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Data Strategy
- By Sid Adelman, Larissa Moss, Majid Abai
- Published Jun 15, 2005 by Addison-Wesley Professional.
Book
- Sorry, this book is no longer in print.
About
Features
Delivers the most current thinking and best practices in developing data management systems with less risk and a higher success rate.
° Reader will be challenged to develop their own set of goals and success criteria.
° Shows how a data strategy can give an organization a competitive edge.
° Suggests best practices to determine what an organization needs.
° Liberal use of Case Studies.
Description
- Copyright 2005
- Dimensions: 7" x 9-1/4"
- Pages: 384
- Edition: 1st
- Book
- ISBN-10: 0-321-24099-5
- ISBN-13: 978-0-321-24099-6
The definitive best-practices guide to enterprise data-management strategy.
You can no longer manage enterprise data "piecemeal." To maximize the business value of your data assets, you must define a coherent, enterprise-wide data strategy that reflects all the ways you capture, store, manage, and use information.
In this book, three renowned data management experts walk you through creating the optimal data strategy for your organization. Using their proven techniques, you can reduce hardware and maintenance costs, and rein in out-of-control data spending. You can build new systems with less risk, higher quality, and improve data access. Best of all, you can learn how to integrate new applications that support your key business objectives.
Drawing on real enterprise case studies and proven best practices, the author team covers everything from goal-setting through managing security and performance. You'll learn how to:
Identify the real risks and bottlenecks you face in delivering data—and the right solutions
Integrate enterprise data and improve its quality, so it can be used more widely and effectively
Systematically secure enterprise data and protect customer privacy
Model data more effectively and take full advantage of metadata
Choose the DBMS and data storage products that fit best into your overall plan
Smoothly accommodate new Business Intelligence (BI) and unstructured data applications
Improve the performance of your enterprise database applications
Revamp your organization to streamline day-to-day data management and reduce cost
Data Strategy is indispensable for everyone who needs to manage enterprise data more efficiently—from database architects to DBAs, technical staff to senior IT decision-makers.
© Copyright Pearson Education. All rights reserved.
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Table of Contents
Acknowledgments.
About the Authors.
Foreword.
1. Introduction.
Current Status in Contemporary Organizations.
Why a Data strategy Is Needed.
Value of Data as an Organizational Asset.
Vision and Goals of the Enterprise.
Support of the IT Strategy.
Components of a Data Strategy.
Data Integration.
Data Quality.
Metadata.
Data Modeling.
Organizational Roles and Responsibilities.
Performance and Measurement.
Security and Privacy.
DBMS Selection.
Business Intelligence.
Unstructured Data.
Business Value of Data and ROI.
How Will You Develop and Implement a Data Strategy?
Data Environment Assessment.
References.
2. Data Integration.
Ineffective “Silver-Bullet” Technology Solutions.
Enterprise Resource Planning (ERP).
Data Warehousing (DW).
Customer Relationship Management (CRM).
Enterprise Application Integration (EAI).
Gaining Management Support.
Business Case for Data Integration.
Integrating Business Data.
Know Your Business Entities.
Mergers and Acquisitions.
Data Redundancy.
Data Lineage.
Multiple DBMSs and Their Impact.
Deciding What Data Should Be Integrated.
Data Integration Prioritization.
Risks of Data Integration.
Consolidation and Federation.
Data Consolidation.
Data Federation.
Data Integration Strategy Capability Maturity Model.
Getting Started.
Conclusion.
References.
3. Data Quality.
Current State of Data Quality.
Recognizing Dirty Data.
Data Quality Rules.
Business Entity Rules.
Business Attribute Rules.
Data Dependency Rules.
Data Validity Rules.
Data Quality Improvement Practices.
Data Profiling.
Data Cleansing.
Data Defect Prevention.
Enterprise-Wide Data Quality Disciplines.
Data Quality Maturity Levels.
Standards and Guidelines.
Development Methodology.
Data Naming and Abbreviations.
Metadata.
Data Modeling.
Data Quality.
Testing.
Reconciliation.
Security.
Data Quality Metrics.
Enterprise Architecture.
Data Quality Improvement Process.
Business Sponsorship.
Business Responsibility for Data Quality.
Conclusion.
References.
4. Metadata.
Why Metadata Is Critical to the Business.
Metadata as the Keystone.
Management Support for Metadata.
Starting a Metadata Management Initiative.
Metadata Categories.
Business Metadata.
Technical Metadata.
Process Metadata.
Usage Metadata.
Metadata Sources.
Metadata Repository.
Buying a Metadata Repository Product.
