Chapter 3 –discussion question #1-4 & exercise 12 (choose any two) When submitting work, be sure to include an APA cover page and include at least two APA formatted references (and APA in-text citations) to support the work this week. All work must be original (not copied from any source). Discussion: 1. How do you describe the importance of data in analytics? Can we think of analytics without data? Explain. 2. Considering the new and broad definition of business analytics, what are the main inputs and outputs to the analytics continuum? 3. Where do the data for business analytics come from? What are the sources and the nature of those incoming data? 4. What are the most common metrics that make for analytics-ready data? Exercise: 12: Go to data.gov—a U.S. government–sponsored data portal that has a very large number of data sets on a wide variety of topics ranging from healthcare to education, climate to public safety. Pick a topic that you are most passionate about. Go through the topic- specific information and explanation provided on the site. Explore the possibilities of downloading the data, and use your favorite data visualization tool to create your own meaningful information and visualizations.

The importance of data in analytics cannot be overstated. Data is the foundation upon which analytics is built. Without data, there would be no information to analyze or draw insights from. Data provides the necessary raw material for analytics processes and enables organizations to make data-driven decisions.

Analytics without data is simply not possible. The entire purpose of analytics is to analyze and interpret data to gain valuable insights and inform decision-making. Without data, there would be nothing to analyze and no insights to uncover. Data is the fuel that powers the analytics engine.

In the new and broad definition of business analytics, the main inputs to the analytics continuum are data, tools and technologies, and expertise. Data is the primary input, as it is the raw material that is analyzed to derive insights. Tools and technologies enable the processing and analysis of data, while expertise refers to the knowledge and skills required to effectively utilize the tools and interpret the results.

The main outputs of the analytics continuum are insights and recommendations. Insights are the meaningful patterns, trends, and relationships that are uncovered through data analysis. Recommendations are the actionable steps that can be taken based on these insights to drive business decisions and improve performance.

Data for business analytics can come from a variety of sources. Some common sources include internal organizational databases, external data providers, social media platforms, web scraping, and sensor data. The nature of incoming data can vary greatly depending on the source. It can be structured or unstructured, real-time or historical, qualitative or quantitative. The diversity of data sources and types is one of the challenges in business analytics, as it requires organizations to effectively manage and integrate different types of data.

In order for data to be analytics-ready, it needs to be in a format that can be easily analyzed and interpreted. This requires data to be accurate, complete, and consistent. Common metrics that make for analytics-ready data include measures of performance, such as revenue, customer satisfaction, and profitability. Other metrics can include demographic data, market share, and customer behavior.

Exercise 12 involves using data.gov, a U.S. government-sponsored data portal, to explore and analyze data on a topic of interest. This exercise allows individuals to not only access a wide variety of data sets but also utilize their favorite data visualization tool to create meaningful information and visualizations. By working with real-world data and applying data visualization techniques, individuals can gain practical experience in analyzing and presenting data in a visually compelling and informative way.

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