BA 1: Introduction to Business Analytic Overview of Business Analytics: - Data and analytics capabilities have made a leap forward. o Growing availability of vast amounts of data. o Improved computational power. o Development of sophisticated algorithms. - Colleges/universities have curriculum emphasizing business analytics. - Data and analytics capabilities have changed the way businesses make decisions. o Companies need data-savvy professionals. o Turn data into insights and action. - Business analytics (data analytics) involves extracting information and knowledge from data. o Improve the bottom line. o Enhance the customer experience. o Develop better marketing strategies. o Deepen customer engagement. o Enhance efficiency and reduce expenses. o Identify emerging markets. o Mitigate risk and fraud. - Business analytics is widely applied. o Marketing o Human Resources management. o Economics. o Finance. o Health, sport, and politics. - Business analytics is a broad topic. o Statics o Computer Science. o Information Systems. - Business analytics differs from data science. o Data science: Develop applications for end users. o Business analytics: Data analyses for business applications. - Business analytics combines qualitative reasoning with quantitative tools. o Identify key business problems. o Translate data analysis into decisions. o Improve business performance. - Business analytics begins with understanding the business context. o Ask the right questions. o Identify the appropriate analysis. o Communicate information. - Numerical results are not very useful unless they are accompanied with clearly stated actionable business insights. - There are three different types of analytics techniques. o Descriptive analytics: What has happened?
o Predictive analytics: What could happen in the future? o Prescriptive analytics: What should we do? - Turning data-driven recommendations into action also requires thoughtful consideration and organizational commitment beyond developing descriptive and predictive analytical models. - This needs to be updated according to the change noted in the textbook revision - Descriptive analytics: What has happened? o Gather o Organize. o Tabulate. o Visualize, o Summarize. - Phân tích mô t : tr l i câu h i v nh ng gì đã x y ra. ả ờ - Descriptive information can be presented in a number of formats. o Written reports. o Tables. o Graphs. o Maps. - Descriptive analytic is referred to as business intelligence (BI). o Access and manipulate data through reports, dashboards, applications and visualization tools. o Uses past data, integrated from multiple sources. o Inform decision-making and identify problems and solutions. - Examples: o A firm's marketing expenses and sales. o Financial reports. o Crime rates across regions and time. - Predictive Analytics: What could happen in the future? o Use historical data to make predictions.
o Analytical models help identify associations. o Associations used to estimate the likelihood of a favorable outcome. o Commonly considered advanced predictions. o Build models that help an organization understand what might happen in the future. o Use statistics and data mining. - Phân tích d đoán : là vi c s d ng s li u th ng kê và mô hình đ sác đ nh hi u su t trong t ng lai d a trên hi n t i và quá kh . ươ - Examples : o Identifying customers who are most likely to respond to specific marketing campaigns. o Transactions that are likely to be fraudulent. o Incidence of crime at certain regions and times. - Prescriptive Analytics: What should we do? o Optimization and simulation algorithm to provide advice. o Explore several possible actions. o Suggest course of action. o Commonly considered advanced predictions. o Build models that help an organization understand what might happen in the future. o Use statistics and data mining. - Phân tích đ xu t : là m t d ng c a phân tích d li u, s d ng công ngh đ giúp các doanh nghi p đ a ra quy t đ nh t t h n thông qua phân ư ế ơ tích d li u thô. - Example : o Scheduling employees' works hours. o Select a mix of products to manufacture. o Choose an investment portfolio. Types of Data: - An Important first step for making decisions is to find the right data and prepare it. o Combination of facts, figures, or other content. o Numerical and non-numerical. o All types and formats are generated from multiple sources. o Often we have a large amount of data. o Even small data can give insights. - Data that have been organized, analyzed, and processed in a meaningful and purposeful way become information. - Use a blend of data, contextual information, experience, and intuition to derive knowledge. - It is not feasible to collect data that comprise a population of all elements of interest. o Too expensive o It is impossible. - A Sample is a subset of the population and is used for analyses. - Traditional statistical techniques use sample information to draw conclusions about the population. - Cross-sectional data :
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