Here’s a question: Why is data science such a big deal these days?
Big Data, obviously, but it’s not just that, contends veteran IT analyst Robin Bloor.
In a follow up to his rant about the term “data scientist,” Bloor actually promotes the importance of data science as a practice, if not the terminology.
He sees 10 reasons data science is gathering so much momentum. While all relate back to Big Data, it’s important to realize that Big Data didn’t just spring from the CIO’s head, fully clothed, like some sort of silicon Athena.
Four Steps to a Big Data Strategy
No, there are significant technology trends that support the growth of Big Data, including:
- The open source movement
- Hadoop. His reasoning isn’t as obvious as you’d think. “The primary contribution of a Hadoop is that it has provided a scale-out data platform that is schema-free,” Bloor writes. “Because of Hadoop there is no need to do immediate data modeling work if you want to collect data and both cleanse and transform it for use on data science projects.”
- R language, aka the langue of data analysts. With 5 million users, R “has become a kind of usage-driven standard which in itself encourages analytical activity, modeling and prototyping,” he writes.
- Parallelism and faster hardware
- Machine learning algorithms and machine-generated data sharing
- The realization that Big Data can be a way to grow revenue or reduce costs, including risks
Check out the full list. It’s a good read and I think speaks to how pervasive Big Data technologies already have become.