Ferrari N.V. Common Shares

Ladies and gentlemen, thank you for standing by and welcome to the Ferrari 2020 full year results conference call. At this time, all participants are in a listen-only mode. After the speaker presentation, there will be a question and answer session. If you wish to ask a question, please press star 1 on your telephone keypad. For your information, this conference is being recorded. Now, I would like to hand the conference over to your speaker today. Nicoletta Russo, please go ahead.

Thank you, Andrea, and welcome to everyone who is joining us. There are two topics that we plan to cover today. First, the group's full year 2020 operating results, and then our full year 2021 guidance. In light of this, the duration of the call is expected to be around 60 minutes. Today's call will be hosted by the group chairman and acting CEO, Mr. John Elkin, and our group CFO, Mr. Antonio Pica-Picon. All relevant materials are available in the investor section of the Ferrari corporate website. At the end of the presentation, we will be available to answer your questions.

Hi everyone, welcome to Datadog's demo for SREs. This will be a quick, high-level introduction to the Datadog platform tailored to SREs use cases. Datadog is a SaaS monitoring and security platform that unifies metrics, traces, logs, and other data from across the stack. What you're looking at here is a Datadog dashboard. This is a high level overview of your entire application where you can see how it's doing across all pillars of observability. This view is also totally customizable with simple drag and drop widgets, no coding required. You can see up top that we have our SLO widget highlighted. Not only is this a great reference point to ensure you're meeting your reliability goals, but it helps your SRE team ensure the entire organization is aligned on reliability initiatives. You can also see we're pulling data from our infrastructure, applications, logs, server lists, synthetics, as well as custom metrics such as furniture sales revenue, all in one single view. You can also keep an eye on your integrations in a dashboard like this. Datadog has over 450 vendor-backed integrations, including with most AWS, Azure, Google Cloud services, as well as Kubernetes, Kafka, Terraform, and much more. Once you set up the Datadog agent and any integrations you're using, Datadog provides several types of data visualizations. For instance, Datadog provides a high level view of your infrastructure. This host map is provided out of the box and gives you a bird's eye view of your hybrid or multi-cloud environment. All these hexagons are individual hosts currently color coded based on their CPU utilization and updated in real time. You can also slice and dice using tags like cloud provider, availability zone, and team as such. This way you can manage all your cloud providers on one screen. Tags propagate throughout the Datadog platform for easy and consistent querying of all your data. You can also use this view to see all your containers, since the Datadog agent automatically discovers new containers as they're spun up. Now, if you've gone from hosts to containers, you may have transitioned to microservices as well. To understand your increasingly distributed microservices architecture, we have the service map. The service map automatically maps all the services in your environment, both serverless and serverful. Your services are intelligently clustered based on how frequently they talk to one another. So even when you have hundreds of microservices, new engineers can still make sense of your systems. At a glance, I can see that one of our critical services, WebStore, has an alert on it. From here, I can immediately see which dependencies, both upstream and downstream, are also triggering alerts and then pull in the relevant teams. I can also immediately view related logs or traces with a click of a button. But let's take a deeper look into the WebStore service using Datadog's service overview. The service overview provides a unified view of a service health, from baseline metrics to more advanced capabilities like deployment tracking. We can see key performance metrics for our web store service, total requests, errors, and percentiles of latency. We can even scope these metrics down to specific code versions to compare requests and errors for each code deploy thanks to our deployment tracking capabilities. We can see that a certain version has led to an increase in errors in our code. So let's dive into the related logs to get a deeper understanding of the root cause behind these errors. With one click, we're directed to all the relevant error logs correlated to the WebStore service. And because everything is parsed on ingestion, we can find the logs we need by just clicking on filters instead of writing regular expression queries. So I don't have to wait for another team to get back to me or even write a query myself in order to surface the logs I need to troubleshoot the critical issue on the WebStore service. Now let's look at how easy we make it to understand millions of verbose and repetitive logs with our machine learning patterns. Patterns group all the related logs and surfaces the things that matter most. In this case, I can see that payments are being rejected because the API call rate is being exceeded, so we can prioritize that immediately. Another thing that sets Datadog's logging platform apart is logging without limits. With Datadog's logging without limits, you would have access to all your logs regardless of whether you paid to index them. You can use them to troubleshoot a live issue when it matters most, start indexing a new type of logs right away, or even rehydrate any logs from your archives when needed. Lastly, in the case of a live outage or emergency, you have LiveTail, which you see here. You can view all your logs live as they stream to the platform, again, regardless of whether you're indexing them or not. It's clear that there are a lot of potential things that can go wrong in a system. And we realize that alert fatigue is not only a frustrating experience for SREs, but it also causes you to lose trust in your alerting system. Datadog solves this by making sure that alerts are both specific and actionable. And the way we do that is by offering a number of different types of monitors, many of which are powered by machine learning capabilities such as anomaly detection. One powerful type of monitor is composite monitors, which allows you to create customizable combinations of symptoms into a single alert, thereby accurately identifying issues within your infrastructure and applications. Datadog also provides recommended monitors, which is a suite of curated alert queries and threshold for key infrastructure technologies like Consul, Kubernetes, Kafka, and more. These are pre-configured based on the expertise of our many technology partners, as well as the experience of thousands of our customers. With recommended monitors, you'll be able to start alerting on key monitoring data from your environment within minutes so you can focus your attention on growing your business. One other way Datadog allows you to make sure your users are having the best possible experience is with UX monitoring. Datadog Synthetics provides a single place to manage and view your entire testing suite. From here, you can view high-level results for all your tests and use facets to quickly sort through them. You can filter your tests by type, status, as well as environment, which allows you to quickly narrow them down to those running in production, dev, or staging. If you're looking to create a new test, you can do this from within this page as well. You can create a single API test to perform a single request, just like you can create multi-step API tests to chain together different requests. Now let's take a look at how easy it is to create an API test with Datadog. From here, you start by selecting the type of test, whether it's HTTP or a network test on your DNS or ICMP. For HTTP requests, all you have to do is pop in the URL, select the environment, and add any additional tags such as the service and resource. From here, you can set more advanced options such as defining assertions, choosing the location from which you'd like to run these tests, whether it's a private location or from one of our managed locations around the world, and finally choose the frequency as well as the alerting parameters, such as who should be alerted as well as the process to follow if an alert goes off. We've now looked at some of the ways Datadog allows you to stay on top of your systems. But with environment scaling so rapidly, we realized that it may be hard to keep track of everything that may potentially go wrong in your systems. This is where Watchdog steps in. Watchdog is like a newsfeed of everything unusual happening in your infrastructure and applications. It'll help you catch errors and latency findings that you didn't even know you should be watching. Anomalies that can happen while scaling increasingly distributed systems. Watchdog uses machine learning to automatically surface issues when your applications or cloud infrastructure is performing out of the norm with zero manual setup or configuration. It's a great starting point that many of our customers like to use to discover and begin solving problems. And not only will Watchdog point out anomalies, it will also show you a dependency map, related anomalies happening across your services, and it'll point out dashboards where you can find that anomalous data. So it surfaces everything in one place to quickly pinpoint root causes. And as you can see here, you can also enable monitors right from Watchdog, which allows you to surface issues and be notified about similar instances moving forward. To summarize, Datadog provides visibility all the way from your backend infrastructure to your application traces to your logs from across your systems. And to top it all off, it uses machine learning to always watch over your environment.