Strayer Univeristy Data Analytics in Business Discussion
Please respond to the two peers below:Having framed the problem/opportunity, formulated a testable hypothesis and gathered and organized key data, you are ready to continue your analysis by developing a data story that can be shared with others. To get started, download and review the "Types of Data Analysis" guide from our Week 4 readings, above.Apply one or more of the following analytical tools to your dataset:CorrelationRegressionGrouping and VisualizationVarianceStandard DeviationExplain whether your analysis of the data confirmed or refuted your testable hypothesis.1. Sudharshan Prahallada RE: Week 4 DiscussionApple's current and future success relies on big data and AI which has been helping the company to dominate the handheld business for decades now. It has also helped them to grow their market share compared to their competitors. The data can help the leadership's performance as well. In the past 5-6 years the concept of big data has grown so big. Now we have tools which are used to analyze big data, Hadoop for example. The usage of big data certainly has provided these companies with competitive advantages to include its new product release management, Labor costs. some of the drawbacks are teams losing traction over data and employees are overwhelmed with the amount of data that is getting generated with these devices.Increase in Consumers using apple devices is yielding more profits also allowing the company to innovate.
Apply one or more of the following analytical tools to your dataset:Grouping and VisualizationFor my project or opportunity this type of analysis is best suited as we will be generating graphs or Business Improvement reports or dashboards which will point us customers who are looking to buy a house soon.RegressionWith this approach it will keep the leadership accountable and will also help them to engage with employees, so far, the feedback channel has been only through third party with this approach we will be able to get firsthand feedback in form of dog food to improve our products and services which will only help us to gain more profits. This House-As-A-Service will also help our apple home platform as we are struggling in that sector with our competitors. We already have vendors who are partnering with us with home kit products which get so much data. From this method we can re-use the data to apple city or HaaS offering to our customers.Explain whether your analysis of the data confirmed or refuted your testable hypothesis.HypothesisIncreasing number of app signup in this case HaaS will increase our iPhone sales as well as this is only available with IOS. This will also generate newer endpoints for us to manage, we will also be able to get to our own cloud instead of paying for vendors to host our data. This will put us in the driving seat for the upcoming quarters which new markets to capture.ReferencesJWI -599 Lecture Videos.Best Apple HomeKit devices - CNETDuarte, N. 2019. Data Story, Ideapress Publishing.2. Paul Weaver RE: Week 4 DiscussionHello Professor Anderson and Classmates,In review, I have selected the Automotive Industry for my capstone project, and my chosen company is Tesla. My specific challenge is determining if it is feasible for Tesla to offer a new model for sale under the $30,000 price point in the global market.There are five types of data needed to determine feasibility and the degree of profitability in the automotive market. The data includes demographics of the Tesla consumer, start-up costs, manufacturing costs per car, profit margin per car, and market data.Start-up costs, manufacturing costs per car, profit per car are simple pieces of quantitative data based on research articles. The demographic data is similar to just a few data points and is derived from research articles. The market data will yield the demand for the new niche market.The market data is a quantitative data set that presents approximately 300 vehicles sold in the U.S. in 2021 with the annual sales of each car. By adding the MSRP of each car in 2021, I can sort the table and determine the ratio of vehicles sold under $30,000 or a target range relative to the total value of all cars sold during the same year.Based on additional research, I can approximately equate the larger Tesla market demographics with the current internal combustion engine market demographics as the two markets are converging (EVC, 4). By understanding the demographics, market research, and the quantitative model, the demand for the niche market was rendered (MSG, 5).Based on my data story, I have chosen to apply "Grouping and Visualization" as an analytical tool to group my data (JWI599, 3).Explain whether your analysis of the data confirmed or refuted your testable hypothesis.My analysis confirms the hypothesis and has yielded a viable approach to deriving a niche market for a lower-priced Tesla vehicle.Thanks,PaulReferences:JWI599. 2022. Week 4, Lecture Notes and VideosDuarte, N. (2019). Data Story. Idea House Publishing.JWI599. (January 27, 2022). Types of Data Analysis. JWI599 Business Analytics and CapstoneEVC (June, 2021). Electric Vehicle Council, EV Consumer Behavior. Fuels Institute.MSG (n.d.) Gathering Information and Measuring Market Demand. MSG Management Study Guide.