Why this is in my collection

From the publisher:

Organizations are waking up to the reality that in order to become more data-driven, they need to attract and develop highly data literate team members. But what does it mean to be highly data literate? Find out the types of knowledge and skills that these sought-after contributors possess, as well as the attitudes and behaviors that they demonstrate on a regular basis. This booklet includes a checklist for taking stock of your own strengths and opportunities for development, and exclusive quotes from thought-leaders and top authors in the data world.

Highlights

  • Data literacy is a language, not a toolset — Jones frames fluency as vocabulary, grammar, and reading/writing skills rather than software proficiency; the same claim underpins concept-libraries: shared, precise terms are the substrate everything else compiles against.
  • Traits split into knowledge, skills, and attitudes — the checklist treats mindset (curiosity, skepticism, humility) as equal to technique; a reminder that governance programs which only train tools miss two thirds of what makes people fluent.
  • Reading data critically comes before producing it — the ability to interrogate a chart or a claim precedes the ability to build one; in a rules-first practice this maps to validating inputs before automating outputs.
  • Fluency is assessable, trait by trait — turning a fuzzy competence into an explicit, checkable list is itself a structural move: once traits are named, gaps become visible and improvable, exactly like data-quality checks made explicit.
  • Statistical intuition guards against confident nonsense — understanding variation, uncertainty, and context stops teams from treating any number in a dashboard as truth; the human-side complement to automated plausibility rules.
  • Organizations become data-driven through people, not platforms — the book's premise that literate team members, not new stacks, unlock value parallels the observation that platforms churn while capabilities (models, vocabulary, judgment) endure.

Highlights on this page are generated with the help of AI.