IBM today announced the launch of IBM Data Gate for watsonx—revolutionary technology that simplifies the way organizations synchronize data originating on IBM Z for analyzing and building AI models. Now, you can unlock your transactional mainframe data for AI and analytics with IBM Data Gate for watsonx integration with IBM watsonx.data 

Break down data siloes and unify access across your data landscape 

It’s no question we are in a new era for AI. AI adoption has more than doubled since 2017.  

But AI begins with data, and enterprises are still facing fundamental challenges to unify data across disparate data siloes and unlock the full value of their data for AI. 82% of enterprises face data silos, inhibiting operations, and limiting the availability of their data for AI.  

So how do you unify data access across disparate data sources, optimize your data workloads, and manage and deliver that data for AI? IBM watsonx.data is a fit-for-purpose data store built on an open data lakehouse, to scale AI workloads, for all your data, anywhere. Watsonx.data is part of IBM’s AI and data platform, watsonx, that empowers enterprises to scale and accelerate the impact of AI across the business. 

Watsonx.data enables users to access all data through a single point of entry, with a shared metadata layer deployed across clouds and on-premises environments. It supports open data and open table formats, enabling enterprises to store vast amounts of data in vendor-agnostic formats, such as Parquet, Avro, and Apache ORC. Leveraging Apache Iceberg allows organizations to share large volumes of data via the open table format specifically built for high-performance analytics. With multiple fit-for-purpose query engines, organizations can optimize costly warehouse workloads, and will no longer need to keep multiple copies of data for various workloads or across repositories for analytics and AI use cases. However, to effectively realize this scalability, there is still a fundamental need for data to be managed and provided differently across environments. 

The importance of transactional mainframe data to fuel AI and analytics 

Some of the most important data for supporting analytics and building predictive AI models is derived from the critical transaction data that often originates on IBM Z in Db2 for z/OS, IMS for z/OS and native VSAM data sets (Virtual Sequential Access Method). This data is a definitive source of truth, represents the current state of the business and provides unique predictive value for AI applications. However, access to this vital source of data can have some challenges. Some organizations prefer to minimize access to Db2 for z/OS for any workloads outside of operational processes. And many developers are unfamiliar with IMS and VSAM data sets, which require specialized skills for accessing data to support analytics and data science requests.  

With this valuable transactional data, organizations can better identify fraud, understand constituent behavior, customer buying journeys and customer attrition and build predictive AI models to understand, anticipate and influence business outcomes. By bringing transactional data originating on the mainframe into an open, governed data lakehouse like watsonx.data, an organization can readily build AI models to drive greater revenue, improve productivity, and better manage cost. 

Data Gate for watsonx strengthens the IBM Z ecosystem by providing read-only data for analytics and AI purposes whilst ensuring that IBM Z continues to be the system of record for transactional applications.

Traditional methods of accessing mainframe data for AI are ineffective and cost prohibitive 

Traditional approaches to providing access to this data include writing complex mainframe programs, ETL processes, and using replication technologies. These approaches could have several potential downsides including data latency, only providing point-in-time data, significant mainframe resource consumption, cost, and they can demand time and effort from IT resources that are often focused on supporting important, core operational systems. 

Organizations have been following a decades old architectural pattern of moving data through these traditional approaches and losing opportunity to take advantage of the latest data to build models and use the most current data within the context of AI applications and analysis. There is a more modern, effective, lower cost and better way of providing data for an analytics and AI infrastructure. Leaders must re-think these ineffective approaches and streamline access to all their data including mainframe data sources.  

Unlock your transactional mainframe data for AI and analytics with IBM Data Gate for watsonx integration with IBM watsonx.data 

IBM Data Gate for watsonx is a purpose-built synchronization technology designed specifically to synchronize data from select IBM Z sources (Db2 for z/OS, IMS and VSAM) to the Iceberg open table format for access by watsonx. It is an integrated component of Db2 for z/OS for simplified installation, configuration, and management. It leverages available zIIP (z Integrated Information Processor) capacity to reduce resource contention and synchronization cost. And has been developed for high efficiency and throughput. 

With IBM Data Gate for watsonx, organizations can now efficiently incorporate the most up-to date, critical enterprise transactional data and reduce time for accessing and analyzing mainframe data. And now with the data in watsonx, they can apply as much compute to a complex analysis as they want and need and use the most up-to-date transactional data in machine learning/AI models without impacting operational processing. 

How to get started

Learn more on watsonx.data. To learn more about IBM Data Gate for watsonx, and how to integrate your mainframe into your watsonx platform, visit our new product page.