Building a Metadata Repository.
Centralized Metadata Repository.
Distributed Metadata Repository.
XML-Enabled Metadata Repository.
Developing a Metadata Repository.
Justification.
Planning.
Analysis.
Design.
Construction.
Deployment.
Managed Metadata Environment.
Metadata Sourcing.
Metadata Integration.
Metadata Management.
Metadata Marts.
Metadata Delivery.
Communicating and Selling Metadata.
Conclusion.
References.
5. Data Modeling.
Origins of Data Modeling.
Significance of Data Modeling.
Logical Data Modeling Concepts.
Process-Independence.
Business-Focused Data Analysis.
Data Integration (Single Version of Truth).
Data Quality.
Enterprise Logical Data Model.
Big-Bang Versus Incremental.
Top-Down versus Bottom-Up.
Physical Data Modeling Concepts.
Process-Dependence.
Database Design.
Physical Data Modeling Techniques.
Denormalization.
Surrogate Keys.
Indexing.
Partitioning.
Database Views.
Dimensionality.
Star Schema.
Snowflake.
Starflake.
Factors that Influence the Physical Data Model.
Guideline 1 :High Degree of Normalization for Robustness.
Guideline 2 :Denormalization for Short-Term Solutions.
Guideline 3 :Usage of Views on Powerful Servers.
Guideline 4 :Usage of Views on Powerful RDBMS Software.
Guideline 5 :Cultural Influence on Database Design.
Guideline 6 :Modeling Expertise Affects Database Design.
Guideline 7 :User-Friendly Structures.
Guideline 8 :Metric Facts Determine Database Design.
Guideline 9 :When to Mimic Source Database Design.
Conclusion.
References.
6. Organizational Roles and Responsibilities.
Building the Teams Who Create and Maintain the Strategy.
Resistance to Change.
Existing Organization.
Resistance to Standards.
“Reasons” for Resistance.
Optimal Organizational Structures.
Distributed Organizations.
Outsourced Personnel.
Training.
Who Should Attend.
Mindset.
Choice of Class.
Timing.
Roles and Responsibilities.
Data Strategist.
Database Administrator.
Data Administrator.
Metadata Administrator.
Data Quality Steward.
Consultants and Contractors.
Security Officer.
Sharing Data.
Strategic Data Architect.
Technical Services.
Data Ownership.
Domains.
Security and Privacy.
Availability Requirements.
Timeliness and Periodicity Requirements.
Performance Requirements.
Data Quality Requirements.
Business Rules.
Information Stewardship.
Steward Deliverables.
Key Skills and Competencies.
Worst Practices.
Agenda for Weekly Data Strategy Team Meeting.
Conclusion.
7. Performance.
Performance Requirements.
Service Level Agreements.
Response Time.
Capacity Planning: Performance Modeling.
Capacity Planning: Benchmarks.
Why Pursue a Benchmark?
Benchmark Team.
Benefits of a Good Benchmark: Goals and Objectives.
Problems with “Standard” Benchmarks.
The Cost of Running a Benchmark.
Identifying and Securing Data.
Establishing Benchmark Criteria and Methodology.
Evaluating and Measuring Results.
Verifying and Reconciling Results.
Communicating Results Effectively.
Application Packages: Enterprise Resource Planning (ERPs).
Designing, Coding, and Implementing.
Designing.
Coding.
Implementation.
Design Reviews.
Setting User Expectations.
Monitoring (Measurement).
Conformance to Measures of Success191
Types of Metrics191
Responsibility for Measurement.
Means to Measure.
Use of Measurements.
Return on Investment (ROI).
Reporting Results to Management.
Tuning.
Tuning Options.
Reporting Performance Results.
Selling Management on Performance.
Case Studies.
Performance Tasks.
Conclusion.
References.
8. Security and Privacy of Data.
Data Identification for Security and Privacy.
User Role.
Roles and Responsibilities.
Security Officer.
Data Owner.
System Administrator.
Regulatory Compliance.
Auditing Procedures.
Security Audits.
External Users of Your Data.
Design Solutions.
Database Controls.
Security Databases.
Test and Production Data.
Data Encryption.
Standards for Data Usage.
Impact of the Data Warehouse.
Vendor Issues.
Software.
External Data.
Communicating and Selling Security.
Security and Privacy Indoctrination.
Monitoring Employees.
Training.
Communication.
Best Practices and Worst Practices.
Identify Your Own Sensitive Data Exercise.
Conclusion.
9. DBMS Selection.
Existing Environment.
Capabilities and Functions.
DBMS Choices.
Why Standardize the DBMS?
