Business Analytics Overview

Introduction

• Presenter: Sharid, Director, Co-founder at Inetics
• Expertise: AI implementations, machine learning operations, data engineering
• Experience: 17 years
• Role: Visiting faculty at various universities and colleges

Topic Overview

What is Business Analytics?

• Definition: Ensuring smooth business operations with consistent growth and success
• Role of Business Analyst (BA):
  • Ensuring businesses run smoothly
  • Enhancing efficiency and profitability
  • Implementing and documenting standard business processes
  • Maintaining quality services

Difference Between Data Analytics and Business Analytics

• Covered at a later stage; various terminologies in data science and their differences

Resources

• 360 Digit MG YouTube Channel
  • Playlists: Data science, tableau, business analytics
• 360 Digit MG Website
  • Learning Resources: Mind maps, blogs, data science books

Business Analytics Basics

Business Analyst Responsibilities

• Gathering and documenting business requirements
• Generating reports from past data and estimating future outcomes
• Defining benchmarks for various processes
• Financial analysis
• Customer behavior analysis
• Logical reasoning and creative thinking
• Employing machine learning and statistical techniques for forecasting and predictions
• Understanding and implementing quality maintenance strategies

Key Skills for Business Analysts

• Logical reasoning
• Business knowledge
• Creative thinking
• Statistical understanding
• Machine learning basics

Phases of Business Analytics

• Requirement Gathering: Understanding business objectives
• Documentation: Recording all business process aspects
• Report Generation: Gathering clarity through past data and future estimations
• Quality Maintenance: Defining benchmarks
• Financial Analysis: Understanding financial aspects
• Customer Analysis: Understanding behavior and expectations

Steps and Importance

Descriptive Analytics

• Question: What happened?
• Data Used: Historical data
• Output: Reports summarizing past events

Diagnostic Analytics

• Question: Why did it happen?
• Output: Identifies causes and effects (root cause analysis)

Predictive Analytics

• Question: What may happen?
• Output: Estimations of future outcomes based on past data
• Techniques: Statistical analysis, machine learning

Prescriptive Analytics

• Question: What should be done?
• Output: Recommendations for controlling future outcomes

Tools and Techniques

• Mind Maps: Detailed architectures of business processes
• Statistical Methods: For quantitative reasoning
• Machine Learning Models: For predictive insights

Real-World Applications

Example Use Case: Credit Card Business

• Problem Statement: Increased support tickets
• Potential Problems: Staffing issues, technical glitches, information gaps, fraud transactions
• Analysis Approach:
  • Define and scope the problem
  • Investigate and document various departments influencing the issue
  • Formulate solutions based on root cause analysis

Summary

• Business Analytics: Crucial for enhancing business efficiency and solving practical problems
• Core Stages: Descriptive, diagnostic, predictive, and prescriptive analytics
• Tools and Resources: Various online resources available for skill enhancement
• Future Sessions: Focus on defining problem scope and exact methods for solution development