#theCUBE #IBM #Spark #SparkSummit #SiliconANGLE
01. Robert Parkin, IBM Commerce, Visits theCUBE . (00:21)
02. Adding Predictive and Optimization Analytics. (00:50)
03. Spark Is Designed for Real-Time Analytics. (01:28)
04. Creating Predictive Models for Transactional Data. (02:19)
05. Shortening the Predictive and Performance Life Cycles. (04:37)
06. Competitive Advantage of Early Adoption. (05:50)
07. Personalizing the Marketing Experience. (08:26)
08. Spreading the Data Across Different Structures. (09:54)
09. Actionable Data Still Requires Historical Data. (12:03)
10. Real-Time Is Relative to the Context of the Decisions. (13:54)
11. New Product Development. (14:24)
--- ---
IBM Commerce sees major performance increases with Spark | #sparkinsight
by Heather Johnson | Jun 17, 2015
IBM Commerce goes under the covers of marketing, merchandising and B2B commerce, and it adds analytics to improve user interaction and the decisions that get made inside those applications. IBM Commerce’s principal scientist Robert Parkin told theCUBE during IBM Spark 2015 that Spark allows the department to cut down run times for the mathematical modeling that takes place. Real time makes all the difference.
The importance of real-time information
“In the past, we’ve seen a lot of work with batch jobs, particularly in the Hadoop infrastructure, which is great if you’re running over very large data sets and you can wait 20 minutes to three hours for something to come back,” Parkin said. “But real-time information is becoming more important, and that’s what Spark is designed to do. It’s doing for memory what Hadoop did for disk. It allows you to have access to the large size of analytics, but in a more real-time fashion.”
In merchandising, for example, Parkin has noticed a 30 to 40 percent increase in performance.
“We get data in from the customer and create predictive models from that data. Customers use this for promotional activities and to find out what people will do at different price points,” he said.
“We put information in the applications and allow the retailer to create goals for each category of product. They can then choose the right set of prices and the right marketing and merchandising based on that goal and what the consumers want.”
IBM’s Journey Analytics
Parkin is especially excited about IBM’s new Journey Analytics, developed with Spark, which helps “retailers and brands connect in a more effective way across different channels — mobile and web — using real-time data.”
Watch the full interview below, and be sure to check out more of SiliconANGLE and theCUBE’s coverage of IBM Spark 2015.
@theCUBE
#SparkInsight
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Robert Parkin, IBM | IBM Spark Summit 2015
#theCUBE #IBM #Spark #SparkSummit #SiliconANGLE
01. Robert Parkin, IBM Commerce, Visits theCUBE . (00:21)
02. Adding Predictive and Optimization Analytics. (00:50)
03. Spark Is Designed for Real-Time Analytics. (01:28)
04. Creating Predictive Models for Transactional Data. (02:19)
05. Shortening the Predictive and Performance Life Cycles. (04:37)
06. Competitive Advantage of Early Adoption. (05:50)
07. Personalizing the Marketing Experience. (08:26)
08. Spreading the Data Across Different Structures. (09:54)
09. Actionable Data Still Requires Historical Data. (12:03)
10. Real-Time Is Relative to the Context of the Decisions. (13:54)
11. New Product Development. (14:24)
--- ---
IBM Commerce sees major performance increases with Spark | #sparkinsight
by Heather Johnson | Jun 17, 2015
IBM Commerce goes under the covers of marketing, merchandising and B2B commerce, and it adds analytics to improve user interaction and the decisions that get made inside those applications. IBM Commerce’s principal scientist Robert Parkin told theCUBE during IBM Spark 2015 that Spark allows the department to cut down run times for the mathematical modeling that takes place. Real time makes all the difference.
The importance of real-time information
“In the past, we’ve seen a lot of work with batch jobs, particularly in the Hadoop infrastructure, which is great if you’re running over very large data sets and you can wait 20 minutes to three hours for something to come back,” Parkin said. “But real-time information is becoming more important, and that’s what Spark is designed to do. It’s doing for memory what Hadoop did for disk. It allows you to have access to the large size of analytics, but in a more real-time fashion.”
In merchandising, for example, Parkin has noticed a 30 to 40 percent increase in performance.
“We get data in from the customer and create predictive models from that data. Customers use this for promotional activities and to find out what people will do at different price points,” he said.
“We put information in the applications and allow the retailer to create goals for each category of product. They can then choose the right set of prices and the right marketing and merchandising based on that goal and what the consumers want.”
IBM’s Journey Analytics
Parkin is especially excited about IBM’s new Journey Analytics, developed with Spark, which helps “retailers and brands connect in a more effective way across different channels — mobile and web — using real-time data.”
Watch the full interview below, and be sure to check out more of SiliconANGLE and theCUBE’s coverage of IBM Spark 2015.
@theCUBE
#SparkInsight
