Data Analytics
Scenario:
Your organization is planning to purchase a tool for data analytics. You have been asked to make a recommendation for two tools.
Your organization is also researching the application of data warehousing, big data, and data mining. You have also been asked to make an evaluation of how data warehousing, big data, and data mining can be beneficial to a healthcare system. Some members of your audience may be unfamiliar with these concepts, so part of your goal is to provide background information.
Research two data analytics tools that you would recommend in response to the scenario above.
Thoroughly investigate the specifications of each tool and consider the pros, cons, and hardware requirements of each tool.
Using supporting evidence from the articles you researched, create a PowerPoint presentation with the following information:
Recommend two tools for data analytics and explain why they would be beneficial to your organization.
Provide a description of big data, data mining, and data warehousing.
Provide an analysis of how data mining can be beneficial to a healthcare system.
Explain the purpose, characteristics, and components of a data warehouse.
Explain how the type of data warehousing used can impact the ability to mine data.
Describe examples of the successful use of guided data mining and automated data mining within healthcare.
Sample Solution
Slide 1: Title Slide
Data Analytics Tools Recommendation and Data Warehousing, Big Data, and Data Mining for Healthcare
Slide 2: Introduction
In order to make informed decisions, healthcare organizations need to be able to collect, analyze, and interpret data effectively. Data analytics tools can help healthcare organizations to do this by providing them with the ability to visualize data, identify patterns and trends, and predict future outcomes.
Full Answer Section
Slide 3: Data Analytics Tools Two data analytics tools that I recommend for healthcare organizations are:- Tableau: Tableau is a powerful data visualization tool that allows users to create interactive dashboards and reports. Tableau is easy to use and does not require any coding knowledge.
- SAS Visual Analytics: SAS Visual Analytics is a comprehensive data analytics platform that offers a wide range of features, including data preparation, data visualization, statistical analysis, and machine learning. SAS Visual Analytics is more powerful than Tableau, but it is also more complex and requires more training to use.
- Big data: Big data refers to large and complex datasets that are difficult to process using traditional data processing tools. Big data can come from a variety of sources, such as electronic health records, medical imaging, and wearable devices.
- Data mining: Data mining is the process of extracting knowledge and insights from large datasets. Data mining can be used to identify patterns and trends, predict future outcomes, and segment customers.
- Data warehousing: A data warehouse is a central repository for data from different sources. Data warehouses are designed to store and manage large datasets in a way that makes them easy to access and analyze.
- Improved quality of care: Data mining can be used to identify patients who are at risk of developing certain diseases or conditions. This information can then be used to develop preventive care plans for these patients.
- Reduced costs: Data mining can be used to identify areas where healthcare costs can be reduced. For example, data mining can be used to identify patients who are overusing healthcare services or who are receiving unnecessary treatments.
- Improved patient outcomes: Data mining can be used to develop new and more effective treatments for diseases and conditions. For example, data mining can be used to identify genetic factors that are associated with certain diseases. This information can then be used to develop new drugs and therapies.
- Purpose: The purpose of a data warehouse is to store and manage data from different sources in a way that makes it easy to access and analyze.
- Characteristics: Data warehouses are typically characterized by the following features:
- They are subject-oriented, meaning that they are designed to support specific business needs.
- They are integrated, meaning that they combine data from different sources into a single, unified view.
- They are time-variant, meaning that they store historical data as well as current data.
- They are non-volatile, meaning that data is not overwritten or deleted.
- Components: The main components of a data warehouse are:
- A data warehouse database: This is the central repository for data in the data warehouse.
- Data extraction, transformation, and loading (ETL) tools: These tools are used to extract data from different sources, transform it into a consistent format, and load it into the data warehouse database.
- Data mining tools: These tools are used to extract knowledge and insights from the data in the data warehouse.
- Reporting tools: These tools are used to create reports and dashboards that communicate the results of the data analysis to users.