In regards to the business itself, the steady decline in profits indicates that there is a problem. Their recent lack of ability to meet product release deadlines and their slow movement into cloud computing technologies is hurting their image as the “top player” in the market. Structurally, there are serious issues with how decisions are made within Celeritas. Boyer’s attitude about being the only one who makes big decisions is really hurting the company. He says that he listens to his SVPs, but in reality, he still does what he thinks is best, even if it doesn’t align with the SVP’s ideas. There needs to be more input and team decision making in order for things to run smoother. By Boyer making all the decisions, it is causing a lot of tension and resentment towards him. The main interpersonal issue steams from the fact that the different departments don’t directly deal with problems that they are having. Instead, they use Boyer as a middleman, which is preventing problems from being resolved. There also seems to be a lot of issues with Lloyd. Most of his colleges believe that his confrontational attitude does not fit in well with Celeritas and they do not get along. 2.The team-building effort at Celeritas seemed quite effective at first. At the end of the meeting, there was a sense of togetherness and the SVPs were excited about the new changes being implemented at Celeritas. What seemed to work well was everyone getting a chance to voice his or her issues and opinions. This lead to some heated arguments, but the problems finally had a chance to be discussed and worked out. It really helped to open Boyer’s eyes to some issues that he didn’t even know existed. He realized that his meetings with the SVPs weren’t as effective as he had previously thought; also he learned that he needs to have more defined...

Part 1This week’s required readings explain the role that analytics plays in the modern business environment. Consider the following questions and respond in a minimum of 175 words:How is data analytics different from statistics?Analytics tools fall into 3 categories:descriptive, predictive, and prescriptive. What are the main differences among these categories?Explain how businesses use analytics to convert raw operational data into actionable information. Provide at least 1 example.Consider the organization you work for (or another organization you’re familiar with). Does this organization use data analytics?If so, how is it used? If not, how could the organization use data analytics to improve its performance? Part 2 ResponsesResponse 1. How is data analytics different from statistics?As stated in our book in chapter one statistics is "the scientific process of collecting, organizing, analyzing, interpreting, and presenting data". Statistics help convert raw data into useful data that can help businesses understand and thrive. Businesses can improve business decisions that will improve overall operations and take the guesswork out of making critical business decisions. Data Analytics is the actual computations used to discover the statistical information to improve business performance. Data analytics allows employees to draw conclusions about the information presented. Analytics tools fall into 3 categories: descriptive, predictive, and prescriptive. What are the main differences among these categories?The main difference between the 3 categories of analytic tools is that descriptive analytics analyzes data from the past. The historical data will help identify trends and patterns that business owners can use to gauge product sales or gain insight into a new product or service and how it will perform in the future. Predictive analytics forecast consumer behavior by predicting probabilities, and prescriptive analytics is about maximizing revenues by helping make decisions that are applicable to real-world situations. Prescriptive analytics' main question is to answer what do we need to achieve which provides a course of action or a strategy to answer the question it poses. Explain how businesses use analytics to convert raw operational data into actionable information. Provide an example. One way that businesses use analytics to convert raw operational data into actionable information is by using the cloud to record in real-time. By using the cloud one can access data any time, anywhere, analyze data faster than before consolidate team efforts, and respond quickly to meet customer needs. Consider the organization you work for (or another organization you’re familiar with). Does this organization use data analytics? If so, how is it used? If not, how could the organization use data analytics to improve its performance?I am sure we use data analytics; however, I don't believe we use it properly or to improve the needs of our residents. If we used it correctly we could provide residents with a better meal plan, give raises, and to improve the overall performance of our facility, and maintain the resident census which is and has been very low for months. By improving (increasing) the resident census the budget cuts that we have made would be unnecessary and practical.Doane, D., & Seward, L. (2022). Applied statistics in business and economics (7th ed.). McGraw-Hill. RESPONSE 2Analytics and statistics are both parts of data science. "Statistics is the science of collecting, organizing, analyzing, interpreting, and presenting data," (Doane, D., & Seward, L., 2022).Analytics is a broad use of statistics, mathematics, and computational tools to extract information from data. For me to understand the difference is analytics is in the terms of taking the data and analyzing it over to make assumptions. Statistics are taking mathematics to test those assumptions. The three categories of analytics tools are in the lines of questions to be answered.Descriptive analytics tools answer what had happened by taking historical data to analyze and seek trends and patterns.Predictive analytics tools are to answer what would like happen next by probability predictions of future events and forecast consumer behavior. Prescriptive analytics tools answer the action to take to achieve goals by helping make decisions on how to achieve objectives within real-world constraints. Businesses use analytics to convert raw operational data into actionable information by testing a question in mind such as auditing. For my job, I work on several accounts to ensure the data is correct. We use analytics daily to ensure we are reporting the correct data to limit errors. For instance, taking the raw data reported that is being reported into invoices as the right count and amount. Then taking the month-to-month data compare the counts to ask why those counts are high or low compared to the previous two months. This will then advance further testing to see if the data is wrong, the customer is leaving our company, or it is just flexed in counts that happened.I think it helps you understand what data you are working with and what to look for if something looks unusual.