Machine learning’s potential to aid an analyst is undeniable, but it’s critical to recognize that it should be embraced when there are clearly defined outcomes. “Machine learning is not great when your data is subjective,” says Andrew Vigneault, Staﬀ Product Manager with Tableau. For example, when conducting a survey to customers about product satisfaction, ML cannot always pick up on qualitative words.
Additionally, the analyst needs to understand success metrics for the data to make sense of it in a way that is actionable. In other words, inputs into a machine don’t make the outputs meaningful. Only a human can understand if the right amount of context has been applied—which means that machine learning cannot be done in isolation (without an understanding of the model and what inputs/outputs are being made)
While there might be concern over being replaced, machine learning will actually supercharge analysts and make them more efficient, more precise, and more impactful to the business. Instead of fearing machine learning technology, embrace the opportunities it presents.