DAT 205 Elevator Pitch Template Complete The Table Below ✓ Solved

DAT 205 Elevator Pitch Template Complete the table below by

Complete the table below by filling in the blank cells. Start by introducing yourself.

Describe what it means to be a data-driven organization. Briefly explain each stage of the data analytics life cycle and a data analyst’s role in each stage. Identify at least two types of tools or methods for sharing data and results. Explain why these tools or methods are appropriate for communicating with stakeholders and other nontechnical audiences in your organization. Explain how data influences and impacts organizational decision making.

Paper For Above Instructions

Hello, my name is Quaneisha, and I am currently working towards an Associate's degree in Data Analytics. In my role as a Registration Clerk at a hospital, I have had the opportunity to witness firsthand the importance of data in providing effective healthcare services.

Understanding a Data-Driven Organization

A data-driven organization is one that makes decisions based on the analysis and interpretation of data rather than intuition or personal experience alone. This approach relies heavily on the systematic collection, processing, and utilization of data to inform strategic choices. In such organizations, data becomes a crucial asset, guiding actions that can lead to improved outcomes, efficiency, and competitive advantage.

The Data Analytics Life Cycle

The data analytics life cycle consists of several stages that guide the data analysis process. Each stage is crucial, and a data analyst plays a significant role at every point. Here are the main stages:

  1. Problem Definition: This initial phase involves identifying the key questions that need answers. The data analyst collaborates with stakeholders to understand the objectives and scope of the analysis.
  2. Data Collection: Once the problem is defined, data is collected from various sources, including databases, surveys, or external sources. Here, the analyst ensures that the data gathered is relevant and of good quality.
  3. Data Cleaning: Raw data often contains inaccuracies or inconsistencies. During this stage, the data analyst cleans the data by removing errors and duplicates, ensuring accuracy for analysis.
  4. Data Analysis: This is where the data analyst applies statistical and analytical techniques to draw insights. Various methods like regression analysis, clustering, or machine learning could be used here.
  5. Data Visualization: After analyzing the data, the analyst creates visualizations such as charts, graphs, or dashboards to present findings in a digestible format.
  6. Communication: This final stage involves sharing insights with stakeholders. The data analyst explains the results and informs decision-making based on the data.

Tools and Methods for Sharing Data

Effective communication is vital for disseminating data insights within an organization. Two common tools or methods for sharing data and results include:

  1. Dashboards: These are visual displays that present key metrics and performance indicators in real time, making it easy for stakeholders to grasp essential information at a glance.
  2. Reports: Written reports provide a detailed narrative of the analysis, including methodology, findings, and recommendations, ensuring comprehensive understanding among audiences.

Both dashboards and reports are appropriate for communicating with stakeholders and non-technical audiences. Dashboards enable quick decision-making through visual means, while reports provide context and detailed analysis necessary for informed decision-making.

The Influence of Data on Organizational Decision Making

Data significantly influences organizational decision-making by providing empirical evidence and actionable insights. For instance, in healthcare, data analytics can identify trends in patient admissions, enabling hospitals to allocate resources effectively or refine service offerings. Similarly, market data can inform product development strategies, aligning them more closely with customer needs. In essence, data acts as a guiding beacon, enabling organizations to minimize risks and enhance operational effectiveness.

In conclusion, my journey in the field of Data Analytics not only serves to enrich my professional expertise but has also highlighted the transformative power of data in shaping organizational decisions. As I continue to learn, I look forward to leveraging data analytics to enact positive changes in healthcare and beyond.

References

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  • Shmueli, G., & Koppius, O. (2011). Predictive Analytics, Data Mining and Statistics: NEMO & the Data Analytics Life Cycle. Industry and Business Analytics.
  • Davenport, T. H., & Harris, J. G. (2007). Competing on Analytics: The New Science of Winning. Harvard Business Review Press.
  • Baesens, B. (2014). Analytics in a Big Data World: The Essential Guide to Data Science and its Applications. Wiley.
  • Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From Data Mining to Knowledge Discovery: An Overview. In Advances in Knowledge Discovery and Data Mining.
  • Kelleher, J. D., & Tierney, B. (2018). Data Science. MIT Press.
  • Boyd, D. & Crawford, K. (2012). Critical Questions for Big Data. Information, Communication & Society.
  • Anderson, C. (2008). The End of Theory: The Data Deluge Makes the Scientific Method Obsolete. Wired Magazine.
  • Chaudhuri, S., Dayal, U., & Narasayya, V. (2011). An Overview of Business Intelligence Technology. Communications of the ACM.
  • Gilbert, E. (2016). How Data Science is Changing the Way Businesses Operate. Forbes.

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