Oracle Analytics Cloud Self Service BI Training Course

Course overview

Business users and analysts are often expected to answer operational questions without waiting for a central BI team to build every report. In Oracle Analytics Cloud (OAC), this means being able to locate trusted datasets, prepare data responsibly, create governed visualisations, and share findings without duplicating metrics or exposing restricted information. This course addresses the gap between consuming dashboards and independently producing reliable self-service analysis that managers can use for decisions on sales, finance, operations, customer, or workforce performance.

Participants learn the Oracle Analytics Cloud workflow from connection and dataset creation through data preparation, visual analysis, dashboard design, and publication. They work with data flows, joins, calculated measures, semantic-model concepts, filters, parameters, drill interactions, and narrative insights. The course also covers the practical controls that make self-service BI dependable: certified datasets, folder permissions, role-aware sharing, data refreshes, and validation of totals against source data. Participants gain the ability to turn a business question into an interactive analysis with documented assumptions and reusable metrics.

Delivery combines instructor demonstrations with guided build exercises in an Oracle Analytics Cloud environment. Each day uses a connected business scenario, requiring participants to prepare data, build visualisations, test calculations, and explain findings to stakeholders. By the end of the week, each participant produces an OAC self-service BI pack: a prepared dataset or data flow, an interactive dashboard, defined calculations and filters, and a short handover note describing data sources, refresh expectations, access requirements, and business decisions supported.

The course is suited to analysts, reporting professionals, subject-matter experts, and BI practitioners who need practical OAC authoring skills rather than database administration or enterprise data-warehouse development expertise.

Course objectives

By the end of this course, participants will be able to:

• Create Oracle Analytics Cloud datasets from approved files, database connections, and subject areas
• Prepare and enrich analytical data using joins, transformations, calculated columns, and data flows
• Build interactive OAC visualisations using appropriate charts, pivots, tables, maps, and KPI tiles
• Define reusable calculations, measures, reference lines, and conditional formatting for business analysis
• Configure dashboard filters, prompts, parameters, drill paths, and canvas interactions
• Validate dashboard totals and calculations against source data and agreed business definitions
• Publish and share governed analyses using OAC folders, permissions, certified content, and refresh settings
• Produce a documented self-service BI dashboard pack for a defined business decision

Benefits of attending

Target competencies

Who should attend

• Business Intelligence Analysts — who need to build and publish governed self-service dashboards in Oracle Analytics Cloud
• Data Analysts — who prepare business data and convert it into interactive analysis for operational stakeholders
• Reporting Analysts — who are moving from static reports and spreadsheets to reusable OAC visualisations
• Finance Analysts — who need controlled variance, budget, forecast, and performance reporting
• Operations Analysts — who monitor service, supply chain, workforce, or process metrics through dashboards
• Business Subject-Matter Experts — who need to answer departmental questions from approved data without writing production code

Requirements and prerequisites

Participants should be comfortable using business data in spreadsheets and understand basic concepts such as rows, columns, data types, filters, totals, dates, and simple business measures such as revenue, cost, volume, or margin. Experience interpreting reports or dashboards is useful. Familiarity with SQL is helpful for understanding source data but is not required, and no Oracle database administration, data modelling, programming, or prior Oracle Analytics Cloud experience is assumed. Complete beginners can attend, but should expect to work carefully through data relationships, calculations, and visualisation choices during the practical exercises. Access to a provisioned OAC training environment is supplied or arranged for the course.

Training methodology

The instructor introduces each Oracle Analytics Cloud capability through a short demonstration, then participants reproduce and extend it in a configured OAC workspace. Exercises use a continuing business-performance scenario with transaction, target, customer, and operational datasets. Participants practise selecting data, preparing it in data flows, designing analyses, and testing dashboard interactions in pairs and individually. Facilitated review sessions compare visualisation choices and calculation logic. On day five, participants assemble their own self-service BI pack and complete an application plan identifying a live reporting use case, data owner, governance needs, and next implementation steps.

Course outline

Day 1: Oracle Analytics Cloud foundations and trusted data

• Oracle Analytics Cloud navigation, home page, catalog, and authoring workflow
• Self-service BI roles, governed content, and the distinction between datasets and subject areas
• Connecting to files, Oracle Autonomous Data Warehouse, and approved business sources
• Dataset profiling, column data types, quality indicators, and initial data inspection
• Data relationships, grain, primary keys, and the risks of duplicate aggregation
• Basic data preparation with column renaming, type conversion, and null handling
• Catalog folders, permissions, certified datasets, and safe content-sharing practices

Workshop: Participants create and profile a sales-performance dataset, document its grain and data-quality issues, and save it in a shared OAC project folder.

Day 2: Data preparation, joins, and analytical measures

• Data flow design for repeatable self-service data preparation
• Joining transaction, target, customer, and product datasets
• Union, append, and aggregation choices for combined reporting data
• Derived columns using expressions, date functions, text functions, and conditional logic
• Calculated measures for revenue, margin, variance, growth, and attainment
• Handling missing values, duplicate records, outliers, and inconsistent categories
• Running, scheduling, and monitoring data-flow output datasets

Workshop: Participants build a data flow that combines actuals and targets, produces margin and attainment measures, and outputs a refreshable analysis-ready dataset.

Day 3: Visual analysis and business storytelling

• Selecting charts based on comparison, trend, composition, distribution, and relationship questions
• Creating canvases, visualisations, tables, pivot tables, KPI tiles, and trellis views
• Using filters, column selectors, sorting, ranking, and top-N analysis
• Applying reference lines, trend lines, conditional formatting, and exception highlighting
• Creating calculated visualisations and analysing measure versus attribute behaviour
• Drilling, zooming, linking canvases, and investigating performance drivers
• Designing accessible dashboards with clear titles, labels, colour use, and visual hierarchy

Workshop: Participants create a three-canvas performance analysis that identifies underperforming regions, product categories, and margin exceptions.

Day 4: Interactive dashboards, governance, and publication

• Dashboard layout planning for executive, manager, and analyst audiences
• Global filters, dashboard prompts, parameters, and selector controls
• Action links, drill paths, navigation, and interactions between visualisations
• Narrative insights, annotations, and explaining exceptions without overstating conclusions
• Validation methods for reconciliations, totals, filters, and calculation edge cases
• Publishing analyses, managing catalog objects, and sharing through role-based access
• Data refresh expectations, dataset ownership, version control practices, and handover requirements

Workshop: Participants publish a role-focused management dashboard, configure interactive filtering and drill-through, and complete a validation checklist against source totals.

Day 5: Applied self-service BI dashboard build

• Translating a stakeholder question into dashboard decisions, metrics, and acceptance criteria
• Selecting trusted inputs and defining the analytical data model for a business scenario
• Building reusable calculations and documenting metric definitions
• Developing an executive summary canvas and supporting diagnostic canvases
• Testing usability with stakeholder questions and alternative filter selections
• Reviewing access, refresh, governance, and maintenance risks before publication
• Creating an implementation roadmap for a live Oracle Analytics Cloud use case

Workshop: Participants complete and present an OAC self-service BI pack containing a prepared dataset or data flow, interactive dashboard, metric definitions, validation evidence, and handover note.

Tools & standards covered

Oracle Analytics Cloud, Oracle Autonomous Data Warehouse, Oracle Analytics Publisher, SQL

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions