Duration 4 Days – 28 hrs.

 

Overview

 

The Data Management and Analytics Training Course is designed to equip participants with the knowledge and practical skills needed to manage, organize, analyze, and interpret data effectively for business decision-making. The course provides a comprehensive understanding of data lifecycle management, data governance, data quality, business intelligence, analytics techniques, and modern data-driven strategies used by organizations today.

 

Participants will learn how to collect, clean, transform, store, analyze, visualize, and present data using industry best practices and analytics methodologies. The course also introduces key concepts in reporting, dashboarding, descriptive and predictive analytics, data storytelling, and data governance to support operational efficiency and strategic planning.

 

This training combines foundational data management principles with practical analytics techniques to help organizations maximize the value of their data assets and support digital transformation initiatives.

 

Objectives

 

• Understand the fundamentals of data management and analytics
• Identify the importance of data-driven decision-making
• Apply data governance and data quality best practices
• Understand data lifecycle and data architecture concepts
• Perform data collection, cleaning, and transformation activities
• Analyze datasets using analytical techniques and reporting methods
• Create meaningful reports and dashboards
• Interpret trends, patterns, and business insights from data
• Understand key concepts in business intelligence and analytics
• Apply data visualization and storytelling techniques
• Understand basic predictive analytics and AI-driven analytics concepts
• Develop a data analytics improvement roadmap for their organization

 

Target Audience

 

• Business Analysts
• Data Analysts
• Operations Managers
• IT Professionals
• Project Managers
• PMO Teams
• Finance and Accounting Professionals
• HR and Marketing Professionals
• Digital Transformation Teams
• Reporting and MIS Personnel
• Business Intelligence Teams
• Executives and Decision Makers
• Professionals working with organizational data

 

Prerequisites

 

• Basic computer literacy
• Familiarity with spreadsheets and reporting tools
• Basic understanding of business operations
• No advanced programming knowledge required

 

Course Outline

 

Day 1 — Foundations of Data Management and Analytics

 

Module 1: Introduction to Data Management and Analytics

 

• Understanding data in modern organizations
• Types of data and data sources
• Structured vs unstructured data
• Importance of data-driven decision-making
• Data management vs data analytics
• Roles in data and analytics teams

 

 Module 2: Data Lifecycle Management

 

• Data collection and acquisition
• Data storage and retention
• Data processing and transformation
• Data usage and sharing
• Data archival and disposal
• Data lifecycle best practices

 

Module 3: Data Governance and Data Quality

 

• Introduction to data governance
• Data ownership and stewardship
• Data privacy and compliance
• Data security concepts
• Data quality dimensions
• Managing data integrity and consistency

 

Module 4: Data Architecture Fundamentals

 

• Databases and data repositories
• Data warehouses and data lakes
• Cloud-based data environments
• Data integration concepts
• Master data management
• Metadata management basics

 

Module 5: Introduction to Business Intelligence

 

• What is Business Intelligence (BI)
• BI tools and reporting platforms
• Operational vs strategic reporting
• KPIs and business metrics
• Self-service analytics concepts
• Modern BI trends

 

Day 2 — Data Preparation and Analysis

 

Module 6: Data Collection and Preparation

 

• Data gathering techniques
• Importing and consolidating datasets
• Data cleaning and validation
• Handling missing and duplicate data
• Data formatting and standardization
• Preparing data for analysis

 

Module 7: Data Analysis Fundamentals

 

• Types of analytics:
  • Descriptive analytics
  • Diagnostic analytics
  • Predictive analytics
  • Prescriptive analytics
• Data exploration techniques
• Trend and variance analysis
• Root cause analysis using data
• Correlation and pattern identification

 

Module 8: Spreadsheet and Analytics Techniques

 

• Data sorting and filtering
• Pivot tables and summaries
• Lookup and reference functions
• Conditional calculations
• Basic statistical analysis
• Data aggregation techniques

 

 Module 9: Data Visualization and Reporting

 

• Principles of effective data visualization
• Charts and graph selection
• Building dashboards
• Reporting best practices
• Data storytelling techniques
• Presenting insights to stakeholders

 

Module 10: Hands-On Data Analysis Workshop

 

• Data preparation exercises
• KPI analysis activities
• Dashboard-building exercises
• Reporting scenarios
• Team-based analytics workshop
• Interpretation of business insights

 

Day 3 — Advanced Analytics and Data-Driven Decision Making

 

Module 11: Advanced Analytics Concepts

 

• Introduction to predictive analytics
• Forecasting concepts
• Scenario and trend analysis
• Customer and operational analytics
• Risk analytics basics
• AI-assisted analytics overview

 

Module 12: Introduction to Data Modeling

 

• Data relationships and structures
• Basic data modeling concepts
• Relational data concepts
• Data normalization overview
• Data mapping techniques
• Building analytical datasets

 

Module 13: Performance Metrics and KPI Management

 

• Defining measurable KPIs
• Operational and strategic metrics
• KPI dashboards and scorecards
• SLA monitoring concepts
• Benchmarking techniques
• Data-driven performance improvement

 

Module 14: Decision-Making Using Analytics

 

• Analytical decision-making frameworks
• Business case analysis
• Identifying trends and opportunities
• Data-driven strategy formulation
• Using analytics for operational improvements
• Executive reporting techniques

 

Module 15: Analytics Case Studies

 

• Finance analytics examples
• HR analytics examples
• Sales and marketing analytics
• Operations analytics
• Customer service analytics
• Industry best practices

 

Day 4 — Data Strategy, Governance, and Future Trends

 

Module 16: Data Security and Compliance

 

• Data security fundamentals
• Access controls and permissions
• Data privacy regulations
• Compliance and audit requirements
• Managing sensitive information
• Data risk management

 

Module 17: Modern Data Technologies

 

• Cloud analytics platforms
• Big Data concepts
• Real-time analytics
• Automation in analytics
• AI and machine learning overview
• Emerging trends in data management

 

Module 18: Data Governance Frameworks

 

• Building a data governance strategy
• Data policies and standards
• Data ownership structures
• Governance operating models
• Data quality monitoring
• Governance maturity assessment

 

Module 19: Building a Data-Driven Culture

 

• Encouraging data literacy
• Promoting data-based decisions
• Organizational change management
• Stakeholder engagement
• Data democratization concepts
• Analytics adoption strategies

 

Module 20: Capstone Workshop and Action Planning

 

• End-to-end analytics case study
• Group data analysis project
• Dashboard presentation workshop
• Building a data improvement roadmap
• Best practices and lessons learned
• Open forum and consultation session