BA 1: Introduction to Business Analytic Overview of Business Analytics: -Data and analytics capabilities have made a leap forward. oGrowing availability of vast amounts of data. oImproved computational power. oDevelopment of sophisticated algorithms. -Colleges/universities have curriculum emphasizing business analytics. -Data and analytics capabilities have changed the way businesses make decisions. oCompanies need data-savvy professionals. oTurn data into insights and action. -Business analytics (data analytics) involves extracting information and knowledge from data. oImprove the bottom line. oEnhance the customer experience. oDevelop better marketing strategies. oDeepen customer engagement. oEnhance efficiency and reduce expenses. oIdentify emerging markets. oMitigate risk and fraud. -Business analytics is widely applied. oMarketing oHuman Resources management. oEconomics. oFinance. oHealth, sport, and politics. -Business analytics is a broad topic. oStatics oComputer Science. oInformation Systems. -Business analytics differs from data science. oData science: Develop applications for end users. oBusiness analytics: Data analyses for business applications. -Business analytics combines qualitative reasoning with quantitative tools. oIdentify key business problems. oTranslate data analysis into decisions. oImprove business performance. -Business analytics begins with understanding the business context. oAsk the right questions. oIdentify the appropriate analysis. oCommunicate 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. oDescriptive analytics: What has happened?
oPredictive analytics: What could happen in the future? oPrescriptive 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? oGather oOrganize. oTabulate. oVisualize, oSummarize. -Phân tích mô t:ảtrli câu hi vnhng gì đã xy ra. ả ờỏềữả-Descriptive information can be presented in a number of formats. oWritten reports. oTables. oGraphs. oMaps. -Descriptive analytic is referred to as business intelligence (BI). oAccess and manipulate data through reports, dashboards, applications and visualization tools. oUses past data, integrated from multiple sources. oInform decision-making and identify problems and solutions. -Examples: oA firm's marketing expenses and sales. oFinancial reports. oCrime rates across regions and time. -Predictive Analytics: What could happen in the future? oUse historical data to make predictions.
oAnalytical models help identify associations. oAssociations used to estimate the likelihood of a favorable outcome. oCommonly considered advanced predictions. oBuild models that help an organization understand what might happen in the future. oUse statistics and data mining. -Phân tích dđoánự: là vic sdng sliu thng kê và mô hình đsác đ nh hiu sut ệửụốệốểịệấtrong tng lai da trên hin ti và quá kh. ươựệạứ-Examples: oIdentifying customers who are most likely to respond to specific marketing campaigns. oTransactions that are likely to be fraudulent. oIncidence of crime at certain regions and times. -Prescriptive Analytics: What should we do? oOptimization and simulation algorithm to provide advice. oExplore several possible actions. oSuggest course of action. oCommonly considered advanced predictions. oBuild models that help an organization understand what might happen in the future. oUse statistics and data mining. -Phân tích đxutềấ: là mt dng ca phân tích dliu, sdng công nghđgiúp các ộạủữệửụệểdoanh nghip đa ra quyt đ nh tt hn thông qua phânệưếịốơtích dliu thô. ữệ-Example: oScheduling employees' works hours. oSelect a mix of products to manufacture. oChoose an investment portfolio. Types of Data: -An Important first step for making decisions is to find the right data and prepare it. oCombination of facts, figures, or other content. oNumerical and non-numerical. oAll types and formats are generated from multiple sources. oOften we have a large amount of data. oEven 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. oToo expensive oIt 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: