Case Study Lemonade, an W startup founded in 2015, made its initial public offering in July with a $1.7 billion valuation. Lemonade is an online platform that aims to address some of the key problems of the traditional home insurance industry. The company has been able to develop its business through smart design and a good marketing strategy. The AI component was built on top of that. The company's website and mobile app are very easy to use, the users can ll out a form with their name, gender, age, marriage status, incoming, address, phone number, and inform whether they had previous insurance or not. The process of buying insurance and filing claims with the app and website goes through digital assistants and is much faster than traditional insurance companies. As one of the first movers in the Wspace, Lemonade had the edge over other similar companies that have cropped up in recent years, and it was able to quickly snatch a lot of users who were looking for a shift from traditional insurance model to one that was more Lechzfucused. Lemonade' 5 business model and messaging are also interesting. The company takes a at fee from premiums, which means the company doesn't make a prot from denying claims. The unclaimed money goes to charities of users' choice. The company also says that it will not invest premiums into heavily polluting industries and companies that cause harm. So, basically, Lemonade is marketing itself as the good guy in a historically reviled industry, on a mission, per the company's words, to \"transform insurance from a necessary evil to a social good.\" Insurance depends a lot on data, and established agencies have more than a century of data they can use to develop risk models and create insurance policies. Lemonade didn't have the data of traditional agencies, but it also didn't have their baggage of customers and old policies. It was able to create its entire technology stack from the ground up to cater to the needs of an AI factory. With the entire experience being digitized, the company can collect a lot more data from each customer interaction, including data points W do not capture. This enables the company to create machine learning models that not only predict insurance risk with growing accuracy over time but can also create automation and personalization opportunities that were impossible before. The company has two AI chatbots: Maya helps you create your insurance plan in a few minutes, and Jim handles the claims process. According to the company, AI handles a third of the cases and pays claims in a matter of minutes. The rest of the claims are transferred to human agents. The chatbot continues to improve as it gathers more data. The company believes that with time, AI will give it the edge over traditional agencies and allow it to provide much more affordable plans to customers. And its $480 million pre-IPO funding and its post-1P0 growth show that investors believe its plan can work. Based on the case study above answer the following questions. 1- Based on the case study above identify and cite (3,0): a. six raw data sources from three different types. b. four types of information that can be extracted from this data. C. two decisions that can be made based on it. 2 - How a dashboard structure for Lemonade would look like? Use the raw data and the information that you described in question 1 and provide ve KPIs that could be part of the three layers structure on a dashboard. (3,0) 3 You have a meeting with the CEO of Lemonade and you are supposed to convince them to hire you to build the dashboard structure that you described above. Describe the main benet of dashboards that you would use to sell your service. (3,0) 4 Based on the case study above and considering computer data types \"having previous insurance\" is a raw data type (1,0): a. Char b. String c. Boolean (1. Double e. Array