Thinking with Data
About this book
A short book about the work that happens before any analysis, namely turning a vague request into a question that data can answer. Shron proposes a scoping framework covering the context you are working in, the need behind the request, the vision of what an answer looks like, and the outcome once somebody acts on it. He then covers arguments and evidence, borrowing from informal logic to show that an analysis is a chain of claims which can be attacked at any link. There are chapters on recurring patterns of analytical work, such as building a dimensional model or comparing against a baseline, and on closing a project cleanly. Read it before writing the query rather than after presenting the results.
Description via Product Digest.
Topics
Related concepts
- Over reliance and automation biasWhy people accept output they should have checked, why it gets worse as the system gets better, and what a product can do about it.
- Events and propertiesThe event model a product analytics practice is built on, the three kinds of property and why the difference between them decides what a team can ask later, an object action naming convention, and the choice between many specific events and few general ones carrying properties.
- Data for generative AIWhat makes data usable by a model, the difference between structured and unstructured data and between labelled and unlabelled data, and why accessibility decides more projects than volume does.
- Communicating analytics findingsWhat turns an analysis into a finding somebody who was absent can act on, the four parts of a written result, why a chart with no claim attached settles nothing and what belongs in the record after the decision is made.