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Things to consider when modernizing mainframes

Before presenting the main considerations in any mainframe modernization strategy, let’s confirm why organizations should consider modernizing their mainframe.

The straightforward answer to this question is that many companies are now implementing mainframe modernization strategies because it allows them to take advantage of the latest cloud innovations without disrupting critical mainframe processing and applications.

For decades, businesses have relied on mainframes to deliver mission-critical applications. Government agencies, healthcare organizations, and businesses of all kinds, especially in finance and insurance, use mainframes to manage their most sensitive and valuable data.

Here’s a statistic that confirms the view that modernization is essential – 69% of IT decision makers report that the rigidity of the mainframe limits the innovation capacity of the IT department. This should be enough to encourage companies to get their technical teams to develop a strategy. But there are a few things to get on the radar first.

Far from being stagnant technology, research shows that in 2017, the global mainframe market was valued at $ 2.094 million and is expected to reach $ 2.907 million by 2025, resulting in a compound annual growth rate of 4.3%.

So, mainframes aren’t going anywhere anytime soon due to their high performance, reliability and security. As you should now understand, the market for them is growing. But unlocking information from these corporate workhorses comes at a cost.

Forward-thinking companies want to take advantage of today’s most advanced analytics platforms, as well as affordable and scalable cloud services. Modernizing existing systems is an essential step in achieving this goal. For example, providing a 360-degree view of customers to the frontline support team requires real-time data replication from the mainframe and other source systems.

Things to consider

I think I have more than justified why businesses need to modernize the mainframe, so now let’s take a look at the issues to consider when embarking on this journey. The best practice is that notebooks and associated support files should contain an accurate description of the underlying data.

Data quality should be assessed in advance when examining IMS and VSAM data sources as part of a data replication project.

Mainframes aren’t going anywhere anytime soon due to their high performance, reliability, and security.

The ability to have all data, including its combinations, accessible to all users within a governance framework that provides security without limiting agility is called data democratization.

It’s important to remember that data is of limited value if only a limited number of highly technical people can access, understand, and use it.

The next issue to consider in the modernization strategy is to design a secure enterprise-wide repository and catalog of all the data resources the enterprise has for analysis. This gives data consumers a single, go-to destination to find, understand, and get information from all enterprise data sources.

It includes data preparation and metadata tools that streamline the transformation of raw data into assets ready for analysis.

Data Integration (DI) can help unleash the value of data in legacy sources, including DB2 z / OS, IMS, and VSAM.

DI can ingest incremental datasets continuously, from numerous transactional sources, in data lake and data warehouse environments, providing up-to-date data with change data capture based on class logs business.

Enterprises can increase their agility and flexibility by aligning IT with business operations and enable “re-platform” by migrating existing data to innovative cloud alternatives. Minimize the impact on production systems and reduce costly mainframe resources by eliminating direct requests and capturing changes once, while delivering to multiple targets.

Plus, an automated, no-code approach to data pipeline creation will save you time. Gain greater visibility into the data landscape with secure, governed, enterprise-wide catalogs and securely democratize data across industries.

Back office IT workload and support for business issues can be reduced while maintaining much-needed flexibility and data security.

The main challenges of the mainframe ID

When businesses attempt to integrate their mainframe data into a larger data environment, they often run into a few common hurdles, namely:

Batch file transfer: Scheduled scripts or mainframe jobs pull data from the “big iron” and write the results to large files that must be transferred over the network, which can cause data to expire. The latter is due to the fact that the data is not delivered in real time.

Direct query of the database: Companies looking to integrate mainframes into a larger analytical environment tend to take a brute-force approach, but each new request escalates instructions and adds millions of additional instructions per second (MIPS) to the monthly bill.

Real-time data flow: To do this, data must be moved immediately whenever changes occur. Without the correct DI architecture, it takes a significant amount of manual tuning to support the large, deep, and rapid analysis required by today’s businesses.

To recap, large organizations have relied on mainframes for half a century to manage their most valuable and sensitive data.

From order processing to financial transactions, production and inventory control to payroll, mainframes continue to support mission-critical applications.

Therefore, mainframe data must be integrated into modern, data-driven analytical business processes and the environments that support them.

Additionally, organizations cannot take the brute-force approach because they must unlock the value of mainframe data without increasing the MIPS consumption, which mainframe billing systems rely on.

So how can businesses continuously leverage mainframe data at an affordable price for business analysis?

Here’s a solution: Offload mainframe data to modern data lake platforms like Apache Hadoop, Azure Data Lake Services (ADLS GEN2), or Databricks unified data analytics platforms, which make it easy to build new possibilities for analysis and information.

Integrating into these new environments requires a new approach that keeps data up-to-date and available, without adding complexity or prohibitive costs.

Things to consider when modernizing mainframes

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