Everyone around SAS seems to be buzzing about big data and high-performance analytics (HPA). As we're preparing for conferences, product launches and customer engagements, it's the main idea on almost everyone's mind.

Along with that excitement and buzz, however, it's worthwhile to step back and look at how we got here, and to recognize what hasn't changed. Two SAS leaders, CTO Keith Collins and CMO Jim Davis have recently published blog posts that step away from the hype to talk about the benefits of high-performance analytics and look at what hasn't changed.

In his post, Collins reminds us that the industry has always dealt with large data volumes:

Speaking from the perspective of somebody who’s been in this industry for many years, big data is not a new issue. We’ve been through this. We called it the Enterprise Data Warehouse (EDW) before, and everybody debated on how big their data was. It’s not about how big it is. It’s about what you’re going to do with it. That’s why the exciting thing is big analytics – because it’s the analytics that help you do something with all of that data.

Likewise, Davis talks about high-performance as an enabler and says big data alone is not the answer:

A lot of big data proponents are promising things bigger, better and faster. But if the information you’re getting is backward looking, it’s still going to be looking at the past when you get it in a shorter timeframe. You’re still only understanding the past faster than before. No matter how fast you go with summary statistics, you’re never going to get to the future.

Both Collins and Davis recognize the value in high-performance analytics as something absolutely transformational, though. Collins says:

Banks are changing how they look at risk in risk portfolios, which allows them not just to understand risk at the end of the day but at the point where the transaction is occurring. Likewise, understanding fraud in the public sector is becoming easier. Or in the healthcare industry, we can actually do text mining across emergency medical service logs and start to identify disease outbreaks weeks earlier than you could by looking at hospital records. These are opportunities that we have now with this type of processing power.

And Davis also points to the predictive capabilities:

When you use high performance analytics to predict things like risk, customer satisfaction or marketing optimization, you’re getting your predictions sooner than before, and you can react more quickly. When you’re computing forward-looking results, the speed really can make a difference.

How do other industry veterans view the big data trend? Where is the hype and where is the value?

This is day ten of my "HPA once a day" blog post series. To read more, see all of the high-performance analytics posts on this blog or follow the high-performance analytics rss feed.