Impossible Data Warehouse Situations: Solutions from the Experts
Sid Adelman, Joyce Bischoff, Jill Dyché, Douglas Hackney, Sean Ivoghli, Chuck Kelley, David Marco, Larissa T. Moss, Clay Rehm
- 出版商: Addison Wesley
- 出版日期: 2002-10-01
- 售價: $1,575
- 貴賓價: 9.5 折 $1,496
- 語言: 英文
- 頁數: 432
- 裝訂: Paperback
- ISBN: 0201760339
- ISBN-13: 9780201760330
-
相關分類:
管理與領導 Management-leadership、資料庫、Information-management
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商品描述
Table of Contents
Credits.
I IMPOSSIBLE MANAGEMENT SITUATIONS.
1. Management Issues.
The Data Warehouse Has a Record of Failure.
IT Is Unresponsive.
Management Constantly Changes.
IT Is the Assassin.
The Pilot Must Be Perfect.
User Departments Don't Want to Share Data.
Senior Management Doesn't Know What the Data Warehouse Team Does.
IT Is Unresponsive.
Management Constantly Changes.
IT Is the Assassin.
The Pilot Must Be Perfect.
User Departments Don't Want to Share Data.
Senior Management Doesn't Know What the Data Warehouse Team Does.
2. Changing Requirements and Objectives.
The Operational System Is Changing.
The Source System Constantly Changes.
The Data Warehouse Vision Has Become Blurred.
The Objectives Are Misunderstood.
The Prototype Becomes Production.
Management Doesn't Recognize the Success of the Data Warehouse Project.
The Source System Constantly Changes.
The Data Warehouse Vision Has Become Blurred.
The Objectives Are Misunderstood.
The Prototype Becomes Production.
Management Doesn't Recognize the Success of the Data Warehouse Project.
3. Justification and Budget.
User Productivity Justification Is Not Allowed.
How Can the Company Identify Infrastructure Benefits?
Does a Retailer Need a Data Warehouse?
How Can Costs Be Allocated Fairly?
Historical Data Must Be Justified.
No Money Exists for a Prototype.
How Can the Company Identify Infrastructure Benefits?
Does a Retailer Need a Data Warehouse?
How Can Costs Be Allocated Fairly?
Historical Data Must Be Justified.
No Money Exists for a Prototype.
4. Organization and Staffing.
To Whom Should the Data Warehouse Team Report?
The Organization Uses Matrix Management.
The Project Has No Consistent Business Sponsor.
Should a Line of Business Build Its Own Data Mart?
The Project Has No Dedicated Staff.
The Project Manager Has Baggage.
No One Wants to Work for the Company.
The Organization Is Not Ready for a Data Warehouse.
The Organization Uses Matrix Management.
The Project Has No Consistent Business Sponsor.
Should a Line of Business Build Its Own Data Mart?
The Project Has No Dedicated Staff.
The Project Manager Has Baggage.
No One Wants to Work for the Company.
The Organization Is Not Ready for a Data Warehouse.
5. User Issues.
The Users Want It Now.
The Business Does Not Support the Project.
Web-Based Implementation Doesn't Impress the Users.
Management Rejects Multidimensional Tools as Being Too Complex.
The Users Have High Data Quality Expectations.
The Users Don't Know What They Want.
The Business Does Not Support the Project.
Web-Based Implementation Doesn't Impress the Users.
Management Rejects Multidimensional Tools as Being Too Complex.
The Users Have High Data Quality Expectations.
The Users Don't Know What They Want.
6. Team Issues.
A Heat-Seeking Employee Threatens the Project.
Management Assigned Dysfunctional Team Members to the Data Warehouse Project.
Management Requires Team Consensus.
Prima Donnas on the Team Create Dissension.
Team Members Aren't Honest about Progress on Assignments.
A Consultant Offers to Come to the Rescue.
The Consultants Are Running the Show.
The Contractors Have Fled.
Knowledge Transfer Is Not Happening.
How Can Data Warehouse Managers Best Use Consultants?
Management Wants to Outsource the Data Warehouse Activities.
Management Assigned Dysfunctional Team Members to the Data Warehouse Project.
Management Requires Team Consensus.
Prima Donnas on the Team Create Dissension.
Team Members Aren't Honest about Progress on Assignments.
A Consultant Offers to Come to the Rescue.
The Consultants Are Running the Show.
The Contractors Have Fled.
Knowledge Transfer Is Not Happening.
How Can Data Warehouse Managers Best Use Consultants?
Management Wants to Outsource the Data Warehouse Activities.
7. Project Planning and Scheduling.
Management Requires Substantiation of Estimates.
IT Management Sets Unrealistic Deadlines.
The Sponsor Changes the Scope But Doesn't Want to Change the Schedule.
The Users Want the First Data Warehouse Delivery to Include Everything.
The Project Manager Severely Underestimates the Schedule.
IT Management Sets Unrealistic Deadlines.
The Sponsor Changes the Scope But Doesn't Want to Change the Schedule.
The Users Want the First Data Warehouse Delivery to Include Everything.
The Project Manager Severely Underestimates the Schedule.
II. IMPOSSIBLE TECHNICAL SITUATIONS.
8. Data Warehouse Standards.
The Organization Has No Experience with
Methodologies.
Database Administration Standards Are Inappropriate for the Data Warehouse.
The Employees Misuse Data Warehouse Terminology.
It's All Data Mining.
A Multinational Company Needs to Build a Business Intelligence Environment.
Database Administration Standards Are Inappropriate for the Data Warehouse.
