The Art of Data Usability
Tryggvi Björgvinsson
  • MEAP began June 2017
  • Publication in Early 2018 (estimated)
  • ISBN 9781617294716
  • 300 pages (estimated)
  • printed in black & white

You're planning a large event in Stockholm. It's important for you to know who is coming and when, but you don't need complete data about them-family history, employment status and property ownership is probably irrelevant. You need different things out of data for different situations. Crop forecasts are measured in days or weeks, while train schedules must be accurate within minutes or seconds. Knowing how data will be consumed and planning data projects accordingly is the essential art of data usability. Measures of data quality, such as precision, timeliness, format, and so on all depend on how those data are to be used.

Table of Contents detailed table of contents

1. Learning from the past

1.1. Base future improvements on past insights

1.2. The path to wisdom

1.2.1. Data

1.2.2. Information

1.2.3. Knowledge

1.2.4. Wisdom

1.2.5. DIKW hierarchy

1.3. A cozy and murderous project

1.3.1. Designing for different situations

1.3.2. Setting up the infrastructure

1.3.3. Collecting and processing data

1.3.4. Announcing the results

1.3.5. Wrapping things up

1.4. Summary

2. Creating the perfect world

2.1. What exactly is quality?

2.1.1. Imitating Santa Claus

2.2. Managing quality

2.2.1. Planning and designing metrics

2.2.2. Implementing controls and changes

2.2.3. Analyzing the implementation

2.2.4. Establishing a new baseline

2.2.5. Document everything

2.2.6. The difference of data quality

2.3. Summary

3. Designing a data project

4. Managing data

5. Data collection

6. Processing data

7. Dissemination of data

8. Closing a data project

Appendixes

Appendix A: Installing and using Python

A.1. Installation

A.1.1. GNU/Linux

A.1.2. Mac OS X

A.1.3. Microsoft Windows

A.2. Virtual environments

A.3. Create your first Python program

About the book

The Art of Data Usability teaches you how to set up data projects in a way that maximizes their effectiveness for their intended users. In this interesting, practical guide, you�ll master techniques for understanding the type and role of the data you have and preparing those data to maximize their value. With the help of dozens of real world examples that the author has faced before, you'll learn from them to piece together the perfect workflow for sorting through data for your clients, principles for establishing data projects, and techniques to apply to your datasets. Throw in a bit of coding, and you'll be more than prepared to get the data you have in the right shape, exactly when you need it.

What's inside

  • The attributes of quality data
  • Identifying user needs and requirements
  • Using Python for data quality monitoring
  • The correct way to disseminate your data
  • Best practices and methods to improve data usability

About the reader

Written for readers comfortable with data management and common data formats such as CSV and JSON. Some examples require novice-level programming skills.

About the author

Tryggvi Björgvinsson is the head of IT and dissemination at Statistics Iceland. He holds a Ph.D. in software engineering.


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