Why Analytics for Games

This course addresses the use of analytics in gaming use cases. Learners will explore the benefit of analytics and how insights can be used to improve game design, increase efficiency of game operations, and inform financial and strategic decisions.

Learners will see different sources and types of game data to use for business intelligence and how an analytics pipeline can be used to translate game data to answers.

Intended Audience

This course is intended for:

• Business leaders
• Game developers
• Other game industry professionals

Course Objectives

This course is designed to teach you how to:

• Describe the business case for analytics in the games industry
• Identify and describe business questions about games and data sources to provide answers
• Identify and describe types of data to provide answers to business questions
• Describe the key components of an analytics pipeline.

Prerequisites

We recommend that attendees of this course have:

• Foundational understanding of cloud computing and Amazon Web Services (AWS), equivalent to Cloud Practitioner Essentials certification
• Basic knowledge of data analytics and AWS Analytics services, equivalent to Data Analytics Fundamentals

Delivery Method

This course is delivered through digital training

Duration

60 minutes

Course Outline

Module 1:

Course overview

• Introduction

Module 2:

Business case for analytics in games

• Analytics enable great games
• Cost of development
• Free-to-play games and games as a service
• Reasons for analyzing
• Impact of not having a data-gathering plan
• Analytics in the Cloud

Module 3:

Determining what to measure

• What to measure in your game
• Common questions for game development insights
• Determining the speed of insights
• Analyzing data in real-time with streaming analytics
• Analyzing data over time with batch analytics
• Planning your solution

Module 4:

Understanding types of data

• Understanding different types of data

Module 5:

Components of an analytics pipeline

• Exploring the analytics pipelines
• Potential challenges
• AWS services for analytics

Module 6:

Summary

Module 7:

Knowledge assessment