Legacy Data: Integrate Legacy Data into a Modern Data Environment

There are some very good reasons why many of the world’s most transaction-intensive companies are still running critical business applications on mainframes and using legacy data. Those legacy systems are highly reliable, secure, and scalable. In industries such as banking, insurance, and healthcare, these qualities are extremely important.

However, there are also drawbacks: mainframes lack the flexibility offered by distributed computing, and accessing data from those systems can be expensive and cumbersome. Very often, legacy data requires on-the-fly transformation to be compatible with modern open-standard systems. Moreover, working with mainframe data requires specialized skills, and the talent pool of experienced mainframe IT workers has been shrinking in recent years.

As a result of these challenges, many companies are still treating mainframe data as if it needs to reside in a silo, performing batch updates to synchronize mainframe data with external platforms, or simply living with the limitations of siloed information. Those alternatives have become less and less palatable over time. Fortunately, there are better alternatives.

Let’s look at some use cases and best practices for integrating data from legacy systems.

For the world’s leading enterprises, big data is the key that unlocks competitive insights. Companies that run core transactional functions on a mainframe are faced with a conundrum; how can they provide a comprehensive view of the enterprise if they don’t have timely access to their core transactional data?

With an array of different systems including mobile applications, e-commerce, CRM, shared services, trading platforms, and more, it’s imperative that companies are able to aggregate data from multiple sources and report on that in real time.

In today’s business climate, responsiveness is critically important. Day-old information just isn’t good enough. This is especially true during periods of disruptive change when economic behavior is shifting rapidly and business leaders need to respond quickly. Companies that can see trends right away and react swiftly will outperform those who can’t.

For the IT department, timely discovery and rapid response to security events are critical. For IT Operations Analytics (ITOA) and Security Event and Information Management (SEIM), real-time visibility is non-negotiable. A majority of respondents to Precisely’s State of the Mainframe for 2018 survey placed high importance on real‑time analysis of security alerts and audit readiness. Many respondents were seeking to stream SMF and log data to platforms such as Splunk, Hadoop, or Spark for an enterprise-wide view of IT operations and security. With the passage of the European Union’s GDPR (General Data Protection Regulation) and with the likely passage of similar legislation in other countries, compliance with security and privacy standards is more important than ever.

Interoperability is imperative. Customers have come to expect self-service applications as a matter of course. These applications are built on modern platforms that use open standards and operate across distributed platforms. Mainframe data needs to interact with those systems in real time. Fraud detection, similarly, requires real-time access to data. To deliver these kinds of solutions, businesses need an efficient and secure process for streaming data from the mainframe to distributed platforms. All this needs to be done securely, and without creating data processing bottlenecks. Again, real-time access is an absolute requirement.

The good news is that there are some powerful tools for mastering these challenges.