Chapter 3      Data Management: Data,    Databases and Warehousing1
Learning Objectives       Recognize the importance of data, managerial        issues, and life cycle       Describe sour...
Learning Objectives (Continued)       Understand conceptual, logical, and physical data       Understand ERD       The ...
Introduction     Corporate data are key strategic assets.     Managing data quality is vital to the organizations.     ...
Goal of data management     The goal of DM is provide the infrastructure to       transform raw data into corporate infor...
Building Blocks of DM     Data profiling:         Understanding the data     Data quality management:         Improvin...
Data Problems and Difficulties     Exponential increase in the data volume with      time.     Scattered data across org...
-cont…     Selection of data management tools.     Validity of the data.     Maintenance of data to eradicate redundanc...
Solution to Managing Data     Organizing data in a hierarchical format in one      location.     Supports secured and ef...
Data Life Cycle Process10    Chapter 3
Transactional vs. Analytical Data Processing          Transactional processing takes place in operational systems (TPS) t...
-cont…      Supplementary activity to transaction processing is       called analytical processing, which involves the   ...
Data Sources      Organizational Data: Organizational internal data       about people, products, services, processes,   ...
Methods for Collecting Raw Data      Manually        Surveys, observations, contributions from experts..      Electroni...
Data Quality      Data quality determines the data’s usefulness as       well as the quality of the decisions based on th...
Categories of Data Quality      Standardization (for consistency)      Matching (of data if stored in different places) ...
3-Step Method for DM      Analyzing the actual organizational processes      Moving on to analyzing the entities that th...
??? To Understand Business Flow      What information is input to the process?      What information is changed or creat...
Data Privacy, Cost and Ethics      Collecting data raises the concern about privacy       protection.      Data collecte...
Document Management      DM is the automated control of        electronic documents,        page images,        spread...
Benefits of DM      Allows organization to exert greater control over        production, storage and distribution of doc...
Major Tools for DM      Workflow software      Authoring tools      Scanners      Databases22
DMSs      Document Management Systems:       provide decision makers with information in an        electronic format and...
Hierarchy of Data24   Chapter 3
Hierarchy of Data (cont’d)25   Chapter 3
The Data Warehouse & Data Management26    Chapter 3
Marketing Databases in Action      Introduction:      Data warehouses and data marts serve end users       in all functi...
Marketing Transaction Databases      MD provides effective means of capturing       information on customer preferences a...
Web-based Data Management Systems –     content and information29     Chapter 3
Managerial Issues Cost-benefit issues and justification Where to store data physically Legal issues Internal or extern...
Managerial Issues (Continued)     Disaster recovery     Data security and ethics     Ethics: Paying for use of data    ...
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3 data mgmt

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3 data mgmt

  1. 1. Chapter 3 Data Management: Data, Databases and Warehousing1
  2. 2. Learning Objectives  Recognize the importance of data, managerial issues, and life cycle  Describe sources of data, collection, and quality  Describe DMS  Describe Data Warehousing and Analytical Processing  Describe DBMS (benefits and issues)2
  3. 3. Learning Objectives (Continued)  Understand conceptual, logical, and physical data  Understand ERD  The importance of Marketing  The Internet and Data Management3
  4. 4. Introduction  Corporate data are key strategic assets.  Managing data quality is vital to the organizations.  Dirty data results in:  Poor business decisions  Poor customer service  Inadequate product design4
  5. 5. Goal of data management  The goal of DM is provide the infrastructure to transform raw data into corporate information of the highest quality.5 Chapter 3
  6. 6. Building Blocks of DM  Data profiling:  Understanding the data  Data quality management:  Improving the quality of data  Data integration:  Combining similar data from multiple sources  Data augmentation:  Improving the value of the data6 Chapter 3
  7. 7. Data Problems and Difficulties  Exponential increase in the data volume with time.  Scattered data across organization collected and stored through various methods and devices.  Consideration of external data for decision making  Data security, quality and integrity.7
  8. 8. -cont…  Selection of data management tools.  Validity of the data.  Maintenance of data to eradicate redundancy and obsolete.8
  9. 9. Solution to Managing Data  Organizing data in a hierarchical format in one location.  Supports secured and efficient high-volume processing.  RDBMS add to facilitate end-user computing and decision support.  Data-warehousing.9
