Impossible Data Warehouse Situations: Solutions from the Experts
暫譯: 不可能的資料倉儲情境：專家解決方案

Sid Adelman, Joyce Bischoff, Jill Dyché, Douglas Hackney, Sean Ivoghli, Chuck Kelley, David Marco, Larissa T. Moss, Clay Rehm

Impossible Data Warehouse Situations: Solutions from the Experts

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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.


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.


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.


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.


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.


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.


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.

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.


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.


10. Ten Security.

The Data Warehouse Has No Security Plan.
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.


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?


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

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目錄

致謝。

I 不可能的管理情境。

1. 管理問題。
資料倉儲有失敗的紀錄。
IT 不回應。
管理層不斷變動。
IT 是刺客。
飛行員必須完美。
使用部門不想分享數據。
高層管理對資料倉儲團隊的工作一無所知。

2. 需求和目標的變更。
操作系統正在變化。
來源系統不斷變更。
資料倉儲的願景變得模糊。
目標被誤解。
原型變成生產。
管理層不認可資料倉儲專案的成功。

3. 正當性和預算。
不允許用戶生產力的正當性。
公司如何識別基礎設施的好處？
零售商需要資料倉儲嗎？
如何公平分配成本？
歷史數據必須得到正當性。
沒有資金用於原型。

4. 組織和人員配置。
資料倉儲團隊應向誰報告？
組織使用矩陣管理。
專案沒有一致的業務贊助者。
業務部門應該建立自己的數據集市嗎？
專案沒有專職人員。
專案經理有包袱。
沒有人想為公司工作。
組織尚未準備好資料倉儲。

5. 用戶問題。
用戶想要立即獲得。
業務不支持該專案。
基於網頁的實施未能打動用戶。
管理層拒絕多維工具，認為其過於複雜。
用戶對數據質量的期望很高。
用戶不知道他們想要什麼。

6. 團隊問題。
一名尋求熱點的員工威脅專案。
管理層將功能失調的團隊成員分配到資料倉儲專案。
管理層要求團隊達成共識。
團隊中的明星成員造成不和。
團隊成員對任務進度不誠實。
一位顧問提出來救援。
顧問們在主導專案。
承包商已經逃跑。
知識轉移未能發生。
資料倉儲經理如何最好地利用顧問？
管理層希望外包資料倉儲活動。

7. 專案規劃和排程。
管理層要求對估算進行證明。
IT 管理設置不切實際的截止日期。
贊助者改變範圍但不想改變時間表。
用戶希望第一次資料倉儲交付包含所有內容。
專案經理嚴重低估了時間表。

II 不可能的技術情境。

8. 資料倉儲標準。
組織對方法論沒有經驗。
資料庫管理標準不適用於資料倉儲。
員工誤用資料倉儲術語。
這都是數據挖掘。
一家跨國公司需要建立商業智慧環境。

9. 工具和供應商。
撰寫提案請求的最佳實踐是什麼？
用戶不喜歡查詢和報告工具。
OO 是答案（但問題是什麼？）。
IT 已經選擇了工具。
這些工具的性能會好嗎？
供應商有不當影響。
被拒絕的供應商不理解「不」。
供應商的收購公司提供的支持很差。

10. 安全性。
資料倉儲沒有安全計劃。
必須確立安全責任。
新的安全管理員應該從哪裡開始？

11. 數據質量。
如何將抽樣應用於數據質量？
冗餘數據需要被消除。
管理層低估了髒數據的數量。
管理層不認識數據質量的價值。
資料倉儲架構師對數據質量情有獨鍾。
ETL 過程部分失敗。
來源數據錯誤導致大規模更新。

12. 整合。
多個來源系統需要重大數據整合。
企業模型正在延遲進展。
公司應該去中心化嗎？
業務贊助者希望實時客戶更新。
公司不希望有孤立系統。
來自資料倉儲和操作系統的報告不匹配。
資料倉儲團隊應該修復不充分的操作系統嗎？

13. 資料倉儲架構。
資料倉儲架構不充分。
孤立系統妨礙整合。
回溯交易應該改變資料倉儲中的值嗎？
點擊流資料倉儲將會...

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