Fortune 1000 companies refuse to let legacy nonrelational databases hold them back from relational data warehousing and true business intelligence. Here's how they're tackling the IDMS to relational challenge.
The Fortune 1000 is embracing data as the next driver for business efficiency and competitive advantage, from the ads that pop up on Facebook to credit card offers we get in the mail. For better or worse, the data management world is witnessing a grand process of moving intelligence closer to the people on the front lines. It seems like a millenium ago folks moved data from the mainframe and IDMS to a relational database management system (RDBMS) equivalent. However, the 2014 Information Week State of Database Technology Survey shows today's Fortune 1000 database landscape isn't just static. It's positively retro.
Bringing back the 90s
The 956 respondents to the 2014 State of Database Technology Survey are a conservative lot. They run mission-critical systems on tried-and-true conventional databases from Microsoft, Oracle, and IBM, with little inclination to take a chance with newer offerings like PostgreSQL (with just 3% of critical loads) or MongoDB (at just 1%). In short, the relational databases of the 1990s are still very much alive and well in the Fortune 1000.
Speaking of the 90s, what about pre-relational databases still on the Mainframe?
As the Fortune 1000 continues rolling out data warehousing and business intelligence, some old friends are showing up to ruin the party. Pre-relational databases still on the mainframe tend to become obstacles to full data warehousing, business intelligence and analytics. Operational and transactional data to support big data architecture and reporting trapped in legacy databases won't integrate with SQL Server, Oracle, Db2, or PostgreSQL.
What Intelligence or Competitive Advantage is gained by exposing data from pre-relational sources?
Pre-relational databases from VSAM to Adabas or IDMS can be a treasure trove for data scientists. Details of how fast user and supply chain interactions occur, to how often, when, where, and with who- it's all there. Some refer to this as dark data, underutilised information assets that have been collected for single purpose and then archived. But given the right circumstances, that data can be mined for other reasons. Infinity Property & Casualty Corp., for example, realised it had years of adjusters' reports that could be analysed and correlated to instances of fraud. It built an algorithm out of that project and used the data to reap $12 million in subrogation recoveries.
Can data be replicated, or shared, from IDMS to relational?
Many Fortune 1000 companies shy away from large database modernisation projects, fearful of disruption or failure. Companies cannot switch off those systems, disconnect them from legacy applications written in COBOL, or simply import the data into modern PostgreSQL platforms. Due to the many mergers and acquisitions in the finance world, banks sometimes have dozens of separate legacy systems. These aging cobbled-together legacy systems can often be found in payment and credit card systems, ATMs and branch or channel solutions. The fact that these legacy systems cause companies headaches is illustrated by the Deutsche Bank, whose big data plans are held back due to the legacy systems. Advanced Mainframe Datashare service empowers true business intelligence by integrating IDMS to relational. This service:
- Is non-invasive, does not require a migration and does not add to mainframe footprint
- Enables holistic visibility and analysis of key operational data which was previously unavailable
- Reduces MIPS consumption by shifting report functionality off mainframe
- Offers automated provision, ongoing replication and confirmation of data from pre-relational to relational
The following use cases are for companies seeking to move data from IDMS to relational- each has a desire to:
- Share data from pre-relational sources without pain and cost of migration
- Get data to the business, give IT more time to modernise apps
- Stimulate innovation and efficiency through enhanced operational reporting
- Incrementally modernise data sources to an RDBMS
- Reduce cost associated with reporting on the mainframe
- Respond to business change (M&A) reqs for consolidation and reporting
How To Learn More
If you're evaluating a shift from IDMS to a relational equivalent, you have several options. Learn more about ModPaaS or our other IDMS modernisation solutions. There are plenty of Whitepapers and Case Studies to reference as well. Contact us now to set up a Proof of Concept!