hoffer_mdbm9e_IM_12

hoffer_mdbm9e_IM_12 - Modern Database Management, Ninth...

Info iconThis preview shows pages 1–3. Sign up to view the full content.

View Full Document Right Arrow Icon
Modern Database Management, Ninth Edition Chapter 12 Data Quality and Integration Chapter Overview This chapter reviews the importance of data quality and integration in today’s modern organizations. The chapter begins with an in-depth discussion of data quality: what it is, what the state of data quality is in most organizations, and how data quality can be improved. The data quality discussion forms the foundation for understanding how disparate sources of data are consolidated into an integrated view for decision making and business intelligence activities. The chapter also covers data integration in terms of data federation, data propagation, and data consolidation [via extract-transform-load (ETL) processes used in data warehousing]. The chapter concludes with a brief summary of application software used in data reconciliation activities for data integration. This chapter refers to foundational material covered in Chapter 11 Data Warehousing, as well as to data modeling (covered in Chapters 3 through 6) and SQL (covered in Chapters 7 and 8). Students will gain the most from this chapter’s topics if these other chapters are covered before beginning study of data quality and integration. Chapter Objectives Specific student learning objectives are included at the beginning of the chapter. From an instructor’s point of view, the objectives of this chapter are to: 1. Impart to the student a greater appreciation for the importance of data quality in organizational information systems. 2. Provide a framework for developing a data quality program in an organization. 3. Understand the critical importance of data quality and some of the key steps that can be taken to improve data quality. 4. Provide examples and explanations regarding the challenges involved in presenting a consolidated view of data in organizations and to explain why many organizations are “drowning in data but starving from information.” 5. Provide a foundation to understanding data integration in general, and the extract-transfer- load (ETL) process in particular as an example of specific data integration for data warehousing. 6. Discuss the problems of data reconciliation. Key Terms Aggregation Data steward Refresh mode Changed data capture (CDC) Data transformation Selection Data federation Incremental extract Static extract Data governance Joining Update mode Data scrubbing Master data management (MDM) 180
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Modern Database Management, Ninth Edition Classroom Ideas 1. Inexperienced students are likely to underestimate the problems associated with establishing adequate data quality. A discussion of the various areas that must be considered, along with examples, should be conducted. Encourage students to provide examples of both poor and good data quality instances from current events or news reports. Examine through discussion how these instances of poor data quality might have been prevented with more attention to data quality in the organization(s) or what some of the contributing factors were to good data quality in this situation. 2.
Background image of page 2
Image of page 3
This is the end of the preview. Sign up to access the rest of the document.

Page1 / 19

hoffer_mdbm9e_IM_12 - Modern Database Management, Ninth...

This preview shows document pages 1 - 3. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online