Why locate analytics exactly where your data resides?
Your competition is the last, best experience your client had. To differentiate yourself you must know how each individual has been interacting with you in real-time - right down to the split second. Generating instantaneous business insights requires co-location of analysis and operations, transforming your systems of record into systems of insight.
IBM z Systems, optimized for real-time analytics, is a platform designed for availability, tuned for flexibility and engineered for the highest levels of security - when both operations and analytics are mission-critical.
Cognitive computing with z Systems unlocks insights in real time
What is the customer value of real time?
Explore real time analytic solutions
How can you gain deep customer insights by analyzing all of your enterprise data along with data from other sources – without having to move the data first?
This enterprise-grade, native z/OS distribution of the in-memory analytics engine, coupled with optimized data access and abstraction services, provides universal, optimized data access and analysis of a broad set of structured and unstructured data sources through Spark APIs.
Designed to simplify the analysis of big data, the product provides developers and data scientists with the ability to combine the benefits of Apache Spark with the advantage of analyzing business-critical z/OS data in place — with no data movement.
Can you gain insights from all your data?
How can real-time analysis of transactional data give you top-line competitive advantage and reduce bottom-line expense?
Next-generation in-database transformation and modeling optimization provides significant performance improvements in support of real-time, in-transaction predictive analytics by eliminating the requirement to move data off of z Systems for analysis.
Enables truly real-time, in-transaction predictive analysis for a wide variety of use cases by employing the Predictive Model Markup Language (PMML) standard to import and deploy predictive models from most popular tools (SPSS, SAS, R, KNIME, Python, and so on) directly into z/OS transactional applications.
More offerings for predicting behavior
How can you dramatically improve time-to-value and accuracy of business-critical reports and ad-hoc queries, at a low-cost point?
This high-performance appliance provides extremely fast results for complex and data-intensive queries for data warehousing, business intelligence and analytic workloads. V5.1 also delivers a number of functional enhancements.
DB2 Analytics Accelerator: Trends and Directions
Can you always anticipate customer needs?
Enterprise-ready analytics and IBM DB2 Analytics Accelerator
What if you could use analytics to improve the efficiency of IT operations (e.g. capacity management, outage repair and avoidance) and deliver superior service?
Simplify the management of applications across the data center and optimize resource sharing. This latest release offers a variety of new reports, additional analytics, and simplified installation.
Analyze log data end-to-end from your enterprise workload and gain insights to diagnose and resolve problems more quickly. V2.2 extends search and analysis capabilities and includes alert notifications, support for Hadoop and role-based access control.
Are your IT systems always available?
Can you predictively alter
Hear from customers and analysts
Apache Spark aficionados are in for a real shock.
Petrol drives top line growth with predictive point of sale
The ETL problem: The costs of transferring data
Barclays accelerates channel analysis by 60%
IBM Mainframe for Systems of Engagement and Insight
The Case for Running Real-Time Transactional Analytics on IBM z
Fast Company article