newsroom

Data is ever increasing, and organizations are looking for ways to harness and glean insight from that data. But to do so, they need fast, powerful analytics tools.

If the majority of your data is on the IBM Z mainframe, IBM Open Data Analytics for z/OS can now help you gain analytic insights at the source of data origin by leveraging Apache Spark as well as the Python and Anaconda data science components. This is an exciting opportunity that opens new doors for data analytics on the IBM Z platform.

The trouble with distributed data

In today’s typical environment, data has to be migrated from multiple endpoints to a data “lake” before it can be analyzed by data scientists. Moving the data takes time, has associated costs and can increase security risks. This “extract, transform and load” (ETL) approach reduces efficiency and can result in missed business opportunities.

I work on a team in IBM Systems Lab Services that regularly supports clients with Apache Spark proof of technology projects. For many organizations, flattening the ETL process and reducing associated costs are priorities that motivate them to explore new analytics possibilities. Our experience has shown that organizations can save substantially on total cost of acquisition by executing analytics where the data is located. The savings come from the elimination of data movement across platforms, the extraction, reformatting and reloading of data into requisite tool formats and other activities that are required before analytic processes can be executed.

How to get faster insights

IBM Z has taken the approach of integrating prevalent analytic interfaces and languages into the platform, thus reducing the ETL requirements and increasing the time to insight.

IBM Open Data Analytics for z/OS is an integrated, optimized runtime foundation of industry-leading open source technologies. It takes advantage of Apache Spark — an open source engine built to conduct deeper analysis and deliver results faster. And it now extends these capabilities with the Python and Anaconda packages for data science.

By participating in this open ecosystem with an integrative approach, IBM Open Data Analytics for z/OS offers clients numerous benefits:

• A streamlined decision-making process
• Faster transactions
• Faster, more accurate modeling
• Faster, more accurate reporting
• Faster transformation of data
• Cost reduction
• Security and privacy protections

The bottom line? Doing data analysis on IBM Z can help you achieve innovative insight in near real time. Whether it is fraud detection within the transaction cycle or real-time analysis of System Management Facility (SMF) log streams, you can use the value of your data with actionable insights relevant to your business. The number and variety of use cases supported by IBM Open Data Analytics for z/OS continues to grow and expand at a rapid pace.

A real-world view of faster insights

What does this look like for real businesses? What sorts of processes can it improve?

• For clients in the banking industry, this technology has enabled enhanced fraud detection and faster compliance/risk assessment for ACH payments.
• In the insurance industry, it can be used for proactive claims analysis, to reduce appeals backlogs and to prevent grievances.
• A state government agency is using it to track vehicle licensing, collect fines for rules infractions and manage overall compliance with the state department of transportation rules and processes.

Need help optimizing your IBM Z environment?

Lab Services IBM Z and LinuxONE is an experienced group of consultants with proven expertise on real-time analytics solutions for IBM Z. If you’re looking to get more value from your data on the IBM Z platform, we can help you plan, install and configure IBM Open Data Analytics for z/OS.

Contact Lab Services today to see if we can help with your next IBM Z project.

In addition, please join Lab Services at the upcoming IBM Z Technical University in Washington, DC, to learn more about turning your IT challenges into successes with IBM Systems solutions.

The post Analyzing data at the source for faster insights appeared first on IBM Systems Blog: In the Making.