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.
Sprache
Verlagsort
Verlagsgruppe
Elsevier Science & Technology
Zielgruppe
Maße
Höhe: 229 mm
Breite: 152 mm
Gewicht
ISBN-13
978-0-08-100663-4 (9780081006634)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
Brian Cox has been responsible for a number of activities within an academic library, ranging from managing research data collection, to facilitating strategic planning. During that time Brian developed a deep understanding of how libraries use data, and where they could improve. His work in this area culminated in the creation of the Library Cube, a breakthrough in measuring value that propelled the University of Wollongong Library into the international spotlight within the Library sector.
Autor*in
Innovator, academic libraries
Chapter 1 - Introduction
Chapter 2 - Lifting the fog
Chapter 3 - Step away from the spreadsheet - common errors in using spreadsheets, and their ramifications
Chapter 4 - Starting from scratch
Chapter 5 - Getting the most out of your raw data
Chapter 6 - Stop, police!
Chapter 7 - Pivot magic
Chapter 8 - Moving beyond basic pivots
Chapter 9 - How to create your own desktop library cube
Chapter 10 - Beyond the ordinary