Data science relies on exceptional data - not just any data.
Without digital data, data science suffers.
But 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 are 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.
