Data Clean-Up and Management: A Practical Guide for Librarians

ISBN-13: 9781843346722 / Angielski / Miękka / 2012 / 578 str.

Margaret Hogarth; Kenneth Furuta

Opis

Data use in the library has specific characteristics and common problems. Data Clean-up and Management addresses these, and provides methods to clean up frequently-occurring data problems using readily-available applications. The authors highlight the importance and methods of data analysis and presentation, and offer guidelines and recommendations for a data quality policy. The book gives step-by-step how-to directions for common dirty data issues.

• Focused towards libraries and practicing librarians
• Deals with practical, real-life issues and addresses common problems that all libraries face
• Offers cradle-to-grave treatment for preparing and using data, including download, clean-up, management, analysis and presentation

Recenzje

"This excellent book is highly recommended; it will be keenly sought out and become a well-used tool. Whilst a key text for students of this subject, it is also particularly suited for staff using Excel, Access and other applications to improve the quality of the information available for decision-making."--The Australian Library Journal, Vol. 63, No. 1, 2014

Spis treści

• List of figures
• List of tables
• About the authors
• Chapter 1: Introduction (why this book is needed)
• Abstract:
• What makes this book unique?
• Why library data is important
• The book's outline
• Chapter 2: Commonalities
• Abstract:
• Microsoft Office Excel
• MarcEdit
• Microsoft Access
• XML
• Commonalities
• Capture and use
• Standardization
• Data import issues
• Technical skills
• Project management challenges
• Chapter 3: Defining data
• Abstract:
• Rule 1: define data points
• Rule 2: apply data point definitions
• Rule 3: count the right apples
• Rule 4: avoid capturing redundant data
• Chapter 4: Types of data issues
• Abstract:
• Microsoft Excel vs Microsoft Access
• General data-handling edicts
• Data issues: importing data
• Chapter 5: Microsoft Excel techniques
• Abstract:
• Creating datasheets
• Selecting cells
• Copying
• Sorting
• Filter
• AutoSum
• Sum
• Fill
• Chapter 6: Data clean-up in Excel
• Abstract:
• Common dirty data scenarios
• The usefulness of delimiting
• System limitations
• Removing extra characters
• Chapter 7: Excel: combining data
• Abstract:
• IF statements
• The TEXT function
• PivotTables and filtering
• VLOOKUP
• HLOOKUP
• MATCH
• Chapter 8: Additional tools
• Abstract:
• PDFs
• Notepad
• Microsoft Word
• Global update in an integrated library system
• Regular expressions
• Excel
• Access
• Macros
• XML
• MarcEdit
• The MARC tools window
• Chapter 9: Access techniques
• Abstract:
• What is a database?
• Access
• Planning a database
• Preparing data for a database
• Adding a table to a database
• Chapter 10: Access forms
• Abstract:
• Types of form
• Parts to a form
• Form controls
• Validating data
• Option buttons
• Combo boxes
• For a Spin Button:
• Tab control techniques
• Multiple-table forms
• Chapter 11: Access reports
• Abstract:
• Creating a report using the Report Wizard
• Controls
• Making additions to a report
• AutoFormat a report
• Working with report properties
• Inserting a control into a report
• Conditional formatting
• Sizing reports
• Moving controls in Access
• Publishing reports
• Sorting and grouping options
• Adding calculations to reports
• Launching reports
• Creating a subreport
• Chapter 12: Access queries
• Abstract:
• Sorting in Access
• Filtering in Access
• Queries
• Entering data
• Query properties
• Access relationships
• Chapter 13: Data clean-up in Access
• Abstract:
• Prevention is the best cure
• Extra characters
• Access data upload errors
• ISSN issues
• Chapter 14: Access -- combining data
• Abstract:
• Combining data from one or moredata sources
• Query with a sum
• Types of operators
• Totals queries
• Parameter queries
• Action queries
• Update queries
• Delete queries
• Make-Table queries
• Append queries
• PivotTable queries
• SQL in Access
• Parameter Queries in SQL
• Export data to Excel
• Finding unique values in a dataset
• Matching on ISSN
• Chapter 15: Strategies for missing data
• Abstract:
• Resources are missing ISBNs
• Resources are missing ISSNs
• Richard Jackson's OCLC look-up strategy
• Chapter 16: Qualitative data
• Abstract:
• The definition of qualitative data
• Qualitative data is valuable
• Types of qualitative data
• Qualitative data techniques
• SWOT analysis
• Tools
• The whole picture
• Chapter 17: ROI
• Abstract:
• Chapter 18: Data collection and analysis
• Abstract:
• What data do you need to answer the question?
• Does the data measure what you need to measure?
• Analysing data
• Data presentation
• Charts
• Stacked charts
• Chapter 19: Data quality policy
• Abstract:
• Poor data quality
• Data as an asset and a product
• Apply quality principles
• Process design
• Framework for a data quality policy
• Chapter 20: Next steps
• Abstract:
• Appendix 1: Excel techniques
• Appendix 2: Excel functions
• Appendix 3: Access quick keys
• Appendix 4: Redman's model data policy
• Bibliography and references
• Index