How AI and ML differ from data analytics and business intelligence
1. How AI and ML differ from data analytics and business intelligence
Within your organization, perhaps you're familiar with a specific dashboard that analysts view every day. Or maybe managers review a particular report each month. Both the dashboard and the report are examples of backward looking data. They look at what happened in the past. Most data analysis and business intelligence is based on historical data, used 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. This is where artificial intelligence and machine learning come in. They're the key to unlocking these capabilities. Let's consider an example to emphasize this point. Maya leads the business strategy and operations team for an international airline to establish a trend in customer purchasing patterns, she's looking at historical annual reports. She can use this data to generate dashboards that present information such as customer demographic distribution and sales in recent years. But there's nothing new or transformational about this decision making process. Maya is simply using data analytics to illustrate what's happened in the past. But 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 needs access to a lot more data and use ML models to make predictions for future business. The data she needs might include the number of passengers per flight, the duration of each flight, the customer satisfaction ratings per flight, and the number of customer complaints per flight. She also needs to understand factors that contributed to customer complaints, weather reports, seasonal indicators, and the time to resolution data for customer complaints. With all of these various data points, Maya might 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. Remember, ML provides a method to teach a computer how to solve problems by feeding examples of the correct answers. With access to the right data, Maya can use machine learning to uncover these types of predictive insights to benefit the airline and its customers.2. Let's practice!
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