Data Literacy and the Modern Marketing Stack
The productive tension
Big dataandthick data
volume without meaning is expensive noise
The synthesis
The false dichotomy is "big data will solve everything" versus "data is dehumanising — trust your instincts." The big data evangelists believe that volume, velocity, and variety will eventually produce understanding automatically. The humanists believe that reducing people to data points strips away the context that makes behaviour intelligible. Both are wrong in isolation. Big data — large-scale, quantitative, behavioural — tells you what people did, but not why. Thick data — small-scale, qualitative, contextual — tells you why people do things, but not how many. The evidence-based answer: use big data for pattern detection at scale AND thick data for meaning-making in depth. Volume without meaning is expensive noise. Meaning without volume is anecdote. The modern marketer needs both — and needs the literacy to know which one to reach for.
Learning objectives
- →Map the modern marketing data stack and identify what each tool measures and misses
- →Distinguish between behavioural data and attitudinal data and explain why both are necessary
- →Evaluate digital attribution models and articulate their fundamental limitations
- →Explain the privacy revolution (GDPR, iOS14, cookie deprecation) and its strategic implications
- →Apply the big data / thick data framework to determine the appropriate data approach for a given marketing question
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