Certification in Business Data Analytics (IIBA®- CBDA) Training (UT-BA-IIBA-CBDA)

Course Description

Business data analytics is the discipline by which a specific set of techniques, competencies and practices are applied to perform the continuous exploration and investigation of business data. It focuses on effective business decision-making through data analysis, enabling organizations to make more informed decisions. This two-day session will prepare you for the Certification in Business Data Analytics (IIBA®-CBDA) exam awarded by the International Institute of Business Analysis™ (IIBA®) and help you make the most of your post-class study time. This course was designed to help candidates focus on the critical areas to study and to provide insights into the exam. CBDA Certification (IIBA® Certification in Business Data Analytics) is a specialty certification from IIBA®. It is the perfect certification for business analysts who want to demonstrate that they have the skills and expertise needed to be an effective business analyst working with a data analytics team on a data analytics initiative.

Course Objectives

  1. Describe the overall data analytics framework and the six domains
  2. Articulate the role of the business analysis professional in the data analytics framework
  3. Understand the various tasks in each domain and the role of the business analyst in those tasks
  4. Identify and understand how to apply the core concepts described in the Guide to Business Data Analytics v.1.0
  5. Learn how to apply various techniques to the data analytics effort
  6. Anticipate the general types of questions that appear on the exam and learn how to answer them

Course Outline

1 - IIBA-CBDA Exam Overview

  • Eligibility and the application
  • Exam blueprint
  • Sample exam questions

2 - What is Business Data Analytics?

  • Business data analytics through 5 perspectives
  • Business data analytics cycle
  • Business data analytics objectives
  • Business analysis and business data analytics

3 - Identify Research Questions

  • Define the business problem or opportunity
  • Identify and understand stakeholders
  • Assess current state
  • Define future state
  • Formulate research questions
  • Plan business data analytics approach

4 - Source Data

  • Plan data collection
  • Determine data sets
  • Collect data
  • Validate data

5 - Analyze Data

  • Develop data analysis plan
  • Prepare data
  • Explore data
  • Perform data analysis
  • Assess the analytics and system approach taken

6 - Interpret and Report Analysis

  • Validate understanding of stakeholders
  • Plan stakeholder communication
  • Determine communication needs of stakeholders
  • Derive insights from data
  • Document and communicate findings for complete analysis

7 - Use Results to Influence Business Decision-making

  • Recommend actions
  • Develop implementation plan
  • Manage change

8 - Guide Organizational-level Strategy for Business Data Analytics

  • Organizational strategy
  • Talent strategy
  • Data strategy

9 - 20 CBDA Techniques

  • Purpose
  • Description
  • Elements / Business Data Analytics Perspective
  • Usage considerations

10 - IIBA-CBDA Exam and Application Tips

  • Exam tips and testing strategies
  • Application tips and recertification info


There are no prerequisites for this course.

Course Information

Length: 2 day

Format: Lecture and Lab

Delivery Method: n/a

Max. Capacity: 16


Contact Us

Geography & Location
Jun 24, 2024 - 2 day(s)
Jun 24, 2024
AMER, Remote-CST
$1195 USD
$1195 USD

Do you have more questions? We're delighted to assist you!


Who Should Attend

This course is intended for business analysts and others performing business analysis tasks on data analytics initiatives and who wish to be recognized for their ability to effectively execute on those initiatives as well as those who want to prepare for the IIBA®-CBDA exam. There are no eligibility requirements for this exam. However, it is recommended that you have a few years of data analytics experience or at least a minimal understanding of data analytics.