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#SASanalytics: Twitter chat recap on high performance analytics – Sponsored by SAS Canada

Candice So
Image of blue line graphs, charts

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Big data, high performance analytics, data visualizations – do these sound like they have any impact on your organization? We just concluded a great Twitter chat sponsored by SAS Canada, speaking to solutions specialist Tim Trussell on how predictive analytics can benefit businesses, both small and large. Writer and tech analyst Carmi Levy moderated the chat.

See how SAS High Performance Analytics products have helped others:

In case you missed the chat, here’s a quick recap of what we covered:

Q1. How exactly do we define high performance analytics?

High performance analytics isn’t something we hear about often, but Trussell answered it’s about using data to answer questions that are pivotal to the future of a business.

He also added that while big data and high performance analytics are often paired together, they’re not interchangeable – you don’t absolutely need big data to leverage high performance analytics.

 

Q2. SAS has been around since 1976. How much has data analytics changed since then?

 

 

Q3. Big data and analytics is still a new field. What are some misconceptions around it?

The chat took several different paths, as people had questions of their own surrounding big data and high performance analytics:

 

 

 

 

Q4. What is the difference between structured and unstructured data? How can businesses use them?

 

 

Q5. Charts can be very boring ways of showing info. What sets data visualizations apart from them?

Most people would agree data visualizations look good. But to look good and also be effective, they need to represent their data efficiently, Trussell said.

Q6. What are some common use cases for tech businesses harnessing analytics?

 

 

 

Q7. Why should businesses invest in big data and analytics?

The main message behind this one was that if businesses don’t invest in big data, their competitors will leave them behind. Some Twitter chat participants pointed out with big data, businesses can invest in all kinds of practical tools. For example, some marketers use big data to improve targeting, email messaging with opt in and opt out mechanics, and automated messaging, based on data showing what interests specific customers.

 

 

 

Q8. How does Hadoop impact big data and analytics?

As Levy put it, this question was for the geekier participants in the chat. The Hadoop platform is a handy one for users who have a lot of data that doesn’t really fit into tables and charts, especially if that data is a mix of structured and unstructured data. It can also be helpful for users who want to leverage data in answering very complex, specific questions.

 

 

 

Q9. Many small businesses seem to think big data is for big companies. Can it apply to them as well?

This question seemed to provoke a lot of discussion throughout the chat, as many small businesses seem to avoid working with big data since they’re already strapped for resources and time. And going off of that, someone also asked whether big data and high performance analytics can apply to startups.

However, most chat participants seem to feel there’s a place at the big data table for small business.

 

 

 

 

 

 

 

 

Q10. Where do we see big data and analytics heading in the next year or two?

 

 

 

 

 

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