Mastering Business Intelligence and Data Analytics for Decision Making

Overview:

Introduction:

Business intelligence and data analytics refer to the structured integration of data sources, modeling techniques, and interpretation frameworks that support institutional forecasting, evaluation, and planning. These systems enable decision makers to analyze structured data sets, extract patterns, and generate insight aligned with strategic goals. This training program introduces core analytics functions, BI architectures, and evaluation models used to transform data into actionable intelligence for long term institutional alignment.

Program Objectives:

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

• Identify the elements and roles of business intelligence and data analytics in strategic environments.

• Structure data modeling techniques used to support analytics output and decision inputs.

• Differentiate methods for generating insight from structured datasets.

• Map analytics evaluation frameworks linked to institutional goals and performance.

• Assess governance systems supporting data integration, quality, and compliance.

Targeted Audience:

• Business Intelligence and Data Analysts.

• Strategy and Planning Officers.

• Performance Evaluation Professionals.

• Data Governance and Reporting Managers.

• Senior Executives responsible for analytics oversight.

Program Outline:

Unit 1:

Foundations of Business Intelligence and Data Analytics:

• Institutional role of BI and analytics in strategic planning.

• Classification principles of analytics types, including descriptive, diagnostic, predictive, and prescriptive.

• Oversight on core BI components, including data sources, integration pipelines, dashboards.

• Purpose of structured analytics in tracking trends and performance.

• Relationships between data, indicators, and executive decision making.

Unit 2:

Data Modeling and Analytics Structures:

• Overview of schema design and dimensional modeling.

• How to organize time series, transactional, and categorical data for analysis.

• Data normalization, transformation, and preparation methods for analytics use.

• Key activities for structuring analytics pipelines from source to output.

• Frameworks for aligning input models with BI reporting structures.

Unit 3:

Analytical Output and Decision Support Models:

• Visualization frameworks for delivering analytic results.

• Trend analysis, variance detection, and correlation mapping techniques.

• How to structure dashboards and analytic reports for executives.

• KPI development process using analytic logic and historical comparisons.

• Oversight on role based filtering and controlled data access systems.

Unit 4:

Strategic Planning Supported by Analytics:

• Importance of linking forecast models with planning cycles using analytics output.

• How to identify performance gaps using structured BI indicators.

• The role of strategic realignment through continuous analytics review.

• Evaluation criteria of risk and opportunity through predictive models.

Unit 5:

Data Governance, Integration, and Analytics Quality:

• Governance frameworks for analytics accuracy and integrity.

• Systems for integrating analytics across departments.

• Key principles for quality control checks for data consistency and traceability.

• Standards for managing structured data lifecycles.

• Oversight and compliance measures for analytics driven decision systems.