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1、Chapter 101Chapter 10Data Warehousing, Analyzing, Mining, and Visualization Chapter 102Learning ObjectivesRecognize the importance of data, their managerial issues, and their life cycle.Describe the sources of data, their collection, and quality issues.Describe document management systems.Explain th
2、e operation of data warehousing and its role in decision support.Describe information and knowledge discovery and business intelligence.Understand the power and benefits of data mining.Describe data presentation methods and explain geographical information systems, visual simulations, and virtual re
3、ality as decision support tools.Discuss the role of marketing databases and provide examples.Recognize the role of the Web in data management.Chapter 103Data Management The amount of data increases exponentially with time.Data are scattered throughout organizations.Data are collected by many individ
4、uals using several methods.External data needs to be considered in making organizational decisions.Data security, quality, and integrity are critical.Selecting data management tools can be a major problem.IT applications cannot be done without using some kind of dataWhich are at the core of manageme
5、nt and marketing operations. However, managing data is difficult for various reasons.Data are an asset, when converted to information and Data are an asset, when converted to information and knowledge, give the firm competitive advantages.knowledge, give the firm competitive advantages.Chapter 104Da
6、ta Life Cycle Process 1.New data collection occurs from various sources.2.It is temporarily stored in a database then preprocessed to fit the format of the organizations data warehouse or data marts3.Users then access the warehouse or data mart and take a copy of the needed data for analysis.4.Analy
7、sis (looking for patterns) is done with Data analysis toolsData mining toolsBusinesses run on data that have been processed to information and knowledge, which mangers apply to businesses problems and opportunities. This transformation of data into knowledge and solutions is accomplished in several
8、ways.The result of all these activities is the generating of decision The result of all these activities is the generating of decision support and knowledgesupport and knowledge Chapter 105Data Life Cycle Process ContinuedThe result - generating knowledgeThe result - generating knowledge Chapter 106
9、Data Sources Internal Data Sources are usually stored in the corporate database and are about people, products, services, and processes.Personal Data is documentation on the expertise of corporate employees usually maintained by the employee. It can take the form of:estimates of salesopinions about
10、competitors business rulesProceduresEtc.External Data Sources range from commercial databases to Government reports.Internet and Commercial Database Services are accessible through the Internet.The data life cycle begins with the acquisition of data from data sources. These sources can be classified
11、 as internal, personal, and external. Chapter 107Methods for Collecting Raw Data Collection can take placein the fieldfrom individualsvia manually methodstime studiesSurveysObservationscontributions from experts using instruments and sensorsTransaction processing systems (TPS)via electronic transfer
12、from a web site (Clickstream)The task of data collection is fairly complex. Which can create data-quality problem requiring validation and cleansing of data.Chapter 108Methods for managing data collection DFM consists of a decision support systema central data request processora data integrity compo
13、nentlinks to external data suppliersthe processes used by the external data suppliers.One way to improve data collection from multiple external sources is to use a data flow manager (DFM), which takes information from external sources and puts it where it is needed, when it is needed, in a usable fo
14、rm.Chapter 109Data Quality and IntegrityIntrinsic DQ: Accuracy, objectivity, believability, and reputation.Accessibility DQ: Accessibility and access security.Contextual DQ: Relevancy, value added, timeliness, completeness, amount of data.Representation DQ: Interpretability, ease of understanding, c
15、oncise representation, consistent representation.Data quality (DQ) is an extremely important issue since quality determines the datas usefulness as well as the quality of the decisions based on the data. Data integrity means that data must be accurate, accessible, and up-to-date. Data quality is the
16、 cornerstone of effective business intelligence.Data quality is the cornerstone of effective business intelligence.Chapter 1010Document Management Maintaining paper documents, requires that:Everyone have the current versionAn update schedule be determinedSecurity be provided for the documentThe documents be distributed to the appropriate individuals in a timely mannerDocument management is the automated control of electronic documents, page images, spreadsheets, word processing documents, and ot
