MBA 6207 - Chapter 9 Business Intelligence and Analytics| latest full solution

MBA 6207 - Chapter 9 Business Intelligence and Analytics| latest full solution Business Intelligence (BI) - A wide range of applications, practices, and technologies for the extraction, transformation, integration, visualization, analysis, interpretation, and presentation of data to support improved decision making. Which of the following is NOT considered business intelligence practice? - transaction processing Suppose management wishes to start a BI project at your new job. Which of following will you recommend as the first step? 1) Clarify business goals and design a project plan 2) Gather data to be used, become familiar with the data and identify any data quality problems 3) Select a subset of data to be used, clean data to address quality issues, and transform data into form suitable for analysis 4) Deploy the model into the org. decision-making process - Clarify business goals and design a project plan data cube - A collection of data that contains numeric facts called measures, which are categorized by dimensions, such as time and geography. example: unit sales for a specific item, on a specific day for all stores within each market Conversion Funnel - A graphical representation that summarizes the steps a consumer takes in making the decision to buy your product and become a customer. Provides a visual representation of the conversion data between each step and enables decision makers to see what steps are causing customers confusion and trouble. During modeling of the CRISP-DM method, we would ______. - apply selected modeling techniques data mining - A BI analytics tool used to explore large amounts of data for hidden patterns to predict future trends and behaviors for use in decision making Cross-Industry Process for Data Mining (CRISP-DM) - A six-phase structured approach for the planning and execution of a data mining project Six-Phase structure goals Phase 1: Business Understanding - Goal 1: Clarify the business goals for the data mining project, convert the goals into a predictive analysis problem, and design a project plan to accomplish these objectives Six-Phase structure goals Phase 2: Data Understanding - Goal 2: Gather data to be used (may involve multiple sources), become familiar with the data, and identify any data quality problems (lack of data, missing data, data needs adjustment, etc.) that must be addressed. Six-Phase structure goals Phase 3: Data Preparation - Goal 3: Select a subset of data to be used, clean data to address quality issues, and transform data into form suitable for analysis. Six-Phase structure goals Phase 4: Modeling - Goal 4: Apply selected modeling techniques Six-Phase structure goals Phase 5: Evaluation - Goal 5: Assess if the model achieves business goals Six-Phase structure goals Phase 6: Deployment - Goal 6: Deploy the model into the organization's decision-making process Data governance involves identifying people who are responsible for fixing and preventing issues with data True or False - True is the core component of data management; it defines the roles, responsibilities, and processes for ensuring that data can be trusted and used by the entire organization, with people identified and in place who are responsible for fixing and preventing issues with data. Role of data scientist - - individuals who combine strong business acumen - deep understanding of analytics - appreciation of limitations of their data, tools, techniques to deliver improved decision making - GOAL: to uncover valuable insights that will influence organizational decisions and help the organization to achieve competitive advantage - Education requirements require a mastery of statistics, math, and computer programming