Data Preparation & Quality Assurance for BI Training Course

Description

This course equips participants with practical skills to prepare, clean, validate, and ensure the quality of data used in Business Intelligence (BI) systems. It focuses on data cleaning techniques, transformation processes, data profiling, validation rules, and quality assurance frameworks that support reliable reporting and analytics. Participants will learn how to turn raw, inconsistent data into accurate, consistent, and analysis-ready datasets for dashboards, reporting, and decision-making.

Target Groups

Data analysts and business intelligence professionals
Data engineers and ETL developers
Reporting and M&E officers
Database administrators
IT and systems integration teams
Finance and operations analysts
Public sector and NGO data teams
Business users involved in reporting
Anyone working with data for BI and analytics

Course Objectives

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

Understand the importance of data quality in BI systems
Clean and transform raw data for analysis
Identify and correct data inconsistencies and errors
Apply data validation and profiling techniques
Improve data accuracy, completeness, and reliability
Design data quality assurance processes
Prepare datasets for dashboards and reporting
Implement standard data transformation workflows
Reduce reporting errors and inconsistencies
Strengthen trust in BI insights and decision-making

Course Modules

Module 1: Introduction to Data Preparation & Quality Assurance

Role of data quality in BI and analytics
Data preparation lifecycle
Common data quality issues
Impact of poor data on decision-making
Principles of trustworthy data

Module 2: Data Profiling and Assessment

Understanding data structures and patterns
Data profiling techniques and tools
Identifying missing, duplicate, and inconsistent data
Assessing data completeness and accuracy
Data quality scoring methods

Module 3: Data Cleaning Techniques

Handling missing values
Removing duplicates and inconsistencies
Standardizing formats (dates, text, numbers)
Correcting errors in datasets
Outlier detection and treatment

Module 4: Data Transformation for BI

Structuring data for reporting and dashboards
Aggregation and summarization techniques
Data normalization and standardization
Feature engineering basics for BI
Preparing data for analysis tools

Module 5: Data Validation and Integrity Checks

Defining validation rules and constraints
Cross-field validation techniques
Referential integrity in datasets
Automated validation processes
Exception handling and correction workflows

Module 6: Data Quality Frameworks and Standards

Dimensions of data quality (accuracy, consistency, timeliness, etc.)
Data governance principles
Establishing data quality standards
Data stewardship roles and responsibilities
Compliance and audit considerations

Module 7: Data Integration and Consistency Management

Combining data from multiple sources
Resolving conflicts between datasets
Master data management concepts
Ensuring consistency across systems
Data reconciliation techniques

Module 8: Tools and Techniques for Data Preparation

Overview of BI and ETL tools for data preparation
Using Excel, Power Query, and BI platforms
Automation of data cleaning processes
Scripting basics for data transformation
Best practices for scalable preparation workflows

Module 9: Data Quality Monitoring and Reporting

Setting up data quality dashboards
Continuous monitoring of data integrity
Error tracking and resolution processes
Data quality KPIs and metrics
Reporting on data health

Module 10: Capstone Project and Case Studies

End-to-end data preparation exercise
Cleaning and transforming real-world datasets
Data quality assessment and reporting project
Case studies on BI data quality challenges
Emerging trends: AI-powered data cleaning, automated data validation systems, real-time data quality monitoring, and intelligent data preparation platforms