East: Hi, there, Marcus here. In this module, I'll walk you through how you can begin transforming your business with artificial intelligence and machine learning. In previous modules, we've touched on the importance of data for digital transformation. Now we're going to look at how you can use your data to bring value to yourself and your business. First, let's think about the data you already have. Do you have a dashboard that analysts view every day, or perhaps there's a report that your managers view each month? Both the dashboard and the report are examples of backward-looking data. They look at what happened in the past. Most analysis of the data in your organization is probably backward-looking, analysis of historical data to calculate metrics or identify trends. But to create value in your business, you need to use that data to make decisions for future business. Identifying trends in historical data is only part of the solution. Let me give you an example. Suppose Maya leads the business strategy and operations team for an international airline. She might be looking at historical annual reports to establish a trend in customer purchasing patterns. She'd probably use this data to forecast annual sales and operational costs, but there's nothing new or transformational about this decision-making process. What if Maya could predict the satisfaction rate of each flight or predict customer complaints and get ahead of them? To do this effectively, she'd need access to a lot more data, including number of passengers per flight, duration of each flight, customer satisfaction ratings per flight, number of customer complaints per flight, factors that contributed to customer complaints, weather reports, seasonal indicators, and time to resolution for customer complaints. Soon, she might be able to use the multiple points of data to predict the quality of a single flight and its customer complaints. But there are hundreds of flights each day. The real value for Maya would come from being able to make predictive insights for all flights all year round. More importantly, it would be far more valuable if she could dynamically adjust pricing or staff assignments, or even catering based on the predictions. Like the case with Maya, in order for any business professional to make repeated decisions using predictive insights at scale, machine learning, or ML, is needed. ML is a tool that enables you to derive predictive insights from data with repeatability and that's at a scale that traditional methods of data analysis don't allow. So let's take the time in this module to understand what ML is and how you can harness its potential. In this module, I'll explain what ML is and why data is so integral for its successful use. I'll discuss some real-world use cases for ML. Finally, I'll describe Google Cloud's differentiators when it comes to AI and ML and offer resources for where you can learn even more. Let's get started.
