11 The magic of well-structured data

 

This chapter covers

  • Tools and strategies for storing and processing data
  • Approaches to data management at organizations
  • Methods of structuring data for analytics
  • Developing a strategy for enhancing the value of your data

Think about the data that you’ve analyzed throughout your education, career, and reading this book—what was the structure of that information? What was contained in each row and column? How much work did it take to transform the data into the shape needed to analyze it? What information was accurate, and what was potentially wrong or missing?

While your answers will vary depending on the data and context, the structure of your data shapes what you can do with it. Well-structured data makes it easy to focus on insights, and poorly structured data takes countless hours of cleaning and reorganization before meaningful work can begin. In most organizations, you aren’t bound to the format of the data available, and you can strategically contribute to the data collection, storage, and transformation of information made available for analysis. This chapter will explore the tools and methods that enable you to streamline the generation of valuable insights in your work.

11.1 The analyst’s dilemma

Consider the following scenario:

11.2 Data tools at a glance

11.2.1 Data gathering

11.2.2 Data storage

11.2.3 Data processing

11.2.4 Exercises

11.3 Data management practices

11.3.1 Data structure

11.3.2 Data quality

11.3.3 Using metadata

11.3.4 Exercises

11.4 Data for analytics

11.4.1 Analytics engineering

11.4.2 Modeling data in star schemas

11.4.3 Exercises

Summary