Are you still struggling to understand what "big data" means to you or your industry? Or maybe you get it, but you want your boss to understand the opportunities, and you're not sure how to explain it clearly.
Look no further. I've categorized some of our most popular recent big data posts below into specific industries and interests. Pick what's relevant to you and share the rest with your friends and co-workers who might benefit from the advice for their roles.
For banking
- Assess risk in seconds, not days, with high-performance analytics
- Could high-performance analytics have prevented the housing crisis?
For retail
- Retail exec explains how big data will flatten the world
- Q&A: How big data will revolutionize retail
- Six benefits that big data can bring to retailers
- Optimizing assortments with big data and high-performance analytics
For health care
For Telco
- Making the right call with late-paying telco customers
- High-performance analytics for big customer data
For CIOs and IT leaders
- 12 questions to ask your high-performance vendor
- SAS Hadoop - A peek at the technology
- Introducing high-performance analytics for any environment
For analysts
For executives
- Why would you want to visualize 5 billion rows of data?
- What's the story with high-performance analytics?
For marketers
For SAS fans
- Staying ahead of the big data curve
- Jim Goodnight on high-performance analytics
- Big data is nothing new, but high-performance analytics is
For everyone
- Before & after: high-performance analytics
- How can you use SAS high-performance analytics?
- 8 high-performance metaphors you should know
If you've made it to the end of this list and don't see anything that's relevant for you, let us know. What big data questions do you have for your industry or your organization? We'll help find the answers.
You can read more about "big data" in this special 32-page report on high-performance analytics.
1 Comment
Understanding how to analyze your data can be the make or break for your organization. Optimization, efficiency tasks and good analytics are always important when dissecting data.