  10. 10. Data Life Cycle Process10 Chapter 3
  11. 11. Transactional vs. Analytical Data Processing  Transactional processing takes place in operational systems (TPS) that provide the organization with the capability to perform business transactions and produce transaction reports. The data are organized mainly in a hierarchical structure and are centrally processed.  This is done primarily for fast and efficient processing of routine, repetitive data.11 Chapter 3
  12. 12. -cont…  Supplementary activity to transaction processing is called analytical processing, which involves the analysis of accumulated data.  Analytical processing, sometimes referred to as business intelligence, includes data mining, decision support systems (DSS), querying, and other analysis activities.  These analyses place strategic information in the hands of decision makers to enhance productivity and make better decisions, leading to greater competitive advantage.12
  13. 13. Data Sources  Organizational Data: Organizational internal data about people, products, services, processes, equipment, machinery etc.  End User Data: Data created by the IS users or other corporate employees. They may include facts, concepts, thoughts and opinions.  External Data: Commercial databases, sensors, satellites, government reports, secondary storage devices, internet servers, etc..13
  14. 14. Methods for Collecting Raw Data  Manually  Surveys, observations, contributions from experts..  Electronically  H/W and S/W for data storage, communication, transmission and presentation.  Online surveys, online polls, data warehousing, website profiling, data that is scanned/transmitted.  CLICKSTREAM DATA: Data that can be collected automatically using special software from the company’s web site.14
  15. 15. Data Quality  Data quality determines the data’s usefulness as well as the quality of the decisions based on the data.  Data quality dimensions:  Accuracy  Accessibility  Relevance  Timeliness  Completeness15
  16. 16. Categories of Data Quality  Standardization (for consistency)  Matching (of data if stored in different places)  Verification (against the source)  Enhancement (adding of data to increase its usefulness)16
  17. 17. 3-Step Method for DM  Analyzing the actual organizational processes  Moving on to analyzing the entities that these elements comprise  Finish by analyzing the relationship between the data in the business processes that the data must support.17
  18. 18. ??? To Understand Business Flow  What information is input to the process?  What information is changed or created during the process?  What happens to the information once the process is complete?  What value-added information does the process produce?18
  19. 19. Data Privacy, Cost and Ethics  Collecting data raises the concern about privacy protection.  Data collected is about:  Employees  Customers  Other people  Accessible to authorized people only.  Reasonable costs for collection, storage and use19
  20. 20. Document Management  DM is the automated control of  electronic documents,  page images,  spreadsheets,  voice word processing documents, and  other complex documents through their entire life cycle within an organization.20
  21. 21. Benefits of DM  Allows organization to exert greater control over  production, storage and distribution of documents,  yielding greater efficiency in the reuse of information ,  the control of a documents through a workflow process and  the reduction of product cycle times.  Deals with knowledge,data and information.21
  22. 22. Major Tools for DM  Workflow software  Authoring tools  Scanners  Databases22
  23. 23. DMSs  Document Management Systems:  provide decision makers with information in an electronic format and usually include computerized imaging systems that can result in substantial savings.23
  24. 24. Hierarchy of Data24 Chapter 3
  25. 25. Hierarchy of Data (cont’d)25 Chapter 3
  26. 26. The Data Warehouse & Data Management26 Chapter 3
  27. 27. Marketing Databases in Action  Introduction:  Data warehouses and data marts serve end users in all functional areas.  Dramatic applications of DW and DM are seen in Marketing Databases.  Marketing today requires:  New databases oriented towards targeting personalizing marketing messages in real time.27
  28. 28. Marketing Transaction Databases  MD provides effective means of capturing information on customer preferences and needs.  Enterprises, use this knowledge to create new and/or personalized products and services.  Combines many of the characteristics of current databases and marketing data sources into a new database that allows marketers to engage in real- time personalization and target every interaction with customers.28
  29. 29. Web-based Data Management Systems – content and information29 Chapter 3
  30. 30. Managerial Issues Cost-benefit issues and justification Where to store data physically Legal issues Internal or external? Data Delivery30 Chapter 3
  31. 31. Managerial Issues (Continued) Disaster recovery Data security and ethics Ethics: Paying for use of data Privacy Legacy Data31 Chapter 3
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