E-Book, Englisch, 562 Seiten, Format (B × H): 191 mm x 235 mm
A Guide for Solution Architects and Project Leaders
E-Book, Englisch, 562 Seiten, Format (B × H): 191 mm x 235 mm
ISBN: 978-0-12-396518-9
Verlag: Academic Press
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)
- Requirements managements benefits from streamlined templates that not only define projects quickly, but ensure nothing essential is overlooked.
- Data engineering receives two new "hyper modeling" techniques, yielding data warehouses that can be easily adapted when requirements change without having to invest in ruinously expensive data-conversion programs.
- Quality assurance advances with not only a stereoscopic top-down and bottom-up planning method, but also the incorporation of the latest in automated test engines.
Use this step-by-step guide to deepen your own application development skills through self-study, show your teammates the world's fastest and most reliable techniques for creating business intelligence systems, or ensure that the IT department working for you is building your next decision support system the right way.
- Learn how to quickly define scope and architecture before programming starts
- Includes techniques of process and data engineering that enable iterative and incremental delivery
- Demonstrates how to plan and execute quality assurance plans and includes a guide to continuous integration and automated regression testing
- Presents program management strategies for coordinating multiple agile data mart projects so that over time an enterprise data warehouse emerges
- Use the provided 120-day road map to establish a robust, agile data warehousing program
Zielgruppe
<p>data warehousing professionals including architects, designers, data modelers, testers, database administrators, and project managers as well as IT managers, directors, and VPs </p>
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Angewandte Informatik Wirtschaftsinformatik
- Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Software Engineering Agile Softwareentwicklung
- Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsinformatik, SAP, IT-Management
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Warehouse
Weitere Infos & Material
1- Agile Versus Enterprise Data Warehousing (EDW)
2- Agile Requirements Management Framework
3- Agile Architectural Framework
4- Agile Data Engineering Framework
5- Agile Quality Assurance Framework
6- Agile Program Management Framework
7- Managing Frameworks via Enterprise BI Architecture Groups
List of Figures
Figure 1.1 The negative feedback loop present in most traditionally managed projects. 2 Figure 1.2 The five major components to agile enterprise data warehousing. 4 Figure 1.3 Agile EDW practices switch projects to a positive feedback loop. 4 Figure 1.4 How a team might acquire agile EDW techniques working from the inside out. 8 Figure 2.1 Mind map of generic iterative methods summarized in Chapters 2 and 3. 14 Figure 2.2 A family tree of methods and influences leading to the agile EDW method. 15 Figure 2.3 The traditional waterfall method. 17 Figure 2.4 The Agile manifesto cover page. 17 Figure 2.5 Values and principles of the agile manifesto and Extreme Programming. 18 Figure 2.6 The essence of the Scrum method. 21 Figure 2.7 Typical user story. 22 Figure 2.8 A sample Scrum task board as it would appear in mid-iteration. 24 Figure 2.9 A Scrum burndown chart as it would appear in mid-iteration. 25 Figure 3.1 Lean values, principles, and tools. 33 Figure 3.2 Value-stream analysis of development work for a challenged waterfall project. 34 Figure 3.3 Typical Kanban work board. 42 Figure 3.4 Kanban-style cumulative flow diagram. 43 Figure 3.5 Sample cycle time distribution analysis for a Kanban team. 44 Figure 3.6 Typical stages of “Scrumban”—the transition from Scrum to Kanban. 48 Figure 3.7 Two-tiered Scrumban task board. 48 Figure 3.8 Values and principles of the Rational Unified Process. 50 Figure 3.9 RUP Whale Chart. 51 Figure 3.10 Google Ngram of “Scrum” and “RUP” through 2008. 53 Figure 4.1 Business organizational terms used in this book. 64 Figure 4.2 Business conceptual model. 72 Figure 4.3 Logical data model. 72 Figure 4.4 Physical data model. 73 Figure 4.5 Sample enterprise data warehouse “reference architecture”. 75 Figure 4.6 Zachman framework adapted for an enterprise data warehousing program. 77 Figure 4.7 DAMA’s framework for data management functions. 78 Figure 4.8 Hammergren’s matrix for sequencing DW/BI development work. 79 Figure 5.1 Typical RUP-style whale chart for an agile EDW project. 91 Figure 5.2 Agile EDW user stories result in too many developer stories for one, short Iteration. 92 Figure 5.3 Deriving developer stories from user stories. 94 Figure 5.4 A “current estimate” for an agile data warehousing project. 96 Figure 5.5 Agile data warehousing requires pipelined work specialties. 99 Figure 5.6 Work packages tend to flow diagonally across technical specialties and iterations. 101 Figure 5.7 Cycle time distribution analysis for an agile data warehousing project. 103 Figure 5.8 A current estimate adjusted for observed delivery cycle times. 104 Figure 5.9 Success rates for agile data warehousing teams, by number of agile projects completed, compared to traditional methods. 105 Figure 5.10 Agile’s impact upon key performance indicators for data warehousing development projects. 105 Figure 5.11 Agile data warehousing surveys indicate that practitioners have overcome some challenge areas. 107 Figure 6.1 Relative cost of correcting defects grows by 100 between requirements and promotion into production. 112 Figure 6.2 Incremental delivery mitigates risk by increasing the number of product check points. 113 Figure 6.3 The sources of EDW project risk mitigated with three types of iterations. 115 Figure 6.4 Relative timing for the three types of iterations that Agile EDW employs. 118 Figure 7.1 Mind map of topics addressed in Part III. 126 Figure 7.2 Sample EDW requirements expressed at three levels. 127 Figure 7.3 Waterfall-style requirements management. 131 Figure 7.4 Typical requirements work breakdown for a traditional project. 133 Figure 7.5 As-is business process diagram showing a sample work flow requiring re-engineering. 135 Figure 7.6 To-be business process re-engineered to use EDW to communicate between agents. 136 Figure 7.7 Accuracy vs. precision. 137 Figure 7.8 Standard risk analysis. 140 Figure 7.9 Standard analysis adjusted for dollar value of each type of risk. 141 Figure 7.10 Agile EDW’s requirements management benefits greatly from intersecting value chains. 146 Figure 7.11 Overall agile EDW requirements management plan. 148 Figure 7.12 Enterprise requirements management roles. 150 Figure 8.1 Big picture – decomposing epics into a backlog of stories. 152 Figure 8.2 Immediate business stakeholder formalizing all levels of stories by linking them to the hierarchy among business stakeholders. 153 Figure 8.3 Primary technique for decomposing user stories into developers stories. Note the 25-to-1 multiplier for this project’s user story. 158 Figure 8.4 INVEST and DILBERT’S test. 158 Figure 8.5 Big picture – recompling modules for perceived value. 160 Figure 8.6 Value build-up charts distingishing between delivery environments. 166 Figure 9.1 User modeling example. 171 Figure 9.2 Business...
