Enterprise data continues to be a primary and critical data source for most analytics initiatives including those focused on machine learning. IBM z Systems helps organizations gain analytics agility by keeping enterprise data in place while making it highly accessible.
IBM z Systems analytics solutions allow you to integrate transactional and historical data from your server platform with other non-relational data sources for powerful, real-time analytics and cognitive insights to help you outthink your competition.
What if you could gain business agility by keeping enterprise data in place?
Minimize the cost and complexity of analytics by accessing your enterprise data “in-place” with minimal data duplication or data movement. Your enterprise data can be highly accessible to analytics applications and tools by integrating transactional and analytics processing. And you can protect sensitive data used in analytics by keeping data within the secure IBM z Systems platform.
What is the impact of big data, mobile, cloud and customer expectations on analytics?
What if you could improve decisions by gaining deeper insight from secure, data-driven analytics?
What if you could deliver insight to the business faster by accelerating queries up to 2000x? What if you could provide a holistic view of all data by integrating enterprise, structured, and unstructured data with minimal to no impact on transactional systems? And offer data analysts fast, secure access to “live” enterprise data through support of open source technologies like Spark?
See how to Spark on z Systems can federate analytics across critical data and external data.
How agile could your business become if “live” enterprise data were quickly accessible?
Your enterprise can enable real-time analytics by making “live” enterprise data more accessible to lines of business. You can increase the accuracy of predictive analytics by scoring the data as the transaction is occurring. Further, you can improve the quality of your data models by updating models more frequently against larger, more current data volumes.