consider requirements related to performance, availability, security, data management, and monitoring.
• Navigate the software development lifecycle, from repositories and environments through CI/CD, deployment, and rollback.
• Interpret basic information about tests, defects, logs, metrics, alerts, and incidents.
• Understand fundamental AI concepts and explain the role of models, data, and integrations in AI-powered applications.
• Ask informed questions about AI response quality, data access, human oversight, and system behavior in failure scenarios.
• Translate business needs into requirements that account for technical constraints, dependencies, and validation approaches.

Who is this course for

This training is ideal if you:

• Work as a Product Owner, Product Manager, or Business Owner and regularly make decisions that influence IT solutions.
• Are a Business Analyst, Process Analyst, or Domain Specialist looking to collaborate more effectively with developers, testers, and architects.
• Manage digital projects and need a better understanding of dependencies, risks, and the technical consequences of change.
• Are transitioning from a purely business role into a product or technology team.
• Recognize many IT concepts from meetings but struggle to connect them into one coherent picture.

This training is not for you if:

You are looking to learn programming, infrastructure configuration, cloud administration, or a level of technical detail intended for experienced developers and architects.

Topics covered

Module 1. From Click to Response: The Anatomy of an Application

After this module, you will be able to: map the key elements of a digital product, explain how they work together when processing user actions, and identify where AI-powered features fit into the application architecture.

• Web applications, mobile applications, and backend systems.
• Frontend, backend, servers, databases, and caches.
• Client-server architecture and the request-response model.
• HTTP, URLs, methods, response statuses, and JSON from a business perspective.
• Where applications run: user devices, servers, cloud environments, and on-premises infrastructure.
• Fundamentals of Artificial Intelligence (AI), Machine Learning (ML), Generative AI, and Large Language Models (LLMs).
• Models versus applications.
• Model training versus model usage.
• Prompts and context.
• Practical exercise: tracing a user’s click through the system, including an AI-powered feature.

Module 2. How Systems Communicate: APIs, Integrations, and Events

After this module, you will be able to: read a simple integration map, identify dependencies, and spot potential points of failure.

• APIs, endpoints, contracts, and versioning.
• Synchronous and asynchronous integrations.
• Queues, messages, events, webhooks, and batch processing visualized on a single data flow map.
• Monoliths and microservices illustrated through the impact of a single business change.
• Change compatibility and dependencies on external systems.
• Practical exercise: integration mapping and “what could go wrong?” analysis, including AI services, data transfer, response times, usage costs, and service unavailability.

Module 3. Data, Identity, and Security: What You Really Need to Know

After this module, you will be able to: ask the right questions about data, access control, and security before requirements enter implementation.

• Tables, records, relationships, and basic data models.
• Relational and non-relational databases: fundamental differences and questions to ask when choosing between them, without database design.
• Source of truth, caching, copies, and data synchronization.
• Corporate knowledge bases as AI sources: information retrieval, providing context to models (RAG), source freshness, and access permissions.
• Authentication and authorization.
• Sessions, tokens, roles, and permissions.
• Data encryption in transit and at rest.
• Data minimization, auditing, and operational traceability.

Module 4. How Changes Reach Production: From Code to Deployment

After this module, you will be able to: describe the journey of a change from development to safe release.

• Repositories, branches, commits, pull requests, and code reviews using a single change scenario.
• Development, testing, UAT, staging, and production environments.
• Builds, pipelines, CI/CD, and deployment.
• Data migrations and deployment dependencies.
• Feature flags, gradual rollouts, and rollback strategies.
• Changes to AI models, prompts, or knowledge sources: validation and controlled release.
• Versioning for web applications, mobile applications, and APIs.

Module 5. When Things Go Wrong: Testing, Monitoring, and Incidents

After this module, you will be able to: understand basic testing approaches and actively participate in discussions about defects, performance issues, and incidents.

• Unit testing, integration testing, end-to-end testing, and UAT.
• Integration reliability and AI response quality: source fidelity, completeness, and behavior when information is missing or contradictory.
• Errors, defects, incidents, and problems.
• Logs, metrics, traces, alerts, and dashboards.
• Availability, performance, scalability, and resilience.
• SLAs, SLOs, and key service quality indicators.
• Technical debt and its impact on product evolution.

Module 6. From Business Need to a Buildable Solution

After this module, you will be able to: prepare requirements that address not only functionality but also data, dependencies, risks, deployment, and ongoing maintenance.

• Functional and non-functional requirements.
• Constraints, assumptions, dependencies, and risks.
• Acceptance criteria and outcome measurement.
• Questions for developers, testers, architects, and support teams.
• Impact analysis on integrations, data, and existing users.
• Deployment, monitoring, and rollback planning.
• Requirements for AI-powered features: information sources, quality criteria, human approval, and fallback processes without AI.
• Case study: from a short business brief to a complete solution map, including a simple AI-enabled feature.