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IBM Machine Learning for z/OS Gives Clients the Tools to Make Better, Faster Decisions

IBM Machine Learning for z/OS
Illustration by Paul Farrington / StudioTonne

A customer seeks a home-improvement loan to accommodate her growing home-based business. The bank’s loan officer looks at her credit score. It’s not bad, but it also suggests there’s a high probability she won’t be able to pay back the loan. The loan officer says as much to his manager.

Not long thereafter, his boss returns the application with the word “approved.”

This leaves the loan officer scratching his head. In fairness, his manager had access to more information than just the customer’s credit score. With the help of the bank’s data analyst, he was able to examine data concerning the customer’s saving and investment patterns, income, accounts and even information about her business’s social media postings. And based on this data, as well as comparison to customers that fall in similar parameters, it turns out the customer is actually a good risk—and thus a good opportunity for the bank.

“Using machine learning, data scientists can uncover new insights from their data to support better outcomes. These innovative insights will generate new business opportunities.”
—Terrie Jacopi, IBM z Systems analytics offering manager

This is a simple example of an organization using internal and external data to derive better insight. Data and analytics have become more integrated with the way businesses operate. But analyzing data to make it useful remains a huge challenge. That’s chiefly because of the amount and sources of the data.

In February, IBM introduced Machine Learning for z/OS*. This new offering transforms the IBM z Systems* mainframe into a machine learning platform. IBM Machine Learning for z/OS allows clients and their data scientists to quickly create, deploy and continuously monitor a high volume of analytic models.

IBM Machine Learning’s capabilities are helping data scientists identify patterns in historical data, build behavioral models from those patterns, and make recommendations based on those behavioral models to help predict the most accurate outcomes. These models can help banks, governments, insurers, manufacturers, retailers and service providers make better decisions. And like the earlier example, they can use machine learning to better understand their clients and offer services or products that are highly targeted to individual needs. What’s more, IBM Machine Learning continuously monitors these models to ensure they remain accurate and useful.

Thinking Through the Data

First, a quick definition: Machine learning is a type of artificial intelligence that enables a computer to learn without being explicitly programmed, thus requiring less human intervention. It uses what it learns to anticipate or predict a behavior or outcome.

“When data scientists are solving a business problem, the data within their organization often holds the insights they need to find a solution,” says Terrie Jacopi, IBM z Systems analytics offering manager. “The broader the set of data they can evaluate, the better the outcomes. They have to understand all of the data sources that are relevant to solving the problem and then determine the best approach.”

Again, the big challenge is the amount of data out there. Insights need to be gleaned not only from an organization’s own information, but also from outside sources, such as social media and mobile devices. That external data can originate from current or potential customers. For data scientists and their organizations, channeling that flood of data can be a massive headache. It’s also an opportunity. “If you can take data that’s core to your business, like transactional data that originates on IBM z Systems, and combine it with external data, the insights you get about your clients are phenomenal,” Jacopi says.

To develop and exploit those insights, data scientists build behavioral models based on patterns within data. “Traditionally, data scientists do a lot of manual, iterative model development.” Jacopi notes. “Numerous algorithms might be relevant, and they have to figure out which approach would be the best. Data scientists spend a great deal of their time identifying the best model by trial and error, which takes time away from addressing the business challenge.” And time works against successful model development. Models can degrade as business, markets and data change—which, as most organizations know, can happen rapidly.

Gene Rebeck is a freelance writer based in Duluth, Minnesota.

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