The Employees Misuse Data Warehouse Terminology.
It's All Data Mining.
A Multinational Company Needs to Build a Business Intelligence Environment.
9. Tools and Vendors.
What Are the Best Practices for Writing a
Request for Proposals?
The Users Don't Like the Query and Reporting Tool.
OO Is the Answer (But What's the Question?).
IT Has Already Chosen the Tool.
Will the Tools Perform Well?
The Vendor Has Undue Influence.
The Rejected Vendor Doesn't Understand "No".
The Vendor's Acquiring Company Provides Poor Support.
The Users Don't Like the Query and Reporting Tool.
OO Is the Answer (But What's the Question?).
IT Has Already Chosen the Tool.
Will the Tools Perform Well?
The Vendor Has Undue Influence.
The Rejected Vendor Doesn't Understand "No".
The Vendor's Acquiring Company Provides Poor Support.
10. Ten Security.
The Data Warehouse Has No Security Plan.
Responsibility for Security Must Be Established.
Where Should a New Security Administrator Start?
Responsibility for Security Must Be Established.
Where Should a New Security Administrator Start?
11. Eleven Data Quality.
How Should Sampling Be Applied to Data Quality?
Redundant Data Needs to Be Eliminated.
Management Underestimated the Amount of Dirty Data.
Management Doesn't Recognize the Value of Data Quality.
The Data Warehouse Architect Is Obsessed with Data Quality.
The ETL Process Partially Fails.
Source Data Errors Cause Massive Updates.
Redundant Data Needs to Be Eliminated.
Management Underestimated the Amount of Dirty Data.
Management Doesn't Recognize the Value of Data Quality.
The Data Warehouse Architect Is Obsessed with Data Quality.
The ETL Process Partially Fails.
Source Data Errors Cause Massive Updates.
12. Integration.
Multiple Source Systems Require Major Data
Integration.
The Enterprise Model Is Delaying Progress.
Should a Company Decentralize?
The Business Sponsor Wants Real-Time Customer Updates.
The Company Doesn't Want Stovepipe Systems.
Reports from the Data Warehouse and Operational Systems Don't Match.
Should the Data Warehouse Team Fix an Inadequate Operational System?
The Enterprise Model Is Delaying Progress.
Should a Company Decentralize?
The Business Sponsor Wants Real-Time Customer Updates.
The Company Doesn't Want Stovepipe Systems.
Reports from the Data Warehouse and Operational Systems Don't Match.
Should the Data Warehouse Team Fix an Inadequate Operational System?
13. Data Warehouse Architecture.
The Data Warehouse Architecture Is Inadequate.
Stovepipes Are Impeding Integration.
Should Backdated Transactions Change Values in the Data Warehouse?
A Click-Stream Data Warehouse Will Be
Stovepipes Are Impeding Integration.
Should Backdated Transactions Change Values in the Data Warehouse?
A Click-Stream Data Warehouse Will Be
商品描述(中文翻譯)
目錄
致謝。
第一部分：無法解決的管理問題。
1. 管理問題。
數據倉庫一直失敗。
IT不給予回應。
管理層經常變動。
IT是刺客。
須完美無缺的試點。
用戶部門不願共享數據。
高層管理不知道數據倉庫團隊在做什麼。
2. 需求和目標的變化。
運營系統在變化。
源系統不斷變動。
數據倉庫的願景變得模糊。
目標被誤解。
原型變成了正式產品。
管理層不認可數據倉庫項目的成功。
3. 證明和預算。
不允許使用用戶生產力來證明。
公司如何確定基礎設施的好處？
零售商需要數據倉庫嗎？
如何公平分配成本？
必須證明歷史數據的價值。
沒有原型的資金。
4. 組織和人員配置。
數據倉庫團隊應該向誰匯報？
組織使用矩陣管理。
項目沒有一致的商業贊助人。
業務線是否應該建立自己的數據集市？
項目沒有專職人員。
項目經理有包袱。
沒有人願意為該公司工作。
組織尚未準備好建立數據倉庫。
5. 用戶問題。
用戶希望立即得到。
業務不支持該項目。
基於網絡的實施對用戶沒有印象。
管理層認為多維工具太複雜。
用戶對數據質量有很高的期望。
用戶不知道自己想要什麼。
6. 團隊問題。
一個熱衷的員工威脅著項目。
管理層將功能失調的團隊成員分配給數據倉庫項目。
管理層要求團隊達成共識。
團隊中的大牌引起分歧。
團隊成員對任務進展不誠實。
顧問提出幫助的建議。
顧問主導了項目。
承包商已經離開。
知識轉移沒有進行。
數據倉庫經理如何最好地利用顧問？
管理層希望外包數據倉庫活動。
7. 項目計劃和排程。
管理層要求對估算結果進行證明。
IT管理層設定不切實際的截止日期。
贊助人改變範圍，但不想改變進度表。
用戶希望第一個數據倉庫交付包含所有內容。
項目經理嚴重低估了進度。
第二部分：無法解決的技術問題。
8. 數據倉庫標準。
組織對方法論沒有經驗。
數據庫管理標準不適用於數據倉庫。
員工誤用數據倉庫術語。
一切都是數據挖掘。
跨國公司需要建立商業智能環境。
9. 工具和供應商。
撰寫請求提案的最佳實踐是什麼？
用戶不喜歡查詢和報告工具。
面向對象是答案（但問題是什麼？）。
IT已經選擇了工具。
這些工具能夠良好運行嗎？
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