Cox How Libraries Should Manage Data

Practical Guidance On How With Minimum Resources to Get the Best From Your Data
1. Auflage 2015
ISBN: 978-0-08-100671-9
Verlag: Elsevier Reference Monographs
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 150 Seiten, Format (B × H): 152 mm x 229 mm

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

Autoren/Hrsg.

Cox, Brian

Fachgebiete

• Interdisziplinäres Bibliothekswesen, Informationswissenschaften Systematiken, Katalogisierung, Klassifikation, Bibliographisches Arbeiten
• Interdisziplinäres Bibliothekswesen, Informationswissenschaften Archivierung, Konservierung & Digitalisierung

Inhaltsverzeichnis

- 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

Leseproben

2 Lifting the fog
Many libraries struggle to obtain real value from the data they have, and this can result in a vicious cycle where they collect more data in the hope that the additional data will yield previously unobtainable insights. This chapter shows how to break out of this cycle by recognizing the emotions that underpin the decisions to continue to collect useless data, and advocating for an objective and simple set of tests to determine whether you should keep, change, or stop collecting specific datasets. This chapter outlines the importance of managing this change as a project, outlining key issues, considerations, roles and tasks. Keywords
Decision making; emotional decisions; useless data; project management; objective tests; data selection criteria Imagine your house is in shambles, clothes piled up in random containers tucked away in dark corners, shoe boxes collecting dust balancing precariously on the top of wardrobes, and you are sick of the state of mess. There is a sensible way to go about cleaning, and an irrational way. It is quite possible that the reason you have a mess is because you have more than you need. That dress may have looked great on you in your early twenties, but it is never going to fit again. And those pair of shoes you wore to your first job have gone out of style along with other relics that should stay in the past, like mullet haircuts. So, if you are serious about cleaning, this means letting go of some things. Easy said, not so easy to do. The same applies to data. You might have some wonderful time series data that makes a pretty chart, or you might have some stats that staff have been collecting since the Stone Age, or you might have some statistics to which staff feel emotionally attached. Just because you collected it in the past does not mean you should have ever collected it, or even if it once was a legitimate collection from a business perspective, it does not mean that it is now. Just like the messy house, a bloated collection of irrelevant data, is counterproductive. At the very best irrelevance distracts from the data that is useful. At worst, the good data gets tainted by the bad data, with staff becoming cynical or disconnected with data. If the numerical literacy at your workplace is low, then chances are this will provide comfortable validation for those staff that want nothing to do with numbers. When you are cleaning your house, the last thing you should do is rush off and buy more storage, and perhaps buy more clothes and shoes. This would only make the mess worse. The same applies to data. If you are not happy with the state of affairs with your data, don't rush off and create new spreadsheets, sign up to new data vendors, or collect more data. Useful things become useless if they are hidden in a sea of rubbish. Indeed, this is meant to be one of the key value propositions of the library - they are a gateway to quality resources. Unfortunately, many professions don't practice what they preach. However, if you are worried about the long term viability of your business model, then you will need good data; and to get good data you need to be disciplined and focused. What is the first sensible thing to do when cleaning your house? You decide on criteria for determining whether to keep something or not, then assess whether the things you have meet those criteria. You would at the very least have three piles, one pile for stuff to keep, one to give away, another to chuck. Your criteria might be simple - it might be I will keep it if it fits me, and I will allow myself to keep five items for sentimental value. When you are cleaning your data it is essential that you determine the criteria before you start. Cleaning data can be an emotional exercise, and if you don't determine the criteria first, chances are you will inadvertently allow emotion to make the decisions. Of course, emotions

Über Autor(innen)

Cox, Brian
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.