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Moving to the Cloud? Don’t Forget the Mainframe!

Most enterprises have at least some presence in the cloud, and many even have cloud-first or cloud-native mandates. Reduced costs, increased efficiency and faster time to value are just some of the advantages of moving to cloud. However, cloud adoption is not a silver bullet for becoming a data-driven organization. One of the biggest challenges for data and analytics leaders today is how to effectively connect disparate data sources. In a recent Syncsort survey, 68% of respondents reported that siloed data negatively impacts their organization, and fewer than 1 in 10 say they are very effective in getting value from their enterprise data.

Even with the growing adoption of next-gen technologies, mainframes remain a critical data source (it’s estimated that 2.5 billion transactions are run per day, per mainframe across the world). If your cloud data warehouse or data lake is only as good as the data filling it and you’re leaving mainframe data out of the picture, you could be leaving valuable information out of your processes.

To feed artificial intelligence, business insights or analytics, you need the full data story and much of that does live on the mainframe. Ignoring legacy data can cause three major issues within the business:

  • Risk of diminishing the value of your big data investments by not fully leveraging that data
  • Danger of providing incomplete or inaccurate analytics (For example, not including transactional customer data which often lives on the mainframe)
  • Losing the opportunity for deeper business insights by not incorporating large, rich enterprise datasets for analysis

However, pulling data from the mainframe can be a challenge. There is often no native connectivity between legacy systems and new platforms, data governance, and capturing changes to mainframe data in real-time are all common challenges we see with businesses tackling this challenge.

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Helping organizations bring legacy data to big data to realize value is a core mission we strongly believe in. Last month, we announced seamless data integration to deliver mainframe data to Snowflake for advanced analytics in the cloud. On the heels of that announcement, we are expanding our capabilities to support Microsoft Azure cloud initiatives, specifically for leveraging Microsoft Azure SQL Data Warehouse (ADW), for enabling business insights.

For some time, customers have used Syncsort Connect products to populate Azure data lake and archive legacy data in Azure cloud storage. With our latest announcement, businesses can integrate mainframe data (e.g. VSAM, Db2/z) into ADW for next generation data management platforms for advanced analytics, on-premises or in the cloud. Leveraging the power of a cloud data platform like Microsoft Azure and integration capabilities of Syncsort Connect, help to ensure your business is strongly positioned for success when it comes to tapping the valuable resource that is legacy data.

For additional insights on how to extract maximum value from your enterprise data in the cloud, including mainframe data, check out Syncsort’s new white paper: Mainframe to Cloud: Design Once, Deploy Anywhere in a Hybrid World

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