We are increasingly seeing that bigger data is often not better. Companies are realizing that extracting more data may not help them address certain problems more effectively.
While more data can be useful if it is clean, the vast majority of business use-cases experience diminishing marginal returns. More data can actually slow innovation, making it harder for data scientists to iterate quickly as testing takes longer and requires more infrastructure.
We’ve recently seen data science shift markedly from a peripheral capability to a core function, with larger teams tackling increasingly complex analytics problems. We’ve watched rapid advances in data science platforms and their big implications for data and analytics teams. But what surprises are in store in the realm of data, analytics and machine learning going forward?