How Libraries Should Manage Data

Have you ever looked at your Library’s key performance indicators and said to yourself "so what!"? Have you found yourself making decisions in a void due to the lack of useful and easily accessible operational data? Have you ever worried that you are being left behind with the emergence of data analytics? Do you feel there are important stories in your operational data that need to be told, but you have no idea how to find these stories? If you answered yes to any of these questions, then this book is for you. How Libraries Should Manage Data provides detailed instructions on how to transform your operational data from a fog of disconnected, unreliable, and inaccessible information - into an exemplar of best practice data management. Like the human brain, most people are only using a very small fraction of the true potential of Excel. Learn how to tap into a greater proportion of Excel’s hidden power, and in the process transform your operational data into actionable business intelligence.

• Recognize and overcome the social barriers to creating useful operational data
• Understand the potential value and pitfalls of operational data
• Learn how to structure your data to obtain useful information quickly and easily
• Create your own desktop library cube with step-by-step instructions, including DAX formulas

This book is aimed at any Library staff responsible for managing or interpreting data. Because it is also meant to be a broad road map on how to use data effectively, this book is also aimed at Executive Library Managers and may also be useful for students undertaking information and library studies.

• Dedication
• About the author
• 1. Introduction
• 2. Lifting the fog
  • First steps – project management
• 3. Step away from the spreadsheet – common errors in using spreadsheets, and their ramifications
  • The ten table commandments
• 4. Starting from scratch
  • How low do you go?
  • Measuring loans and accounting for variation
  • Visits and how to organize the data into columns
  • Browsed items and avoiding false conclusions
• 5. Getting the most out of your raw data
  • Keep it simple stupid!
  • Make it easy stupid! Absolute and relative formulas
  • Formulas you must know
  • Typical error messages and what they mean
  • Managing error messages
• 6. Stop, police!
  • Protecting data
  • Data validation
  • Using tables
  • Using a table to populate a validation list
  • Dependent lookups
• 7. Pivot magic
  • How to create a pivot table
  • Anatomy of a pivot table
  • Bringing it all together
• 8. Moving beyond basic pivots
  • Relational databases
  • PowerPivot
  • How to use PowerPivot
  • Creating a PowerPivot PivotTable
  • The difference between a measure and a calculated column
  • Adding a measures
• 9. How to create your own desktop library cube
  • Making the “desktop cube”
  • Sourcing the datasets
  • Using MS Access to create a merged dataset
  • Linking PowerPivot to the merged dataset
  • Adding a few more tables
  • Adding calculated columns to PowerPivot
  • Creating relationships
  • Writing measures
  • Some suggested views
• 10. Beyond the ordinary
• Index