chapter two

2 Artificial Intelligence for core business data

 

This chapter covers:

  • Looking at business data assets in terms of dollar density and business impact.
  • Learning how to teach machines to predict key information for your business with supervised learning
  • Advantages and risks of Machine Learning compared to conventional Software Engineering.
  • Two case studies about using AI for creating new business lines and optimizing existing processes.

As we’ve seen in the introduction, today’s AI revolution is based on learning from data, and that’s why we have decided to organize this book based on the different shapes and forms that data can take. In this chapter, we start by looking at what we call core business data, the type of data that’s closest to the value proposition of your business. We define core business data as “data with a direct impact on the top or bottom line of the organization”.

2.1   What is core business data, and how can you exploit it with AI

2.2   Using AI with core business data

2.2.1   The real estate marketplace example

2.2.2   The principles of Machine Learning - how to add AI capabilities to a real estate marketplace

2.2.3   The Machine Learning advantage

2.2.4   Beyond the real estate example: applying AI to general core business data

2.3   Core business data case studies

2.3.1   How Google used AI to cut their data centers’ energy bill by 40%

2.3.2   The Data Center energy consumption problem

2.3.3   A Machine Learning approach to Data Center efficiency

2.3.4   ML applied to Data Center optimization gains momentum

2.3.5   How Square used AI to lend billions to small businesses

2.3.6   Case studies learnings

2.4   Evaluating performance and risk

2.5   Summary