What’s the key to successful data science? While it’s true that so much of it stems from the various people in roles up and down the workflow—the IT staff that put together the infrastructure that collects the data; the data scientists who build the models; and the business users who use the models to derive insights the truth is that data science involves massive amounts of data. On a micro scale, each person is an equal link in a very long chain that goes from data collection to final insights.
In the real world, the sheer volume of big data means that it’s nearly impossible to manually process everything in an efficient, time-sensitive way. Enter machine learning, the key to making this possible. Machine learning is not just a tool used by data scientists for modeling; in fact, it is much more critical for the entire data journey.