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IBM z Analytics Makes Real-Time Insight a Reality




Organizations with an IBM Z* mainframe understand the power and capabilities of the platform. Mainframe users benefit daily from the performance, resiliency, security and other advantages the platform offers. Aside from being a powerful transactional system, the IBM Z family also offers exceptional advantages to analytics applications—advantages that can differentiate your organization and offer new opportunities to innovate.
 
These opportunities depend on the ability to turn your organization’s data into insight. Some insights are built on slowly evolving sets of data for which time isn’t a huge factor. In other cases, insight from real-time data makes a significant difference. This is where IBM z Analytics technologies excel, helping provide answers to questions in real time for better fraud detection and prevention, improved risk reduction, enhanced customer experience, and more.
 
All organizations possess a wealth of valuable data on their enterprise systems. Ideally, decisions should be derived from this data as it’s generated to support new initiatives such as real-time decision making and machine learning. A significant amount of the most current, valuable data originates on IBM Z transactional systems. Leveraging this data for analytics and machine learning can provide the insights you need to outperform your competitors.
 
Most organizations, however, support analytics and transactional systems in separate environments, moving data between platforms. This immediately introduces latency, cost, complexity and increased security breach risk. It’s important to consider the impact of moving data from its point of origin for analytics and machine learning.
 

Data Gravity and HTAP

 
Two concepts are transforming the way organizations approach their analytics strategy: data gravity, and hybrid transaction and analytical processing (HTAP). Find more information at gtnr.it/2lcGdi4 and gtnr.it/1onVMLR.
 
  • Data gravity. This is a compelling trend where organizations bring compute to the data. Analyzing data where it originates can minimize cost and simplify infrastructure while improving security, data governance, and decision making. It helps ensure that the information used to make critical business decisions is consistent, reliable and accurate, and that corporate data assets are protected, governed, and secured.
  • HTAP. HTAP supports the data gravity concept by minimizing data movement between platforms. This reduces latency, enabling organizations to process real-time analytics on live transactional data. Top industry analysts recognize the benefits that this simplified analytics architecture delivers, in support of transformation and innovation.
 

Delivering High-Impact Analytics

 
IBM z Analytics provides a broad set of capabilities that bring analytics and transactional processing together in a single environment, making real-time insight possible. These capabilities complement your existing approach to analytics, delivering unique value to your overall strategy.
 
IBM has invested in both IBM Z and z Analytics technologies to provide extensive analytics capabilities and continues to make significant investments to help organizations deliver high-impact analytics on an enterprise scale. IBM also released new versions of IBM Z hardware supporting multicore and single instruction multiple data (SIMD) parallelism along with hardware accelerated pervasive encryption. This means that the analytics software runs more efficiently, cost-effectively and securely.
 
From a software perspective, IBM has enhanced analytics capabilities with new versions
of Db2* for z/OS*, IBM Db2 Analytics Accelerator for z/OS and QMF*. New additions to the analytics software portfolio include Machine Learning for z/OS, Open Data Analytics for z/OS (which includes Spark, Anaconda, Python and the Optimized Data Layer) and Data Virtualization Manager (DVM). Organizations can take advantage of this technology in a security-rich environment with the same resiliency as their operational applications to:
 
  • Perform complex analytics on the most current data in real time without impacting the performance of transactional systems
  • Incorporate relational and nonrelational data along with structured and unstructured data from any platform
  • Build, deploy, score and manage predictive analytics for the enterprise
  • Integrate traditional and self-service business intelligence and collaboration
 
Within this rich portfolio, both IBM Db2 Analytics Accelerator and Machine Learning for z/OS can contribute significantly to an organization’s ecosystem of insight.
 

Making Decisions IBM Db2 Analytics Accelerator

 
IBM Db2 Analytics Accelerator complements Db2 for z/OS, which is built for transactional workloads. It provides a cost-effective, high-speed query engine to run business reporting and analytics workloads efficiently. As part of its unique design, IBM Db2 Analytics Accelerator includes breakthrough technologies to route queries typically found in transactional workloads to Db2 for z/OS and queries typically found in analytics applications to an integrated analytical processing environment. Each query executes in its optimal environment for maximum speed and cost efficiency.
 
The IBM Db2 for z/OS HTAP approach stands alone as the only architecture that successfully implements real-time analytics with heterogeneous scale-out. It assures industry-leading performance for mixed workloads. Analytical processing doesn’t degrade transactional workload performance because transactional and analytical queries are each processed within separate compute resource pools.
 
IBM Db2 Analytics Accelerator in conjunction with IBM Z delivers a hybrid computing platform where organizations can directly leverage the valuable and sensitive data originating on IBM Z. This winning combination transforms the mainframe into a highly-efficient HTAP environment, enabling real-time analytics on transactional data as it’s generated while driving out cost and complexity.
 

IBM Machine Learning for z/OS

 
Machine learning delivers improved customer service through personalization and drives greater employee and IT productivity by helping to make the best decisions at the right time. It serves as a foundation for innovation by uncovering new customer behaviors and product uses.
Data science efforts represent a significant investment in skills, time, hardware and software. Data scientists and the business teams that depend on them want to get the most out of the models that they develop.
 
To help data scientists develop, deploy and monitor behavioral models, IBM introduced Machine Learning for z/OS—an enterprise grade, collaborative and extensible machine learning offering. It can drive decisions where data originates (i.e. data gravity), benefiting from IBM Z platform advantages including security, resiliency and the lowest possible latency.
 
Through a hybrid cloud approach to model life cycle management and collaboration, Machine Learning for z/OS gives data science teams flexibility to develop their models on
IBM Z or their platform of choice. Organizations developing models on other platforms with Spark or Python can readily deploy those models on IBM Z, where the majority of transactions occur and where most enterprise data originates.
 
Machine learning is about building models and using these models to make predictions. Scoring leverages the model to make a prediction based on new data. When incorporating scoring within a production operational application, execution time is critical. With significant transaction volumes, just a few additional milliseconds can impact revenue by millions to tens of millions of dollars. Colocating the scoring process with your transactional processes on IBM Z minimizes the execution time impact.
 
With Machine Learning for z/OS, scoring can be easily integrated with transactional applications via RESTful APIs without significant overhead, enabling real-time insight at the point of interaction. From a data gravity perspective, driving scoring to where data originates simplifies architecture, reduces cost and complexity, and minimizes latency. This helps organizations make optimal decisions at the most impactful moment.
 

When Real-Time Insight Matters

 
When real-time insight matters, IBM Z and z Analytics technologies deliver the powerful transactional and analytics environment that you need. With IBM z Analytics, you can take advantage of your enterprise data where it originates to drive high impact decisions while minimizing latency, reducing cost and improving data security. Turn your data into insight, and insight into opportunity to provide real-time answers to your most critical questions.
 

Annette Zawacki is an IBM worldwide portfolio marketing manager for Machine Learning for z/OS and z Analytics strategy and messaging.

Jonathan Sloan is an IBM senior analytics architect who consults with customers worldwide on IBM z Analytics technologies.

Patricia Zakhar is IBM’s worldwide product marketing manager for data warehousing on System z.


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