The Top Seven Reasons to Marry Machine Learning and Your Mainframe

Machine learning (ML) has come to the mainframe, and with it the ability to analyze (and monetize) mainframe data in place.

“Mainframes generate a massive volume of useful but overwhelming data that enterprise IT organizations often fail to fully leverage,” says David Hodgson, Chief Product Officer, at Precisely. A major part of the problem is the fact that the most modern and useful analytics tools were developed entirely outside of the mainframe sphere. Extracting and exporting mainframe data into external data warehouses for analysis using those standard toolsets has been, until recently, a difficult and expensive operation. But now that’s changing.

What is machine learning? According to one widely used definition, “Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.”

Machine learning and the mainframe

Adding sophisticated machine learning capabilities to your mainframe environment offers the potential for leveraging your company’s wealth of historical and current transactional data at an entirely new level. Here are some of the benefits of beginning that process now.

1. Sophisticated analytics without having to export data

Conducting analytics on mainframe data has traditionally involved using ETL (extract, transform, load) techniques to transfer the data to external data warehouses for analysis. But ETL consumes a large number of mainframe MIPS, and can be time-consuming, costly, and insecure. Use of mainframe-based ML analytics avoids these issues.

2. Real time analytics

Using mainframe ML to perform analytics at the time data is created or processed allows maximum value to be extracted from that information. For example, real-time ML analysis of credit card activity could enable the immediate detection and blocking of fraudulent or erroneous transactions.

3. Enhanced data security

By performing analytics in place on the mainframe, ML helps companies avoid the security vulnerabilities associated with transporting data out of the mainframe environment for analysis. Plus, ML algorithms identify potentially malicious activity patterns that have never been seen before, or that are too subtle for humans to detect. As a result, ML can provide superior data security by acting to mitigate potential intrusions, or even simple human errors, as they occur, rather than trying to remediate them after the fact.

4. Less need for staff with mainframe skills

Senior mainframe engineers are now retiring at accelerated rates, while relatively few younger professionals are receiving mainframe training. ML alleviates this growing problem by significantly simplifying and automating operations in the mainframe environment.

5. Availability of sophisticated ML and analytics tools

Getting top-flight analytics results from mainframe data no longer requires inventing workarounds for tools that are not available in the mainframe environment. Now languages such as Java, Python, and Scala, and ML frameworks like Apache SparkML, TensorFlow, and H2O can be used. This gives mainframe data scientists access to familiar tools that are already well established in the ML field.

6. Optimized operational performance

Mainframe administrators optimize the performance of their systems through the analysis of large amounts of log data. This can require a great deal of staff time and expertise. A well-tuned ML system can perform much of this type of analysis on its own with little human intervention. By identifying abnormal operational patterns, ML-based real-time analytics can detect early problems, often before they impact operations. And when human intervention is needed, it can issue dynamic alerts as well as detailed reports and recommended actions. Plus, an ML system can learn from previous experiences, improving its performance over time.

7. Best-in-class customer experiences

Mainframes host billions of transactions every day for firms servicing the banking, insurance, retail, transportation, healthcare, government, and other sectors. If your company is one of these, you can maximize customer satisfaction through real-time analytics that use the sophisticated predictive and classification features of ML to provide immediate, fully contextualized, relevant, and individualized responses during customer interactions.

Machine learning helps you maximize your existing data assets

In many ways, the data that resides on its mainframes may be the most valuable asset your company has. The advent of machine learning for the mainframe opens up exciting new avenues for harvesting that value that some forward-looking companies will take advantage of in 2020 and beyond. Will yours be one of them?

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