Converting Customer Transactional Data

Business Insights into

IBM commissioned Forrester Consulting

The topic: How are IT managers and data scientists using open source platforms like Apache Spark to create predictive models?

What do they need to build and deploy these models in real time?

   

   

What challenges do they face?    

We surveyed IT managers and data scientists with mainframes

Here’s what we found…

IT Managers and Data Scientists are concerned about:

The amount of data to analyze

Security while transferring data

The time spent wrangling data

IT Managers

67% of IT managers say real-time analytics during transactions would be valuable

Data Scientists

62% of data scientists use predictive analytics for data analysis

91% have an interest in real-time data use for modeling, but 51% of their time is spent preparing and understanding data

Expectation:

Improved analytics improve customer experience and increase revenue

Apache Spark Then we asked about

Here’s what we found…

Spark is the most popular open source

for data scientists

77% of IT managers agree that Spark should be used for transactional

data analysis

Because…

Running Apache Spark helps companies build models…

Quickly Accurately Securely

All in memory – without the need to move data from its original source.

Data gravity does all the heavy lifting

With Spark deployed on the platform where your data resides, you can analyze the data in real-time

No delays or moving expenses    

So what should you do?

Based on these findings, Forrester suggests companies:

Re-examine long held views of the mainframe

Consider adopting Apache Spark as a common standard for analysis and model building

   

   

It’s no longer necessary for workflow

Analyzing in place improves time-to-value and accuracy

Stop copying data for analysis when it originates on the mainframe

   

ibm.com/z/real-time-data-analysis

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