Practical Data Quality Learn Practical, Real-World Strategies to Transform the Quality of Data in Your Organization
Identify data quality issues, leverage real-world examples and templates to drive change, and unlock the benefits of improved data in processes and decision-making Key Features Get a practical explanation of data quality concepts and the imperative for change when data is poor Gain insights into lin...
| Other Authors: | , |
|---|---|
| Format: | eBook |
| Language: | Inglés |
| Published: |
Birmingham, England :
Packt Publishing Ltd
[2023]
|
| Edition: | First edition |
| Subjects: | |
| See on Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009769034306719 |
Table of Contents:
- Cover
- Title Page
- Copyright and Credits
- Dedication
- Foreword
- Contributors
- Table of Contents
- Preface
- Part 1 - Getting Started
- Chapter 1: The Impact of Data Quality on Organizations
- The value of this book
- Importance of executive support
- Detailed definition of bad data
- Bad data versus perfect data
- Impact of bad data quality
- Quantification of the impact of bad data
- Impacts of bad data in depth
- Process and efficiency impacts
- Reporting and analytics impacts
- Compliance impacts
- Data differentiation impacts
- Causes of bad data
- Lack of a data culture
- Prioritizing process speed over data governance
- Mergers and acquisitions
- Summary
- References
- Chapter 2: The Principles of Data Quality
- Data quality in the wider context of data governance
- Data governance as a discipline
- Data governance tools and MDM
- How data quality fits into data governance and MDM
- Generally accepted principles and terminology of data quality
- The basic terms of data quality defined
- Data quality dimensions
- Stakeholders in data quality initiatives
- Different stakeholder types and their roles
- The data quality improvement cycle
- Business case
- Data discovery
- Rule development
- Monitoring
- Remediation
- Embedding into BAU
- Summary
- References
- Chapter 3: The Business Case for Data Quality
- Activities, components, and costs
- Activities in a data quality initiative
- Early phases
- Planning and business case phase
- Developing quantitative benefit estimates
- Example - the difficulty of calculating quantitative benefits
- Strategies for quantification
- Developing qualitative benefits
- Surveys and focus groups
- Outlining data quality qualitative risks in depth
- Anticipating leadership challenges
- The "Excel will do the job" challenge.
- Ownership of ongoing costs challenge
- The excessive cost challenge
- The "Why do we need a data quality tool?" challenge
- Summary
- Chapter 4: Getting Started with a Data Quality Initiative
- The first few weeks after budget approval
- Key activities in those early weeks
- Understanding data quality workstreams
- Workstreams required early on
- Identifying the right people for your team
- Mapping resources to the workstreams
- Summary
- Part 2 - Understanding and Monitoring the Data That Matters
- Chapter 5: Data Discovery
- An overview of the data discovery process
- Understanding business strategy, objectives, and challenges
- Approaches to stakeholder identification
- Content of stakeholder conversations
- The hierarchy of strategy, objectives, processes, analytics, and data
- Prioritizing using strategy
- Linking challenges to processes, data, and reporting
- Basics of data profiling
- Typical tool data profiling capabilities
- Using these capabilities
- Connecting to data
- Summary
- Chapter 6: Data Quality Rules
- An introduction to data quality rules
- Rule scope
- The key features of data quality rules
- Rule weightings
- Rule dimensions
- Rule priorities
- Rule thresholds
- Cost per failure
- Implementing data quality rules
- Designing rules
- Building data quality rules
- Testing data quality rules
- Summary
- Chapter 7: Monitoring Data Against Rules
- Introduction to data quality reporting
- Different levels of reporting
- Data security considerations
- Designing a high-level data quality dashboard
- Dimensions and filters
- Designing a Rule Results Report
- Typical features of the Rule Results Report
- Designing Failed Data Reports
- Typical features of the Failed Data Reports
- Re-using Failed Data Reports
- Multiple Failed Data Reports
- Exporting Failed Data Reports.
- Managing inactive and duplicate data
- Managing inactive data
- Managing duplicate data
- Detecting duplicates
- Presenting findings to stakeholders
- Launching data quality reporting successfully
- Embedding reports into governance
- Summary
- Part 3 - Improving Data Quality for the Long Term
- Chapter 8: Data Quality Remediation
- Overall remediation process
- Prioritizing remediation activities
- Revisiting benefits
- Approach to determining priorities
- Identifying the approach to remediation
- Typical remediation approaches
- Matching issues to the correct approach
- Moving remediation to business as usual
- Understanding the effort and cost
- Types of cost in remediation
- Governing remediation activities
- Key governance activities
- Tracking benefits
- Quantitative example
- Qualitative benefit tracking
- Summary
- Chapter 9: Embedding Data Quality in Organizations
- Preventing issue re-occurrence
- Methods to prevent re-occurrence
- The ongoing impact of human error
- Short-horizon reporting
- Ongoing data quality rule improvement
- Strategies to identify rule changes
- Updating data quality rules
- Transitioning to day-to-day remediation
- Requirements for success
- Planning for a successful transition
- Indications that the transition has been successful
- Continuing the data quality journey
- Roadmap of data quality initiatives
- Identifying the next initiative
- Obtaining support
- What if no further initiative is sanctioned?
- Summary
- Chapter 10: Best Practices and Common Mistakes
- Best practices
- Selecting the best practices
- Manage data quality primarily at the source
- Implementing supporting governance meetings
- Including data quality in an organization-wide education program
- Leveraging the data steward and producer relationship
- Best practices throughout this book.
- Common mistakes
- Failure to implement best practices
- A lack of practicality
- Technically driven data quality rules
- One-off remediation activity
- Restricting access to data quality results
- Avoid silos in data quality work
- The future of data quality work
- LLMs
- Greater emphasis on high-quality data in organizations
- Summary
- Index
- About Packt
- Other Books You May Enjoy.
