Today, I’m here to show you that power of Vantage. Okay, what is Vantage? Vantage is an enterprise-grade software, which brings together the data warehouse, the data lakes, and analytics together. It’s available on the public cloud, the private cloud, as well as on premises, and in a hybrid environment. Vantage is built on world-class Teradata database technology, brings together multiple genres of analytics together, such as your machine learning, statistics, graph. And here’s the deal: All of these different analytics’ functions and all of these data that are available across multiple sources can be executed in SQL, in Python, in R; as well as, analytic visualizations can be delivered through components like Vantage Analyst and AppCenter. The best part of Vantage is that it is available across a wide constituency of people in the organization who want to be data driven: your data scientists, your business analysts, the lines of the business managers. Anybody who wants to be data-driven can use Vantage. And all of these insights can be operationalized in the platform. Not only are you able to deliver these insights on all of the data across these different analytics techniques, but they can also be operationalized in the same platform. This is what Vantage is. Okay, but now that I’ve told you what Vantage is, let’s actually take a specific use case and see what we can do with Vantage. So, here’s an example. Let me set the context for you a little bit. Here is an example of a retailer who wants to understand customer churn. And the way they’re defining customer churn is that there are members in the organization who cancelled their membership. Understandably, the retailer wants to know why people are cancelling their membership. Because if they can know why, and if they can predict who is likely to cancel their membership, they can take certain bits of corrective action. So, here’s an example of a path analysis application. This is a visualization. It’s a Sankey visualization. And there are different sets of activities which contribute towards membership cancelation. So, here’s one example, right? So, it seems like in this case, there are a lot of people who seem to making a complaint call, before they cancel their membership. So, what does this tell the retailer? It tells the retailer that clearly there seems to everybody something going on with the complaints. So, they look at this path and they say, “Okay, let’s figure out what’s happening with the complaints.” So, the next thing they do is they go and they want to understand the nature of these complaints. So, what they go into is a sentiment analytics application. They look at it and say, “Look, it seems like a lot of people seem to be confused with our policies. They seem to be sick. They seem to think that a lot of our policies, a lot of our—what we do for them is silly and they’re confused. They’re tired. It’s inconsistent, it’s expensive, it’s inappropriate. There’s a lot of confusion. And, to that point, the word “confused” comes up big in the word cloud. Now, what’s the point? Now, they’re able to see a path.