Integration Problems.
Greater Staff Expense.
Software Expense.
Total Cost of Ownership.
Hardware.
Network Usage.
DBMS.
Consultants and Contractors.
Internal Staff.
Help Desk Support.
Operations and System Administration.
IT Training.
Application Packages and ERPs.
Criteria for Selection.
Selection Process.
Reference Checking.
Alternatives to Reference Checking.
Selecting and Gathering References.
Desired Types of References.
The Process of Reference Checking.
Questions to Ask.
RFPs for DBMSs.
RFP Best Practices.
Response Format.
Evaluating Vendors.
Dealing with the Vendor.
Performance.
Vendor’s Level of Service.
Early Code.
Rules of Engagement.
Set the Agenda for Meetings and Presentations.
Professional Employee Information.
Financial Information.
Selection Matrix—–Categorize Capabilities and Functions.
Exercise–How Well Are You Using Your DBMS?255
Conclusion.
References.
10. Business Intelligence.
What Is Business Intelligence?
A Brief History.
Importance of BI.
BI Components.
Data Warehouse.
Metadata Repository.
Data Transformation and Cleansing.
OLAP and Analytics.
Data Presentation and Visualization.
Important BI Tools and Processes.
Data Mining.
Rule-Based Analytics.
Balanced Scorecard.
Digital Dashboard.
Emerging Trends and Technologies.
Mining Structured and Unstructured Data.
Radio Frequency Identification.
BI Myths and Pitfalls.
Conclusion.
References.
11. Strategies for Managing Unstructured Data.
What Is Unstructured Data?
A Brief History.
Why Now?
Current State of Unstructured Data in Organizations.
A Unified Content Strategy for the Organization.
Definition of a Unified Content Strategy.
Storage and Administration.
Content Reusability.
Search and Delivery.
Combining Structured and Unstructured Data.
Emerging Technologies.
Digital Asset Management Software.
Digital Rights Management Software.
Electronic Medical Records.
Conclusion.
References.
12. Business Value of Data and ROI.
The Business Value of Data.
Companies that Sell Customer Data.
Internal Information Gathered About Customers.
Call Center Data.
Click-Stream Data.
Demographics.
Channel Preferences.
Direct Retailers.
Loyalty Cards.
Travel Data.
Align Data with Strategic Goals.
ROI Process.
The Cost of Developing a Data Strategy.
Data Warehouse.
Hardware.
Software.
Personnel Costs.
Training.
Operations and System Administration.
Total Cost of Ownership.
Benefits of a Data Strategy.
The Data Warehouse.
Estimating Tangible Benefits.
Estimating Intangible Benefits.
Post-Implementation Benefits Measurement.
Conclusion.
Reference.
Appendix A: ROI Calculation Process, Cost Template, and Intangible Benefits Template.
Cost of Capital.
Risk.
ROI Example.
Net Present Value.
Internal Rate of Return.
Payback Period.
Cost Calculation Template.
Intangible Benefits Calculation Template.
Reference.
Appendix B: Resources.
Publications.
Websites.
Index.
Preface
Besides inflexibility, the lack of enterprise IT planning has lead to epidemic levels of data redundancy. In my experience, most major corporations and large government organizations have three- to four-fold "needless data redundancy"—data that exists for no other reason than failure to properly plan and implement. This issue has become so pressing that it has entered into the chief executive officer (CEO)'s key corporate objectives. I have personally witnessed several CEOs declare that their organization must simplify its IT portfolio, so that redundant data and applications can be removed.
Many organizations target enterprise data strategy as one of the key initiatives to reduce data redundancy, simplify IT portfolios, and ease the strain on the architectures of applications. Through metadata management, an enterprise data strategy identifies how data should be constructed, what data exists, and what the meaning of that data is. This helps organizations address data redundancy by showing when a proposed new system will replicate existing applications. This is a critical aspect of data strategy because many companies want to consolidate existing redundant applications, but processes are not in place to prevent new redundancy from entering the IT environment. Thus, an effective enterprise data strategy can save organizations that currently operate as the proverbial sinking ships whose crews are bailing water, but cannot plug the leaks. A sound enterprise data strategy not only "bails water" by affording IT staff the means and methods for reducing existing redundant data, but it can "plug the leaks" by ensuring that new redundancies stop flowing into the organization.
Sid Adelman, Larissa Moss, and Majid Abai's book represents an outstanding achievement in defining the key activities for implementing a successful enterprise data strategy. Their real-world experience assisting companies shines throughout the book and makes it a must read for any IT professional.
—David Marco, President of EWSolutions
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