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Take the sting out of data: Addressing the 6 points of data pain

Digital data holds an incredible amount of value in today’s world and without it, data science will suffer. Unfortunately, adding digital data to existing or new data science initiatives is often a massive struggle. Whether the data is an input for attribution, scoring models, machine learning, or otherwise, the challenges tend to remain the same.

Data modelling, outputs, inaccuracy, and individual-level data top the list of challenges faced by most data scientists. 

This guide explores the most common points of data pain, and how to solve them.

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What's inside

  1. The 6 most common data pain points
  2. How to address these data pains
  3. The future of data science