the challenges of data wrangling
summary
Common problems are concrete: giving the right people access, solid data pipeline gaps that force analysts to hunt or clean data, and data quality not being up to date. Teams choose between building pipelines in-house or delegating to services like Treasure Data so analysts stop wrangling and start modeling. A frequent surprise is the laptop-to-production gap — prototypes fail at scale, so the priority becomes to operationalize models. A customer case is Wish.com, which scaled predictive analytics and A/B testing over about 20 months.
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